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[FCO] AppliedAICourse - Applied Machine Learning Course
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58.1 - AD-Click Predicition (Size: 25.37 GB) (Files: 4811)
58.1 - AD-Click Predicition
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10.10 - Hyper Cube,Hyper Cuboid
10.10 - Hyper Cube,Hyper Cuboid.mp4
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10.11 - Revision Questions
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10.1 - Why learn it
10.1 - Why learn it .mp4
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10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector
10.2 - Introduction to Vectors(2-D, 3-D, n-D) , Row Vector and Column Vector.mp4
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10.3 - Dot Product and Angle between 2 Vectors
10.3 - Dot Product and Angle between 2 Vectors.mp4
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10.4 - Projection and Unit Vector
10.4 - Projection and Unit Vector.mp4
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10.5 - Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D), Plane Passing through origin, Normal to a Plane
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10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces
10.6 - Distance of a point from a PlaneHyperplane, Half-Spaces.mp4
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10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)
10.7 - Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D).mp4
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10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)
10.8 - Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D).mp4
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10.9 - Square ,Rectangle
10.9 - Square ,Rectangle.mp4
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11.10 - How distributions are used
11.10 - How distributions are used.mp4
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11.11 - Chebyshev’s inequality
11.11 - Chebyshev’s inequality.mp4
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11.12 - Discrete and Continuous Uniform distributions
11.12 - Discrete and Continuous Uniform distributions.mp4
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11.13 - How to randomly sample data points (Uniform Distribution)
11.13 - How to randomly sample data points (Uniform Distribution).mp4
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11.14 - Bernoulli and Binomial Distribution
11.14 - Bernoulli and Binomial Distribution.mp4
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11.15 - Log Normal Distribution
11.15 - Log Normal Distribution.mp4
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11.16 - Power law distribution
11.16 - Power law distribution.mp4
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11.17 - Box cox transform
11.17 - Box cox transform.mp4
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11.18 - Applications of non-gaussian distributions
11.18 - Applications of non-gaussian distributions.mp4
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11.19 - Co-variance
11.19 - Co-variance.mp4
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11.1 - Introduction to Probability and Statistics
11.1 - Introduction to Probability and Statistics.mp4
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11.20 - Pearson Correlation Coefficient
11.20 - Pearson Correlation Coefficient.mp4
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11.21 - Spearman Rank Correlation Coefficient
11.21 - Spearman Rank Correlation Coefficient.mp4
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11.22 - Correlation vs Causation
11.22 - Correlation vs Causation.mp4
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11.23 - How to use correlations
11.23 - How to use correlations.mp4
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11.24 - Confidence interval (C.I) Introduction
11.24 - Confidence interval (C.I) Introduction.mp4
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11.25 - Computing confidence interval given the underlying distribution
11.25 - Computing confidence interval given the underlying distribution.mp4
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11.26 - C.I for mean of a normal random variable
11.26 - C.I for mean of a normal random variable.mp4
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11.27 - Confidence interval using bootstrapping
11.27 - Confidence interval using bootstrapping.mp4
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11.28 - Hypothesis testing methodology, Null-hypothesis, p-value
11.28 - Hypothesis testing methodology, Null-hypothesis, p-value.mp4
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11.29 - Hypothesis Testing Intution with coin toss example
11.29 - Hypothesis Testing Intution with coin toss example.mp4
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11.2 - Population and Sample
11.2 - Population and Sample.mp4
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11.30 - Resampling and permutation test
11.30 - Resampling and permutation test.mp4
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11.31 - K-S Test for similarity of two distributions
11.31 - K-S Test for similarity of two distributions.mp4
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11.32 - Code Snippet K-S Test
11.32 - Code Snippet K-S Test.mp4
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11.33 - Hypothesis testing another example
11.33 - Hypothesis testing another example.mp4
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11.34 - Resampling and Permutation test another example
11.34 - Resampling and Permutation test another example.mp4
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11.35 - How to use hypothesis testing
11.35 - How to use hypothesis testing.mp4
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11.36 - Proportional Sampling
11.36 - Proportional Sampling.mp4
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11.37 - Revision Questions
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11.3 - GaussianNormal Distribution and its PDF(Probability Density Function)
11.3 - GaussianNormal Distribution and its PDF(Probability Density Function).mp4.mkv
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11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution
11.4 - CDF(Cumulative Distribution function) of GaussianNormal distribution.mp4
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11.5 - Symmetric distribution, Skewness and Kurtosis
11.5 - Symmetric distribution, Skewness and Kurtosis.mp4
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11.6 - Standard normal variate (Z) and standardization
11.6 - Standard normal variate (Z) and standardization.mp4
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11.7 - Kernel density estimation
11.7 - Kernel density estimation.mp4
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11.8 - Sampling distribution & Central Limit theorem
11.8 - Sampling distribution & Central Limit theorem.mp4
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11.9 - Q-Q plotHow to test if a random variable is normally distributed or not
11.9 - Q-Q plotHow to test if a random variable is normally distributed or not.mp4
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1.1 - How to Learn from Appliedaicourse
1.1 - How to Learn from Appliedaicourse.mp4
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12.1 - Questions & Answers
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1.2 - How the Job Guarantee program works
1.2 - How the Job Guarantee program works.mp4
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13.10 - Code to Load MNIST Data Set
13.10 - Code to Load MNIST Data Set.mp4
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13.1 - What is Dimensionality reduction
13.1 - What is Dimensionality reduction.mp4
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13.2 - Row Vector and Column Vector
13.2 - Row Vector and Column Vector.mp4
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13.3 - How to represent a data set
13.3 - How to represent a data set.mp4
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13.4 - How to represent a dataset as a Matrix
13.4 - How to represent a dataset as a Matrix..mp4
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13.5 - Data Preprocessing Feature Normalisation
13.5 - Data Preprocessing Feature Normalisation.mkv
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13.6 - Mean of a data matrix
13.6 - Mean of a data matrix.mp4
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13.7 - Data Preprocessing Column Standardization
13.7 - Data Preprocessing Column Standardization.mp4
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13.8 - Co-variance of a Data Matrix
13.8 - Co-variance of a Data Matrix.mp4
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13.9 - MNIST dataset (784 dimensional)
13.9 - MNIST dataset (784 dimensional).mp4
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14.10 - PCA for dimensionality reduction (not-visualization)
14.10 - PCA for dimensionality reduction (not-visualization).mp4
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14.1 - Why learn PCA
14.1 - Why learn PCA.mp4
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14.2 - Geometric intuition of PCA
14.2 - Geometric intuition of PCA.mp4
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14.3 - Mathematical objective function of PCA
14.3 - Mathematical objective function of PCA.mp4
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14.4 - Alternative formulation of PCA Distance minimization
14.4 - Alternative formulation of PCA Distance minimization.mp4
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14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction
14.5 - Eigen values and Eigen vectors (PCA) Dimensionality reduction.mp4
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14.6 - PCA for Dimensionality Reduction and Visualization
14.6 - PCA for Dimensionality Reduction and Visualization.mp4
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14.7 - Visualize MNIST dataset
14.7 - Visualize MNIST dataset.mp4
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14.8 - Limitations of PCA
14.8 - Limitations of PCA.mp4.mkv
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14.9 - PCA Code example
14.9 - PCA Code example.mp4
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15.1 - What is t-SNE
15.1 - What is t-SNE.mp4
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15.2 - Neighborhood of a point, Embedding
15.2 - Neighborhood of a point, Embedding.mp4
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15.3 - Geometric intuition of t-SNE
15.3 - Geometric intuition of t-SNE.mp4
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15.4 - Crowding Problem
15.4 - Crowding Problem.mp4
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15.5 - How to apply t-SNE and interpret its output
15.5 - How to apply t-SNE and interpret its output.mp4
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15.6 - t-SNE on MNIST
15.6 - t-SNE on MNIST.mp4
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15.7 - Code example of t-SNE
15.7 - Code example of t-SNE.mp4
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15.8 - Revision Questions
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16.1 - Questions & Answers
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17.10 - Avg-Word2Vec, tf-idf weighted Word2Vec
17.10 - Avg-Word2Vec, tf-idf weighted Word2Vec.mp4
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17.11 - Bag of Words( Code Sample)
17.11 - Bag of Words( Code Sample).mp4
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17.12 - Text Preprocessing( Code Sample)
17.12 - Text Preprocessing( Code Sample).mp4
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17.13 - Bi-Grams and n-grams (Code Sample)
17.13 - Bi-Grams and n-grams (Code Sample).mp4
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17.14 - TF-IDF (Code Sample)
17.14 - TF-IDF (Code Sample).mp4
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17.15 - Word2Vec (Code Sample)
17.15 - Word2Vec (Code Sample).mp4
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17.16 - Avg-Word2Vec and TFIDF-Word2Vec (Code Sample)
17.16 - Avg-Word2Vec and TFIDF-Word2Vec (Code Sample).mp4
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17.17 - Assignment-2 Apply t-SNE
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17.1 - Dataset overview Amazon Fine Food reviews(EDA)
17.1 - Dataset overview Amazon Fine Food reviews(EDA).mp4
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17.2 - Data Cleaning Deduplication
17.2 - Data Cleaning Deduplication.mp4
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17.3 - Why convert text to a vector
17.3 - Why convert text to a vector.mp4
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17.4 - Bag of Words (BoW)
17.4 - Bag of Words (BoW).mp4
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17.5 - Text Preprocessing Stemming
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Stop-word removal, Tokenization, Lemmatization (Featurizations - convert text to numeric vectors).mp4
17.6 - uni-gram, bi-gram, n-grams
17.6 - uni-gram, bi-gram, n-grams..mp4
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17.7 - tf-idf (term frequency- inverse document frequency)
17.7 - tf-idf (term frequency- inverse document frequency).mp4
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17.8 - Why use log in IDF
17.8 - Why use log in IDF.mp4
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17.9 - Word2Vec
17.9 - Word2Vec..mp4
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18.10 - KNN Limitations
18.10 - KNN Limitations.mp4
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18.11 - Decision surface for K-NN as K changes
18.11 - Decision surface for K-NN as K changes.mp4
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18.12 - Overfitting and Underfitting
18.12 - Overfitting and Underfitting.mp4
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18.13 - Need for Cross validation
18.13 - Need for Cross validation.mp4
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18.14 - K-fold cross validation
18.14 - K-fold cross validation.mp4
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18.15 - Visualizing train, validation and test datasets
18.15 - Visualizing train, validation and test datasets.mp4
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18.16 - How to determine overfitting and underfitting
18.16 - How to determine overfitting and underfitting.mp4
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18.17 - Time based splitting
18.17 - Time based splitting.mp4
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18.18 - k-NN for regression
18.18 - k-NN for regression.mp4
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18.19 - Weighted k-NN
18.19 - Weighted k-NN.mp4
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18.1 - How “Classification†works
18.1 - How “Classification†works.mp4
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18.20 - Voronoi diagram
18.20 - Voronoi diagram.mp4
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18.21 - Binary search tree
18.21 - Binary search tree.mp4
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18.22 - How to build a kd-tree
18.22 - How to build a kd-tree.mp4
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18.23 - Find nearest neighbours using kd-tree
18.23 - Find nearest neighbours using kd-tree.mp4
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18.24 - Limitations of Kd tree
18.24 - Limitations of Kd tree.mp4
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18.25 - Extensions
18.25 - Extensions.mp4
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18.26 - Hashing vs LSH
18.26 - Hashing vs LSH.mp4
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18.27 - LSH for cosine similarity
18.27 - LSH for cosine similarity.mp4
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18.28 - LSH for euclidean distance
18.28 - LSH for euclidean distance.mp4
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18.29 - Probabilistic class label
18.29 - Probabilistic class label.mp4
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18.2 - Data matrix notation
18.2 - Data matrix notation.mp4
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18.30 - Code SampleDecision boundary
18.30 - Code SampleDecision boundary ..mp4
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18.31 - Code SampleCross Validation
18.31 - Code SampleCross Validation.mp4
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18.32 - Revision Questions
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18.3 - Classification vs Regression (examples)
18.3 - Classification vs Regression (examples).mp4
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18.4 - K-Nearest Neighbours Geometric intuition with a toy example
18.4 - K-Nearest Neighbours Geometric intuition with a toy example.mp4
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18.5 - Failure cases of KNN
18.5 - Failure cases of KNN.mp4
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18.6 - Distance measures Euclidean(L2) , Manhattan(L1), Minkowski, Hamming
18.6 - Distance measures Euclidean(L2) , Manhattan(L1), Minkowski, Hamming.mp4
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18.7 - Cosine Distance & Cosine Similarity
18.7 - Cosine Distance & Cosine Similarity.mp4
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18.8 - How to measure the effectiveness of k-NN
18.8 - How to measure the effectiveness of k-NN.mp4
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18.9 - TestEvaluation time and space complexity
18.9 - TestEvaluation time and space complexity.mp4
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19.1 - Questions & Answers
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20.10 - Local reachability-density(A)
20.10 - Local reachability-density(A).mp4
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20.11 - Local outlier Factor(A)
20.11 - Local outlier Factor(A).mp4
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20.12 - Impact of Scale & Column standardization
20.12 - Impact of Scale & Column standardization.mp4
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20.13 - Interpretability
20.13 - Interpretability.mp4
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20.14 - Feature Importance and Forward Feature selection
20.14 - Feature Importance and Forward Feature selection.mp4
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20.15 - Handling categorical and numerical features
20.15 - Handling categorical and numerical features.mp4
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20.16 - Handling missing values by imputation
20.16 - Handling missing values by imputation.mp4
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20.17 - curse of dimensionality
20.17 - curse of dimensionality.mp4
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20.18 - Bias-Variance tradeoff
20.18 - Bias-Variance tradeoff.mp4
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20.19 - Intuitive understanding of bias-variance
20.19 - Intuitive understanding of bias-variance..mp4
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20.1 - Introduction
20.1 - Introduction.mp4
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20.20 - Revision Questions
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20.21 - best and wrost case of algorithm
20.21 - best and wrost case of algorithm.mp4
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20.2 - Imbalanced vs balanced dataset
20.2 - Imbalanced vs balanced dataset.mp4
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20.3 - Multi-class classification
20.3 - Multi-class classification.mp4
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20.4 - k-NN, given a distance or similarity matrix
20.4 - k-NN, given a distance or similarity matrix.mp4
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20.5 - Train and test set differences
20.5 - Train and test set differences.mp4
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20.6 - Impact of outliers
20.6 - Impact of outliers.mp4
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20.7 - Local outlier Factor (Simple solution Mean distance to Knn)
20.7 - Local outlier Factor (Simple solution Mean distance to Knn).mp4
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20.8 - k distance
20.8 - k distance.mp4
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20.9 - Reachability-Distance(A,B)
20.9 - Reachability-Distance(A,B).mp4
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2.10 - Control flow for loop
2.10 - Control flow for loop.mp4.mkv
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21.10 - Revision Questions
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21.1 - Accuracy
21.1 - Accuracy.mp4
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2.11 - Control flow break and continue
2.11 - Control flow break and continue.mp4
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21.2 - Confusion matrix, TPR, FPR, FNR, TNR
21.2 - Confusion matrix, TPR, FPR, FNR, TNR.mp4
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21.3 - Precision and recall, F1-score
21.3 - Precision and recall, F1-score.mp4
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21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC
21.4 - Receiver Operating Characteristic Curve (ROC) curve and AUC.mp4
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21.5 - Log-loss
21.5 - Log-loss.mp4
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21.6 - R-SquaredCoefficient of determination
21.6 - R-SquaredCoefficient of determination.mp4
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21.7 - Median absolute deviation (MAD)
21.7 - Median absolute deviation (MAD).mp4
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21.8 - Distribution of errors
21.8 - Distribution of errors.mp4
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21.9 - Assignment-3 Apply k-Nearest Neighbor
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2.1 - Python, Anaconda and relevant packages installations
2.1 - Python, Anaconda and relevant packages installations.mp4.mkv
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22.1 - Questions & Answers
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2.2 - Why learn Python
2.2 - Why learn Python.mp4
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23.10 - Bias and Variance tradeoff
23.10 - Bias and Variance tradeoff.mp4
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23.11 - Feature importance and interpretability
23.11 - Feature importance and interpretability.mp4
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23.12 - Imbalanced data
23.12 - Imbalanced data.mp4
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23.13 - Outliers
23.13 - Outliers.mp4
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23.14 - Missing values
23.14 - Missing values.mp4
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23.15 - Handling Numerical features (Gaussian NB)
23.15 - Handling Numerical features (Gaussian NB).mp4
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23.16 - Multiclass classification
23.16 - Multiclass classification.mp4
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23.17 - Similarity or Distance matrix
23.17 - Similarity or Distance matrix.mp4
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23.18 - Large dimensionality
23.18 - Large dimensionality.mp4
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23.19 - Best and worst cases
23.19 - Best and worst cases.mp4
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23.1 - Conditional probability
23.1 - Conditional probability.mp4
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23.20 - Code example
23.20 - Code example.mp4
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23.21 - Assignment-4 Apply Naive Bayes
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23.22 - Revision Questions
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23.2 - Independent vs Mutually exclusive events
23.2 - Independent vs Mutually exclusive events.mp4
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23.3 - Bayes Theorem with examples
23.3 - Bayes Theorem with examples.mp4
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23.4 - Exercise problems on Bayes Theorem
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23.5 - Naive Bayes algorithm
23.5 - Naive Bayes algorithm.mp4
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23.6 - Toy example Train and test stages
23.6 - Toy example Train and test stages.mp4
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23.7 - Naive Bayes on Text data
23.7 - Naive Bayes on Text data.mp4
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23.8 - LaplaceAdditive Smoothing
23.8 - LaplaceAdditive Smoothing.mp4
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23.9 - Log-probabilities for numerical stability
23.9 - Log-probabilities for numerical stability.mp4
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2.3 - Keywords and identifiers
2.3 - Keywords and identifiers.mp4
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24.10 - Column Standardization
24.10 - Column Standardization.mp4
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24.11 - Feature importance and Model interpretability
24.11 - Feature importance and Model interpretability.mp4
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24.12 - Collinearity of features
24.12 - Collinearity of features.mp4
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24.13 - TestRun time space and time complexity
24.13 - TestRun time space and time complexity.mp4
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24.14 - Real world cases
24.14 - Real world cases.mp4
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out.pdf
24.15 - Non-linearly separable data & feature engineering
24.15 - Non-linearly separable data & feature engineering.mp4
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24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV
24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV.mp4
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out.pdf
24.17 - Assignment-5 Apply Logistic Regression
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24.18 - Extensions to Generalized linear models
24.18 - Extensions to Generalized linear models.mp4
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24.1 - Geometric intuition of Logistic Regression
24.1 - Geometric intuition of Logistic Regression.mp4
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24.2 - Sigmoid function Squashing
24.2 - Sigmoid function Squashing.mp4
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24.3 - Mathematical formulation of Objective function
24.3 - Mathematical formulation of Objective function.mp4
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24.4 - Weight vector
24.4 - Weight vector.mp4
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24.5 - L2 Regularization Overfitting and Underfitting
24.5 - L2 Regularization Overfitting and Underfitting.mp4
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out.pdf
24.6 - L1 regularization and sparsity
24.6 - L1 regularization and sparsity.mp4
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24.7 - Probabilistic Interpretation Gaussian Naive Bayes
24.7 - Probabilistic Interpretation Gaussian Naive Bayes.mp4
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out.pdf
24.8 - Loss minimization interpretation
24.8 - Loss minimization interpretation.mp4
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24.9 - hyperparameters and random search
24.9 - hyperparameters and random search.mp4
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2.4 - comments, indentation and statements
2.4 - comments, indentation and statements.mp4
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out.pdf
25.1 - Geometric intuition of Linear Regression
25.1 - Geometric intuition of Linear Regression.mp4
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25.2 - Mathematical formulation
25.2 - Mathematical formulation.mp4
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25.3 - Real world Cases
25.3 - Real world Cases.mp4
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25.4 - Code sample for Linear Regression
25.4 - Code sample for Linear Regression.mp4
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out.pdf
2.5 - Variables and data types in Python
2.5 - Variables and data types in Python.mp4.mkv
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out.pdf
26.10 - Logistic regression formulation revisited
26.10 - Logistic regression formulation revisited.mp4
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26.11 - Why L1 regularization creates sparsity
26.11 - Why L1 regularization creates sparsity.mp4
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26.12 - Assignment 6 Implement SGD for linear regression
26.12 - Assignment 6 Implement SGD for linear regression.mp4
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26.13 - Revision questions
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out.pdf
26.1 - Differentiation
26.1 - Differentiation.mp4
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26.2 - Online differentiation tools
26.2 - Online differentiation tools.mp4
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26.3 - Maxima and Minima
26.3 - Maxima and Minima.mp4
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26.4 - Vector calculus Grad
26.4 - Vector calculus Grad.mp4
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26.5 - Gradient descent geometric intuition
26.5 - Gradient descent geometric intuition.mp4
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26.6 - Learning rate
26.6 - Learning rate.mp4
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26.7 - Gradient descent for linear regression
26.7 - Gradient descent for linear regression.mp4
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26.8 - SGD algorithm
26.8 - SGD algorithm.mp4
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26.9 - Constrained Optimization & PCA
26.9 - Constrained Optimization & PCA.mp4
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out.pdf
2.6 - Standard Input and Output
2.6 - Standard Input and Output.mp4
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out.pdf
27.1 - Questions & Answers
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out.pdf
2.7 - Operators
2.7 - Operators.mp4
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out.pdf
28.10 - Train and run time complexities
28.10 - Train and run time complexities.mp4
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28.11 - nu-SVM control errors and support vectors
28.11 - nu-SVM control errors and support vectors.mp4
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28.12 - SVM Regression
28.12 - SVM Regression.mp4
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28.13 - Cases
28.13 - Cases.mp4
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28.14 - Code Sample
28.14 - Code Sample.mp4
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28.15 - Assignment-7 Apply SVM
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28.16 - Revision Questions
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out.pdf
28.1 - Geometric Intution
28.1 - Geometric Intution.mp4
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28.2 - Mathematical derivation
28.2 - Mathematical derivation.mp4
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28.3 - Why we take values +1 and and -1 for Support vector planes
28.3 - Why we take values +1 and and -1 for Support vector planes.mp4
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28.4 - Loss function (Hinge Loss) based interpretation
28.4 - Loss function (Hinge Loss) based interpretation.mp4
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out.pdf
28.5 - Dual form of SVM formulation
28.5 - Dual form of SVM formulation.mp4
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28.6 - kernel trick
28.6 - kernel trick.mp4
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28.7 - Polynomial Kernel
28.7 - Polynomial Kernel.mp4
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28.8 - RBF-Kernel
28.8 - RBF-Kernel.mp4
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28.9 - Domain specific Kernels
28.9 - Domain specific Kernels.mp4
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2.8 - Control flow if else
2.8 - Control flow if else.mp4
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out.pdf
29.1 - Questions & Answers
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out.pdf
2.9 - Control flow while loop
2.9 - Control flow while loop.mp4
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30.10 - Overfitting and Underfitting
30.10 - Overfitting and Underfitting.mp4
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30.11 - Train and Run time complexity
30.11 - Train and Run time complexity.mp4
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30.12 - Regression using Decision Trees
30.12 - Regression using Decision Trees.mp4
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30.13 - Cases
30.13 - Cases.mp4
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30.14 - Code Samples
30.14 - Code Samples.mp4
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30.15 - Assignment-8 Apply Decision Trees
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30.16 - Revision Questions
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out.pdf
30.1 - Geometric Intuition of decision tree Axis parallel hyperplanes
30.1 - Geometric Intuition of decision tree Axis parallel hyperplanes.mp4
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30.2 - Sample Decision tree
30.2 - Sample Decision tree.mp4
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30.3 - Building a decision TreeEntropy
30.3 - Building a decision TreeEntropy.mp4
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30.4 - Building a decision TreeInformation Gain
30.4 - Building a decision TreeInformation Gain.mp4
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30.5 - Building a decision Tree Gini Impurity
30.5 - Building a decision Tree Gini Impurity.mp4
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30.6 - Building a decision Tree Constructing a DT
30.6 - Building a decision Tree Constructing a DT.mp4
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30.7 - Building a decision Tree Splitting numerical features
30.7 - Building a decision Tree Splitting numerical features.mp4
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30.8 - Feature standardization
30.8 - Feature standardization.mp4
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30.9 - Building a decision TreeCategorical features with many possible values
30.9 - Building a decision TreeCategorical features with many possible values.mp4
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31.1 - Questions & Answers
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out.pdf
3.1 - Lists
3.1 - Lists.mp4
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32.10 - Residuals, Loss functions and gradients
32.10 - Residuals, Loss functions and gradients.mp4
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32.11 - Gradient Boosting
32.11 - Gradient Boosting.mp4
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32.12 - Regularization by Shrinkage
32.12 - Regularization by Shrinkage.mp4
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32.13 - Train and Run time complexity
32.13 - Train and Run time complexity.mp4
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32.14 - XGBoost Boosting + Randomization
32.14 - XGBoost Boosting + Randomization.mp4
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32.15 - AdaBoost geometric intuition
32.15 - AdaBoost geometric intuition.mp4
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32.16 - Stacking models
32.16 - Stacking models.mp4
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32.17 - Cascading classifiers
32.17 - Cascading classifiers.mp4
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32.18 - Kaggle competitions vs Real world
32.18 - Kaggle competitions vs Real world.mp4
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32.19 - Assignment-9 Apply Random Forests & GBDT
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32.1 - What are ensembles
32.1 - What are ensembles.mp4
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32.20 - Revision Questions
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32.2 - Bootstrapped Aggregation (Bagging) Intuition
32.2 - Bootstrapped Aggregation (Bagging) Intuition.mp4
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32.3 - Random Forest and their construction
32.3 - Random Forest and their construction.mp4
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32.4 - Bias-Variance tradeoff
32.4 - Bias-Variance tradeoff.mp4
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32.5 - Train and run time complexity
32.5 - Train and run time complexity.mp4
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32.6 - BaggingCode Sample
32.6 - BaggingCode Sample.mp4
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32.7 - Extremely randomized trees
32.7 - Extremely randomized trees.mp4
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32.8 - Random Tree Cases
32.8 - Random Tree Cases.mp4
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32.9 - Boosting Intuition
32.9 - Boosting Intuition.mp4
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[FreeTutorials.Eu].url
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out.pdf
3.2 - Tuples part 1
3.2 - Tuples part 1.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
33.10 - Indicator variables
33.10 - Indicator variables.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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33.11 - Feature binning
33.11 - Feature binning.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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33.12 - Interaction variables
33.12 - Interaction variables.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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33.13 - Mathematical transforms
33.13 - Mathematical transforms.mp4
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33.14 - Model specific featurizations
33.14 - Model specific featurizations.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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33.15 - Feature orthogonality
33.15 - Feature orthogonality.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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33.16 - Domain specific featurizations
33.16 - Domain specific featurizations.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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33.17 - Feature slicing
33.17 - Feature slicing.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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33.18 - Kaggle Winners solutions
33.18 - Kaggle Winners solutions.mp4
[FreeCoursesOnline.Me].url
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33.1 - Introduction
33.1 - Introduction.mp4
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33.2 - Moving window for Time Series Data
33.2 - Moving window for Time Series Data.mp4
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[FTU Forum].url
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33.3 - Fourier decomposition
33.3 - Fourier decomposition.mp4
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[FTU Forum].url
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33.4 - Deep learning features LSTM
33.4 - Deep learning features LSTM.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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33.5 - Image histogram
33.5 - Image histogram.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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33.6 - Keypoints SIFT
33.6 - Keypoints SIFT..mp4
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[FTU Forum].url
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33.7 - Deep learning features CNN
33.7 - Deep learning features CNN.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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33.8 - Relational data
33.8 - Relational data.mp4
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33.9 - Graph data
33.9 - Graph data.mp4
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3.3 - Tuples part-2
3.3 - Tuples part-2.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
34.10 - AB testing
34.10 - AB testing..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
34.11 - Data Science Life cycle
34.11 - Data Science Life cycle.mp4.mkv
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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34.12 - VC dimension
34.12 - VC dimension.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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out.pdf
34.1 - Calibration of ModelsNeed for calibration
34.1 - Calibration of ModelsNeed for calibration.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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34.2 - Productionization and deployment of Machine Learning Models
34.2 - Productionization and deployment of Machine Learning Models.mp4.mkv
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out.pdf
34.3 - Calibration Plots
34.3 - Calibration Plots..mp4
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out.pdf
34.4 - Platt’s CalibrationScaling
34.4 - Platt’s CalibrationScaling..mp4
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34.5 - Isotonic Regression
34.5 - Isotonic Regression.mp4
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[FTU Forum].url
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out.pdf
34.6 - Code Samples
34.6 - Code Samples.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
34.7 - Modeling in the presence of outliers RANSAC
34.7 - Modeling in the presence of outliers RANSAC.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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out.pdf
34.8 - Productionizing models
34.8 - Productionizing models.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
34.9 - Retraining models periodically
34.9 - Retraining models periodically..mp4
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out.pdf
3.4 - Sets
3.4 - Sets.mp4
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out.pdf
35.10 - K-Medoids
35.10 - K-Medoids.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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35.11 - Determining the right K
35.11 - Determining the right K.mp4
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out.pdf
35.12 - Code Samples
35.12 - Code Samples.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
How you can help Team-FTU.txt
35.13 - Time and space complexity
35.13 - Time and space complexity.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
How you can help Team-FTU.txt
35.14 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
35.1 - What is Clustering
35.1 - What is Clustering.mp4
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35.2 - Unsupervised learning
35.2 - Unsupervised learning.mp4
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35.3 - Applications
35.3 - Applications.mp4
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[FreeTutorials.Eu].url
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35.4 - Metrics for Clustering
35.4 - Metrics for Clustering.mp4
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out.pdf
35.5 - K-Means Geometric intuition, Centroids
35.5 - K-Means Geometric intuition, Centroids.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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35.6 - K-Means Mathematical formulation Objective function
35.6 - K-Means Mathematical formulation Objective function.mp4
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35.7 - K-Means Algorithm
35.7 - K-Means Algorithm..mp4
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35.8 - How to initialize K-Means++
35.8 - How to initialize K-Means++.mp4
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out.pdf
35.9 - Failure casesLimitations
35.9 - Failure casesLimitations.mp4
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3.5 - Dictionary
3.5 - Dictionary.mp4
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out.pdf
36.1 - Agglomerative & Divisive, Dendrograms
36.1 - Agglomerative & Divisive, Dendrograms.mp4
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[FreeTutorials.Eu].url
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out.pdf
36.2 - Agglomerative Clustering
36.2 - Agglomerative Clustering.mp4
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out.pdf
36.3 - Proximity methods Advantages and Limitations
36.3 - Proximity methods Advantages and Limitations..mp4
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36.4 - Time and Space Complexity
36.4 - Time and Space Complexity.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
36.5 - Limitations of Hierarchical Clustering
36.5 - Limitations of Hierarchical Clustering.mp4
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36.6 - Code sample
36.6 - Code sample.mp4
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out.pdf
36.7 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
3.6 - Strings
3.6 - Strings.mp4
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out.pdf
37.10 - Assignment-10 Apply K-means, Agglomerative, DBSCAN clustering algorithms
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
37.11 - Revision Questions
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out.pdf
37.1 - Density based clustering
37.1 - Density based clustering.mp4
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37.2 - MinPts and Eps Density
37.2 - MinPts and Eps Density.mp4
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37.3 - Core, Border and Noise points
37.3 - Core, Border and Noise points.mp4
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37.4 - Density edge and Density connected points
37.4 - Density edge and Density connected points..mp4
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37.5 - DBSCAN Algorithm
37.5 - DBSCAN Algorithm.mp4
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37.6 - Hyper Parameters MinPts and Eps
37.6 - Hyper Parameters MinPts and Eps.mp4
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37.7 - Advantages and Limitations of DBSCAN
37.7 - Advantages and Limitations of DBSCAN.mp4
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37.8 - Time and Space Complexity
37.8 - Time and Space Complexity.mp4
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[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
37.9 - Code samples
37.9 - Code samples..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
38.10 - Matrix Factorization for recommender systems Netflix Prize Solution
38.10 - Matrix Factorization for recommender systems Netflix Prize Solution.mp4
[FreeCoursesOnline.Me].url
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out.pdf
38.11 - Cold Start problem
38.11 - Cold Start problem.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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38.12 - Word vectors as MF
38.12 - Word vectors as MF.mp4
[FreeCoursesOnline.Me].url
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38.13 - Eigen-Faces
38.13 - Eigen-Faces.mp4
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[FTU Forum].url
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38.14 - Code example
38.14 - Code example..mp4
[FreeCoursesOnline.Me].url
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out.pdf
38.15 - Assignment-11 Apply Truncated SVD
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38.16 - Revision Questions
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out.pdf
38.1 - Problem formulation Movie reviews
38.1 - Problem formulation Movie reviews.mp4
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38.2 - Content based vs Collaborative Filtering
38.2 - Content based vs Collaborative Filtering.mp4
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38.3 - Similarity based Algorithms
38.3 - Similarity based Algorithms.mp4
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38.4 - Matrix Factorization PCA, SVD
38.4 - Matrix Factorization PCA, SVD.mp4
[FreeCoursesOnline.Me].url
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38.5 - Matrix Factorization NMF
38.5 - Matrix Factorization NMF.mp4
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38.6 - Matrix Factorization for Collaborative filtering
38.6 - Matrix Factorization for Collaborative filtering.mp4
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38.7 - Matrix Factorization for feature engineering
38.7 - Matrix Factorization for feature engineering.mp4
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38.8 - Clustering as MF
38.8 - Clustering as MF.mp4
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out.pdf
38.9 - Hyperparameter tuning
38.9 - Hyperparameter tuning.mp4
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39.1 - Questions & Answers
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out.pdf
40.10 - Data Modeling Multi label Classification
40.10 - Data Modeling Multi label Classification.mp4
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out.pdf
40.11 - Data preparation
40.11 - Data preparation..mp4
[FreeCoursesOnline.Me].url
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out.pdf
40.12 - Train-Test Split
40.12 - Train-Test Split.mp4
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40.13 - Featurization
40.13 - Featurization.mp4
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40.14 - Logistic regression One VS Rest
40.14 - Logistic regression One VS Rest.mp4
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40.15 - Sampling data and tags+Weighted models
40.15 - Sampling data and tags+Weighted models..mp4
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40.16 - Logistic regression revisited
40.16 - Logistic regression revisited.mp4
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40.17 - Why not use advanced techniques
40.17 - Why not use advanced techniques.mp4
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40.18 - Assignments
40.18 - Assignments..mp4
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out.pdf
40.1 - BusinessReal world problem
40.1 - BusinessReal world problem.mp4
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out.pdf
40.2 - Business objectives and constraints
40.2 - Business objectives and constraints.mp4
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40.3 - Mapping to an ML problem Data overview
40.3 - Mapping to an ML problem Data overview.mp4
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40.4 - Mapping to an ML problemML problem formulation
40.4 - Mapping to an ML problemML problem formulation..mp4
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40.5 - Mapping to an ML problemPerformance metrics
40.5 - Mapping to an ML problemPerformance metrics..mp4
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out.pdf
40.6 - Hamming loss
40.6 - Hamming loss.mp4
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40.7 - EDAData Loading
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40.8 - EDAAnalysis of tags
40.8 - EDAAnalysis of tags.mp4
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40.9 - EDAData Preprocessing
40.9 - EDAData Preprocessing.mp4
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4.10 - Debugging Python
4.10 - Debugging Python.mp4
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41.10 - EDA Feature analysis
41.10 - EDA Feature analysis..mp4
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41.11 - EDA Data Visualization T-SNE
41.11 - EDA Data Visualization T-SNE..mp4
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41.12 - EDA TF-IDF weighted Word2Vec featurization
41.12 - EDA TF-IDF weighted Word2Vec featurization..mp4
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41.13 - ML Models Loading Data
41.13 - ML Models Loading Data.mp4
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41.14 - ML Models Random Model
41.14 - ML Models Random Model.mp4
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41.15 - ML Models Logistic Regression and Linear SVM
41.15 - ML Models Logistic Regression and Linear SVM.mp4
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41.16 - ML Models XGBoost
41.16 - ML Models XGBoost.mp4
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41.17 - Assignments
41.17 - Assignments.mp4
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41.1 - BusinessReal world problem Problem definition
41.1 - BusinessReal world problem Problem definition.mp4
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41.2 - Business objectives and constraints
41.2 - Business objectives and constraints..mp4
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41.3 - Mapping to an ML problem Data overview
41.3 - Mapping to an ML problem Data overview.mp4
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41.4 - Mapping to an ML problem ML problem and performance metric
41.4 - Mapping to an ML problem ML problem and performance metric..mp4
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41.5 - Mapping to an ML problem Train-test split
41.5 - Mapping to an ML problem Train-test split.mp4
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41.6 - EDA Basic Statistics
41.6 - EDA Basic Statistics..mp4
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41.7 - EDA Basic Feature Extraction
41.7 - EDA Basic Feature Extraction.mp4
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41.8 - EDA Text Preprocessing
41.8 - EDA Text Preprocessing.mp4
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41.9 - EDA Advanced Feature Extraction
41.9 - EDA Advanced Feature Extraction.mp4
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4.1 - Introduction
4.1 - Introduction.mp4
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42.10 - Text Pre-Processing Tokenization and Stop-word removal
42.10 - Text Pre-Processing Tokenization and Stop-word removal.mp4
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42.11 - Stemming
42.11 - Stemming.mp4
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42.12 - Text based product similarity Converting text to an n-D vector bag of words
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42.13 - Code for bag of words based product similarity
42.13 - Code for bag of words based product similarity.mp4
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42.14 - TF-IDF featurizing text based on word-importance
42.14 - TF-IDF featurizing text based on word-importance.mp4
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42.15 - Code for TF-IDF based product similarity
42.15 - Code for TF-IDF based product similarity.mp4
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42.16 - Code for IDF based product similarity
42.16 - Code for IDF based product similarity.mp4
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42.17 - Text Semantics based product similarity Word2Vec(featurizing text based on semantic similarity)
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42.18 - Code for Average Word2Vec product similarity
42.18 - Code for Average Word2Vec product similarity.mp4
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42.19 - TF-IDF weighted Word2Vec
42.19 - TF-IDF weighted Word2Vec.mp4
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42.1 - Problem Statement Recommend similar apparel products in e-commerce using product descriptions and Images
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42.20 - Code for IDF weighted Word2Vec product similarity
42.20 - Code for IDF weighted Word2Vec product similarity.mp4
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42.21 - Weighted similarity using brand and color
42.21 - Weighted similarity using brand and color.mp4
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42.22 - Code for weighted similarity
42.22 - Code for weighted similarity.mp4
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42.23 - Building a real world solution
42.23 - Building a real world solution.mp4
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42.24 - Deep learning based visual product similarityConvNets How to featurize an image edges, shapes, parts
[FreeCoursesOnline.Me].url
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42.25 - Using Keras + Tensorflow to extract features
42.25 - Using Keras + Tensorflow to extract features.mp4
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42.26 - Visual similarity based product similarity
42.26 - Visual similarity based product similarity.mp4
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42.27 - Measuring goodness of our solution AB testing
42.27 - Measuring goodness of our solution AB testing.mp4
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42.28 - Exercise Build a weighted Nearest neighbor model using Visual, Text, Brand and Color
[FreeCoursesOnline.Me].url
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42.2 - Plan of action
42.2 - Plan of action.mp4
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42.3 - Amazon product advertising API
42.3 - Amazon product advertising API.mp4
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42.4 - Data folders and paths
42.4 - Data folders and paths.mp4
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42.5 - Overview of the data and Terminology
42.5 - Overview of the data and Terminology.mp4
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42.6 - Data cleaning and understandingMissing data in various features
42.6 - Data cleaning and understandingMissing data in various features.mp4
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42.7 - Understand duplicate rows
42.7 - Understand duplicate rows.mp4
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42.8 - Remove duplicates Part 1
42.8 - Remove duplicates Part 1.mp4
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42.9 - Remove duplicates Part 2
42.9 - Remove duplicates Part 2.mp4
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4.2 - Types of functions
4.2 - Types of functions.mp4
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43.10 - ML models – using byte files only Random Model
43.10 - ML models – using byte files only Random Model.mp4
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43.11 - k-NN
43.11 - k-NN.mp4
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43.12 - Logistic regression
43.12 - Logistic regression.mp4
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43.13 - Random Forest and Xgboost
43.13 - Random Forest and Xgboost.mp4
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43.14 - ASM Files Feature extraction & Multiprocessing
43.14 - ASM Files Feature extraction & Multiprocessing..mp4
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43.15 - File-size feature
43.15 - File-size feature.mp4
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43.16 - Univariate analysis
43.16 - Univariate analysis.mp4
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43.17 - t-SNE analysis
43.17 - t-SNE analysis..mp4
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43.18 - ML models on ASM file features
43.18 - ML models on ASM file features.mp4
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43.19 - Models on all features t-SNE
43.19 - Models on all features t-SNE.mp4
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43.1 - Businessreal world problem Problem definition
43.1 - Businessreal world problem Problem definition.mp4
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43.20 - Models on all features RandomForest and Xgboost
43.20 - Models on all features RandomForest and Xgboost.mp4
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43.21 - Assignments
43.21 - Assignments..mp4
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43.2 - Businessreal world problem Objectives and constraints
43.2 - Businessreal world problem Objectives and constraints.mp4
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43.3 - Machine Learning problem mapping Data overview
43.3 - Machine Learning problem mapping Data overview..mp4
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43.4 - Machine Learning problem mapping ML problem
43.4 - Machine Learning problem mapping ML problem.mp4
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43.5 - Machine Learning problem mapping Train and test splitting
43.5 - Machine Learning problem mapping Train and test splitting.mp4
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43.6 - Exploratory Data Analysis Class distribution
43.6 - Exploratory Data Analysis Class distribution..mp4
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43.7 - Exploratory Data Analysis Feature extraction from byte files
43.7 - Exploratory Data Analysis Feature extraction from byte files.mp4
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43.8 - Exploratory Data Analysis Multivariate analysis of features from byte files
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43.9 - Exploratory Data Analysis Train-Test class distribution
43.9 - Exploratory Data Analysis Train-Test class distribution.mp4
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4.3 - Function arguments
4.3 - Function arguments.mp4
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44.10 - Exploratory Data AnalysisCold start problem
44.10 - Exploratory Data AnalysisCold start problem.mp4
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44.11 - Computing Similarity matricesUser-User similarity matrix
44.11 - Computing Similarity matricesUser-User similarity matrix.mp4
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44.12 - Computing Similarity matricesMovie-Movie similarity
44.12 - Computing Similarity matricesMovie-Movie similarity.mp4
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44.13 - Computing Similarity matricesDoes movie-movie similarity work
44.13 - Computing Similarity matricesDoes movie-movie similarity work.mp4
[FreeCoursesOnline.Me].url
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44.14 - ML ModelsSurprise library
44.14 - ML ModelsSurprise library.mp4
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44.15 - Overview of the modelling strategy
44.15 - Overview of the modelling strategy..mp4
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44.16 - Data Sampling
44.16 - Data Sampling..mp4
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44.17 - Google drive with intermediate files
44.17 - Google drive with intermediate files.mp4
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44.18 - Featurizations for regression
44.18 - Featurizations for regression..mp4
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44.19 - Data transformation for Surprise
44.19 - Data transformation for Surprise..mp4
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44.1 - BusinessReal world problemProblem definition
44.1 - BusinessReal world problemProblem definition.mp4
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44.20 - Xgboost with 13 features
44.20 - Xgboost with 13 features.mp4
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44.21 - Surprise Baseline model
44.21 - Surprise Baseline model..mp4
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44.22 - Xgboost + 13 features +Surprise baseline model
44.22 - Xgboost + 13 features +Surprise baseline model.mp4
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44.23 - Surprise KNN predictors
44.23 - Surprise KNN predictors.mp4
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44.24 - Matrix Factorization models using Surprise
44.24 - Matrix Factorization models using Surprise.mp4
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44.25 - SVD ++ with implicit feedback
44.25 - SVD ++ with implicit feedback.mp4
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44.26 - Final models with all features and predictors
44.26 - Final models with all features and predictors..mp4
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44.27 - Comparison between various models
44.27 - Comparison between various models..mp4
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44.28 - Assignments
44.28 - Assignments..mp4
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44.2 - Objectives and constraints
44.2 - Objectives and constraints.mp4
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[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
44.3 - Mapping to an ML problemData overview
44.3 - Mapping to an ML problemData overview..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
44.4 - Mapping to an ML problemML problem formulation
44.4 - Mapping to an ML problemML problem formulation.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
44.5 - Exploratory Data AnalysisData preprocessing
44.5 - Exploratory Data AnalysisData preprocessing.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
44.6 - Exploratory Data AnalysisTemporal Train-Test split
44.6 - Exploratory Data AnalysisTemporal Train-Test split..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
44.7 - Exploratory Data AnalysisPreliminary data analysis
44.7 - Exploratory Data AnalysisPreliminary data analysis..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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out.pdf
44.8 - Exploratory Data AnalysisSparse matrix representation
44.8 - Exploratory Data AnalysisSparse matrix representation.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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out.pdf
44.9 - Exploratory Data AnalysisAverage ratings for various slices
44.9 - Exploratory Data AnalysisAverage ratings for various slices.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
4.4 - Recursive functions
4.4 - Recursive functions.mp4
[FreeCoursesOnline.Me].url
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out.pdf
45.10 - Univariate AnalysisVariation Feature
45.10 - Univariate AnalysisVariation Feature.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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out.pdf
45.11 - Univariate AnalysisText feature
45.11 - Univariate AnalysisText feature.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.12 - Machine Learning ModelsData preparation
45.12 - Machine Learning ModelsData preparation.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.13 - Baseline Model Naive Bayes
45.13 - Baseline Model Naive Bayes.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.14 - K-Nearest Neighbors Classification
45.14 - K-Nearest Neighbors Classification.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.15 - Logistic Regression with class balancing
45.15 - Logistic Regression with class balancing.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.16 - Logistic Regression without class balancing
45.16 - Logistic Regression without class balancing.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.17 - Linear-SVM
45.17 - Linear-SVM..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.18 - Random-Forest with one-hot encoded features
45.18 - Random-Forest with one-hot encoded features.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.19 - Random-Forest with response-coded features
45.19 - Random-Forest with response-coded features.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.1 - BusinessReal world problem Overview
45.1 - BusinessReal world problem Overview.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.20 - Stacking Classifier
45.20 - Stacking Classifier.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.21 - Majority Voting classifier
45.21 - Majority Voting classifier.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.22 - Assignments
45.22 - Assignments..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
45.2 - Business objectives and constraints
45.2 - Business objectives and constraints..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
45.3 - ML problem formulation Data
45.3 - ML problem formulation Data.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.4 - ML problem formulation Mapping real world to ML problem#
45.4 - ML problem formulation Mapping real world to ML problem..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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45.4 - ML problem formulation Mapping real world to ML problem
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
45.5 - ML problem formulation Train, CV and Test data construction
45.5 - ML problem formulation Train, CV and Test data construction.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
45.6 - Exploratory Data AnalysisReading data & preprocessing
45.6 - Exploratory Data AnalysisReading data & preprocessing.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
45.7 - Exploratory Data AnalysisDistribution of Class-labels
45.7 - Exploratory Data AnalysisDistribution of Class-labels.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
45.8 - Exploratory Data Analysis “Random†Model
45.8 - Exploratory Data Analysis “Random†Model.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
45.9 - Univariate AnalysisGene feature
45.9 - Univariate AnalysisGene feature.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
4.5 - Lambda functions
4.5 - Lambda functions.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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out.pdf
46.10 - Data Cleaning Speed
46.10 - Data Cleaning Speed..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.11 - Data Cleaning Distance
46.11 - Data Cleaning Distance..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.12 - Data Cleaning Fare
46.12 - Data Cleaning Fare.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.13 - Data Cleaning Remove all outlierserroneous points
46.13 - Data Cleaning Remove all outlierserroneous points.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.14 - Data PreparationClusteringSegmentation
46.14 - Data PreparationClusteringSegmentation.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.15 - Data PreparationTime binning
46.15 - Data PreparationTime binning.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.16 - Data PreparationSmoothing time-series data
46.16 - Data PreparationSmoothing time-series data..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.17 - Data PreparationSmoothing time-series data cont
46.17 - Data PreparationSmoothing time-series data cont...mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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46.18 - Data Preparation Time series and Fourier transforms
46.18 - Data Preparation Time series and Fourier transforms..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.19 - Ratios and previous-time-bin values
46.19 - Ratios and previous-time-bin values.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
46.1 - BusinessReal world problem Overview
46.1 - BusinessReal world problem Overview.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.20 - Simple moving average
46.20 - Simple moving average.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.21 - Weighted Moving average
46.21 - Weighted Moving average..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
46.22 - Exponential weighted moving average
46.22 - Exponential weighted moving average.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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out.pdf
46.23 - Results
46.23 - Results..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.24 - Regression models Train-Test split & Features
46.24 - Regression models Train-Test split & Features.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
46.25 - Linear regression
46.25 - Linear regression..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.26 - Random Forest regression
46.26 - Random Forest regression.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.27 - Xgboost Regression
46.27 - Xgboost Regression.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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out.pdf
46.28 - Model comparison
46.28 - Model comparison.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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out.pdf
46.29 - Assignment
46.29 - Assignment..mp4
[FreeCoursesOnline.Me].url
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out.pdf
46.2 - Objectives and Constraints
46.2 - Objectives and Constraints.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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out.pdf
46.3 - Mapping to ML problem Data
46.3 - Mapping to ML problem Data.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.4 - Mapping to ML problem dask dataframes
46.4 - Mapping to ML problem dask dataframes.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
46.5 - Mapping to ML problem FieldsFeatures
46.5 - Mapping to ML problem FieldsFeatures..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.6 - Mapping to ML problem Time series forecastingRegression
46.6 - Mapping to ML problem Time series forecastingRegression.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
46.7 - Mapping to ML problem Performance metrics
46.7 - Mapping to ML problem Performance metrics.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.8 - Data Cleaning Latitude and Longitude data
46.8 - Data Cleaning Latitude and Longitude data.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
46.9 - Data Cleaning Trip Duration
46.9 - Data Cleaning Trip Duration..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
4.6 - Modules
4.6 - Modules.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
47.10 - Backpropagation
47.10 - Backpropagation..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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47.11 - Activation functions
47.11 - Activation functions.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
47.12 - Vanishing Gradient problem
47.12 - Vanishing Gradient problem..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
47.13 - Bias-Variance tradeoff
47.13 - Bias-Variance tradeoff..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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47.14 - Decision surfaces Playground
47.14 - Decision surfaces Playground.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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out.pdf
47.1 - History of Neural networks and Deep Learning
47.1 - History of Neural networks and Deep Learning..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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47.2 - How Biological Neurons work
47.2 - How Biological Neurons work.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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47.3 - Growth of biological neural networks
47.3 - Growth of biological neural networks.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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47.4 - Diagrammatic representation Logistic Regression and Perceptron
47.4 - Diagrammatic representation Logistic Regression and Perceptron.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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47.5 - Multi-Layered Perceptron (MLP)
47.5 - Multi-Layered Perceptron (MLP)..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
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47.6 - Notation
47.6 - Notation.mp4
[FreeCoursesOnline.Me].url
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[FTU Forum].url
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47.7 - Training a single-neuron model
47.7 - Training a single-neuron model..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
47.8 - Training an MLP Chain Rule
47.8 - Training an MLP Chain Rule.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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47.9 - Training an MLPMemoization
47.9 - Training an MLPMemoization.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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4.7 - Packages
4.7 - Packages.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
48.10 - Nesterov Accelerated Gradient (NAG)
48.10 - Nesterov Accelerated Gradient (NAG).mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
48.11 - OptimizersAdaGrad
48.11 - OptimizersAdaGrad.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
48.12 - Optimizers Adadelta andRMSProp
48.12 - Optimizers Adadelta andRMSProp.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
48.13 - Adam
48.13 - Adam.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
48.14 - Which algorithm to choose when
48.14 - Which algorithm to choose when.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
48.15 - Gradient Checking and clipping
48.15 - Gradient Checking and clipping.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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48.16 - Softmax and Cross-entropy for multi-class classification
48.16 - Softmax and Cross-entropy for multi-class classification..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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48.17 - How to train a Deep MLP
48.17 - How to train a Deep MLP.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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48.18 - Auto Encoders
48.18 - Auto Encoders..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
48.19 - Word2Vec CBOW
48.19 - Word2Vec CBOW.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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48.1 - Deep Multi-layer perceptrons1980s to 2010s
48.1 - Deep Multi-layer perceptrons1980s to 2010s.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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48.20 - Word2Vec Skip-gram
48.20 - Word2Vec Skip-gram.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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48.21 - Word2Vec Algorithmic Optimizations
48.21 - Word2Vec Algorithmic Optimizations..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
48.2 - Dropout layers & Regularization
48.2 - Dropout layers & Regularization..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
48.3 - Rectified Linear Units (ReLU)
48.3 - Rectified Linear Units (ReLU)..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
48.4 - Weight initialization
48.4 - Weight initialization..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
FTUApps.com website coming soon.txt
[FTU Forum].url
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48.5 - Batch Normalization
48.5 - Batch Normalization..mp4
[FreeCoursesOnline.Me].url
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FTUApps.com website coming soon.txt
[FTU Forum].url
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out.pdf
48.6 - OptimizersHill-descent analogy in 2D
48.6 - OptimizersHill-descent analogy in 2D.mp4
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48.7 - OptimizersHill descent in 3D and contours
48.7 - OptimizersHill descent in 3D and contours..mp4
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48.8 - SGD Recap
48.8 - SGD Recap.mp4
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48.9 - Batch SGD with momentum
48.9 - Batch SGD with momentum..mp4
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4.8 - File Handling
4.8 - File Handling.mp4
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49.10 - Model 3 Batch Normalization
49.10 - Model 3 Batch Normalization..mp4
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49.11 - Model 4 Dropout
49.11 - Model 4 Dropout..mp4
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49.12 - MNIST classification in Keras
49.12 - MNIST classification in Keras..mp4
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49.13 - Hyperparameter tuning in Keras
49.13 - Hyperparameter tuning in Keras..mp4
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49.14 - Exercise Try different MLP architectures on MNIST dataset
49.14 - Exercise Try different MLP architectures on MNIST dataset..mp4
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49.1 - Tensorflow and Keras overview
49.1 - Tensorflow and Keras overview.mp4
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49.2 - GPU vs CPU for Deep Learning
49.2 - GPU vs CPU for Deep Learning..mp4
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49.3 - Google Colaboratory
49.3 - Google Colaboratory..mp4
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49.4 - Install TensorFlow
49.4 - Install TensorFlow.mp4
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49.5 - Online documentation and tutorials
49.5 - Online documentation and tutorials.mp4
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49.6 - Softmax Classifier on MNIST dataset
49.6 - Softmax Classifier on MNIST dataset..mp4
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49.7 - MLP Initialization
49.7 - MLP Initialization.mp4
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49.8 - Model 1 Sigmoid activation
49.8 - Model 1 Sigmoid activation.mp4
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49.9 - Model 2 ReLU activation
49.9 - Model 2 ReLU activation..mp4
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4.9 - Exception Handling
4.9 - Exception Handling.mp4
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50.10 - Data Augmentation
50.10 - Data Augmentation..mp4
[FreeCoursesOnline.Me].url
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50.11 - Convolution Layers in Keras
50.11 - Convolution Layers in Keras.mp4
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50.12 - AlexNet
50.12 - AlexNet.mp4
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50.13 - VGGNet
50.13 - VGGNet.mp4
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50.14 - Residual Network
50.14 - Residual Network..mp4
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50.15 - Inception Network
50.15 - Inception Network..mp4
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50.16 - What is Transfer learning
50.16 - What is Transfer learning..mp4
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50.17 - Code example Cats vs Dogs
50.17 - Code example Cats vs Dogs..mp4
[FreeCoursesOnline.Me].url
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50.18 - Code Example MNIST dataset
50.18 - Code Example MNIST dataset..mp4
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50.19 - Assignment Try various CNN networks on MNIST dataset#
50.19 - Assignment Try various CNN networks on MNIST dataset..mp4
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50.1 - Biological inspiration Visual Cortex
50.1 - Biological inspiration Visual Cortex.mp4
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50.2 - ConvolutionEdge Detection on images
50.2 - ConvolutionEdge Detection on images..mp4
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50.3 - ConvolutionPadding and strides
50.3 - ConvolutionPadding and strides.mp4
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50.4 - Convolution over RGB images
50.4 - Convolution over RGB images..mp4
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50.5 - Convolutional layer
50.5 - Convolutional layer..mp4
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out.pdf
50.6 - Max-pooling
50.6 - Max-pooling..mp4
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50.7 - CNN Training Optimization
50.7 - CNN Training Optimization.mp4
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50.8 - Example CNN LeNet [1998]
50.8 - Example CNN LeNet [1998].mp4
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50.9 - ImageNet dataset
50.9 - ImageNet dataset..mp4
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51.10 - Code example IMDB Sentiment classification
51.10 - Code example IMDB Sentiment classification.mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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out.pdf
51.11 - Exercise Amazon Fine Food reviews LSTM model
51.11 - Exercise Amazon Fine Food reviews LSTM model..mp4
[FreeCoursesOnline.Me].url
[FreeTutorials.Eu].url
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[FTU Forum].url
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51.1 - Why RNNs
51.1 - Why RNNs.mp4
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51.2 - Recurrent Neural Network
51.2 - Recurrent Neural Network..mp4
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51.3 - Training RNNs Backprop
51.3 - Training RNNs Backprop..mp4
[FreeCoursesOnline.Me].url
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51.4 - Types of RNNs
51.4 - Types of RNNs..mp4
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51.5 - Need for LSTMGRU
51.5 - Need for LSTMGRU..mp4
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51.6 - LSTM
51.6 - LSTM..mp4
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51.7 - GRUs
51.7 - GRUs..mp4
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51.8 - Deep RNN
51.8 - Deep RNN..mp4
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51.9 - Bidirectional RNN
51.9 - Bidirectional RNN..mp4
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5.1 - Numpy Introduction
5.1 - Numpy Introduction.mp4
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52.1 - Questions and Answers
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out.pdf
5.2 - Numerical operations on Numpy
5.2 - Numerical operations on Numpy.mp4
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53.10 - NVIDIA’s end to end CNN model
53.10 - NVIDIA’s end to end CNN model..mp4
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53.11 - Train the model
53.11 - Train the model..mp4
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53.12 - Test and visualize the output
53.12 - Test and visualize the output..mp4
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53.13 - Extensions
53.13 - Extensions..mp4
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53.14 - Assignment
53.14 - Assignment..mp4
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53.1 - Self Driving Car Problem definition
53.1 - Self Driving Car Problem definition..mp4
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53.2 - Datasets#
53.2 - Datasets..mp4
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53.2 - Datasets
[FreeCoursesOnline.Me].url
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out.pdf
53.3 - Data understanding & Analysis Files and folders
53.3 - Data understanding & Analysis Files and folders..mp4
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53.4 - Dash-cam images and steering angles
53.4 - Dash-cam images and steering angles..mp4
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[FTU Forum].url
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53.5 - Split the dataset Train vs Test
53.5 - Split the dataset Train vs Test.mp4
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53.6 - EDA Steering angles
53.6 - EDA Steering angles.mp4
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53.7 - Mean Baseline model simple
53.7 - Mean Baseline model simple.mp4
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53.8 - Deep-learning modelDeep Learning for regression CNN, CNN+RNN
53.8 - Deep-learning modelDeep Learning for regression CNN, CNN+RNN.mp4
[FreeCoursesOnline.Me].url
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53.9 - Batch load the dataset
53.9 - Batch load the dataset..mp4
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54.10 - MIDI music generation
54.10 - MIDI music generation..mp4
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54.11 - Survey blog
54.11 - Survey blog.mp4
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54.1 - Real-world problem
54.1 - Real-world problem.mp4
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54.2 - Music representation
54.2 - Music representation.mp4
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54.3 - Char-RNN with abc-notation Char-RNN model
54.3 - Char-RNN with abc-notation Char-RNN model.mp4
[FreeCoursesOnline.Me].url
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54.4 - Char-RNN with abc-notation Data preparation
54.4 - Char-RNN with abc-notation Data preparation..mp4
[FreeCoursesOnline.Me].url
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54.5 - Char-RNN with abc-notationMany to Many RNN ,TimeDistributed-Dense layer
54.5 - Char-RNN with abc-notationMany to Many RNN ,TimeDistributed-Dense layer.mp4
[FreeCoursesOnline.Me].url
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54.6 - Char-RNN with abc-notation State full RNN
54.6 - Char-RNN with abc-notation State full RNN.mp4
[FreeCoursesOnline.Me].url
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54.7 - Char-RNN with abc-notation Model architecture,Model training
54.7 - Char-RNN with abc-notation Model architecture,Model training..mp4
[FreeCoursesOnline.Me].url
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54.8 - Char-RNN with abc-notation Music generation
54.8 - Char-RNN with abc-notation Music generation..mp4
[FreeCoursesOnline.Me].url
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out.pdf
54.9 - Char-RNN with abc-notation Generate tabla music
54.9 - Char-RNN with abc-notation Generate tabla music.mp4
[FreeCoursesOnline.Me].url
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out.pdf
55.1 - Human Activity Recognition Problem definition
55.1 - Human Activity Recognition Problem definition.mp4
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55.2 - Dataset understanding
55.2 - Dataset understanding.mp4
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55.3 - Data cleaning & preprocessing
55.3 - Data cleaning & preprocessing.mp4
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55.4 - EDAUnivariate analysis
55.4 - EDAUnivariate analysis..mp4
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55.5 - EDAData visualization using t-SNE
55.5 - EDAData visualization using t-SNE.mp4
[FreeCoursesOnline.Me].url
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55.6 - Classical ML models
55.6 - Classical ML models..mp4
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55.7 - Deep-learning Model
55.7 - Deep-learning Model..mp4
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55.8 - Exercise Build deeper LSTM models and hyper-param tune them
55.8 - Exercise Build deeper LSTM models and hyper-param tune them.mp4
[FreeCoursesOnline.Me].url
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56.10 - Feature engineering on GraphsJaccard & Cosine Similarities
56.10 - Feature engineering on GraphsJaccard & Cosine Similarities.mp4
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56.11 - PageRank
56.11 - PageRank.mp4
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56.12 - Shortest Path
56.12 - Shortest Path.mp4
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56.13 - Connected-components
56.13 - Connected-components.mp4
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56.14 - Adar Index
56.14 - Adar Index.mp4
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56.15 - Kartz Centrality
56.15 - Kartz Centrality.mp4
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56.16 - HITS Score
56.16 - HITS Score.mp4
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56.17 - SVD
56.17 - SVD.mp4
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56.18 - Weight features
56.18 - Weight features.mp4
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56.19 - Modeling
56.19 - Modeling.mp4
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56.1 - Problem definition
56.1 - Problem definition..mp4
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56.2 - Overview of Graphs nodevertex, edgelink, directed-edge, path
56.2 - Overview of Graphs nodevertex, edgelink, directed-edge, path..mp4
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56.3 - Data format & Limitations
56.3 - Data format & Limitations..mp4
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56.4 - Mapping to a supervised classification problem
56.4 - Mapping to a supervised classification problem..mp4
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56.5 - Business constraints & Metrics
56.5 - Business constraints & Metrics..mp4
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56.6 - EDABasic Stats
56.6 - EDABasic Stats.mp4
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56.7 - EDAFollower and following stats
56.7 - EDAFollower and following stats..mp4
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56.8 - EDABinary Classification Task
56.8 - EDABinary Classification Task.mp4
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56.9 - EDATrain and test split
56.9 - EDATrain and test split..mp4
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57.10 - ORDER BY
57.10 - ORDER BY.mp4
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57.11 - DISTINCT
57.11 - DISTINCT.mp4
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57.12 - WHERE, Comparison operators, NULL
57.12 - WHERE, Comparison operators, NULL.mp4
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57.13 - Logical Operators
57.13 - Logical Operators.mp4
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57.14 - Aggregate Functions COUNT, MIN, MAX, AVG, SUM
57.14 - Aggregate Functions COUNT, MIN, MAX, AVG, SUM.mp4
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57.15 - GROUP BY
57.15 - GROUP BY.mp4
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57.16 - HAVING
57.16 - HAVING.mp4
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57.17 - Order of keywords#
57.17 - Order of keywords..mp4
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57.18 - Join and Natural Join
57.18 - Join and Natural Join.mp4
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57.19 - Inner, Left, Right and Outer joins
57.19 - Inner, Left, Right and Outer joins..mp4
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57.1 - Introduction to Databases
57.1 - Introduction to Databases.mp4
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57.20 - Sub QueriesNested QueriesInner Queries
57.20 - Sub QueriesNested QueriesInner Queries.mp4
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57.21 - DMLINSERT
57.21 - DMLINSERT.mp4
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57.22 - DMLUPDATE , DELETE
57.22 - DMLUPDATE , DELETE.mp4
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57.23 - DDLCREATE TABLE
57.23 - DDLCREATE TABLE.mp4
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57.24 - DDLALTER ADD, MODIFY, DROP
57.24 - DDLALTER ADD, MODIFY, DROP.mp4
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57.25 - DDLDROP TABLE, TRUNCATE, DELETE
57.25 - DDLDROP TABLE, TRUNCATE, DELETE.mp4
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57.26 - Data Control Language GRANT, REVOKE
57.26 - Data Control Language GRANT, REVOKE.mp4
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57.27 - Learning resources
57.27 - Learning resources.mp4
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57.2 - Why SQL
57.2 - Why SQL.mp4
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57.3 - Execution of an SQL statement
57.3 - Execution of an SQL statement..mp4
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57.4 - IMDB dataset
57.4 - IMDB dataset.mp4
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57.5 - Installing MySQL
57.5 - Installing MySQL.mp4
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57.6 - Load IMDB data
57.6 - Load IMDB data..mp4
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57.7 - USE, DESCRIBE, SHOW TABLES
57.7 - USE, DESCRIBE, SHOW TABLES.mp4
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57.8 - SELECT
57.8 - SELECT.mp4
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57.9 - LIMIT, OFFSET
57.9 - LIMIT, OFFSET.mp4
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59.1 - Revision Questions
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59.2 - Questions
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59.3 - External resources for Interview Questions
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out.pdf
6.1 - Getting started with Matplotlib
6.1 - Getting started with Matplotlib.mp4
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7.1 - Getting started with pandas
7.1 - Getting started with pandas.mp4
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7.2 - Data Frame Basics
7.2 - Data Frame Basics.mp4
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7.3 - Key Operations on Data Frames
7.3 - Key Operations on Data Frames.mp4
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8.1 - Space and Time Complexity Find largest number in a list
8.1 - Space and Time Complexity Find largest number in a list.mp4
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8.2 - Binary search
8.2 - Binary search.mp4
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8.3 - Find elements common in two lists
8.3 - Find elements common in two lists.mp4
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8.4 - Find elements common in two lists using a HashtableDict
8.4 - Find elements common in two lists using a HashtableDict.mp4
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9.10 - Percentiles and Quantiles
9.10 - Percentiles and Quantiles.mp4
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9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation)
9.11 - IQR(Inter Quartile Range) and MAD(Median Absolute Deviation).mp4
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9.12 - Box-plot with Whiskers
9.12 - Box-plot with Whiskers.mp4
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9.13 - Violin Plots
9.13 - Violin Plots.mp4
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9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis
9.14 - Summarizing Plots, Univariate, Bivariate and Multivariate analysis.mp4
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9.15 - Multivariate Probability Density, Contour Plot
9.15 - Multivariate Probability Density, Contour Plot.mp4
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9.16 - Exercise Perform EDA on Haberman dataset
9.16 - Exercise Perform EDA on Haberman dataset.mp4
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9.1 - Introduction to IRIS dataset and 2D scatter plot
9.1 - Introduction to IRIS dataset and 2D scatter plot.mp4.mkv
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9.2 - 3D scatter plot
9.2 - 3D scatter plot.mp4
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9.3 - Pair plots
9.3 - Pair plots.mp4
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9.4 - Limitations of Pair Plots
9.4 - Limitations of Pair Plots.mp4.webm
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9.5 - Histogram and Introduction to PDF(Probability Density Function)
9.5 - Histogram and Introduction to PDF(Probability Density Function).mkv
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9.6 - Univariate Analysis using PDF
9.6 - Univariate Analysis using PDF.mp4
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9.7 - CDF(Cumulative Distribution Function)
9.7 - CDF(Cumulative Distribution Function).mp4
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9.8 - Mean, Variance and Standard Deviation
9.8 - Mean, Variance and Standard Deviation.mp4
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9.9 - Median
9.9 - Median.mp4
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