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[FreeTutorials Eu] Udemy - Machine Learning A-Z Become Kaggle Master
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10. Multiple Linear Regression (Size: 13.97 GB) (Files: 542)
10. Multiple Linear Regression
10. Case Study Part5.mp4
10. Case Study Part5.vtt
11. Case Study Part6 (RFE).mp4
11. Case Study Part6 (RFE).vtt
1.1 Multplr_LR_Code_for Udemy.zip.zip
1. Introduction.mp4
1. Introduction.vtt
2. Case Study part1.mp4
2. Case Study part1.vtt
3. Case Study part2.mp4
3. Case Study part2.vtt
4. Case Study part3.mp4
4. Case Study part3.vtt
5. Adjusted R Square.mp4
5. Adjusted R Square.vtt
6. Case Study Part1.mp4
6. Case Study Part1.vtt
7. Case Study Part2.mp4
7. Case Study Part2.vtt
8. Case Study Part3.mp4
8. Case Study Part3.vtt
9. Case Study Part4.mp4
9. Case Study Part4.vtt
11. HotstarNetflix Real world Case Study for Multiple Linear Regression
1.1 Hotstarcode-for-udemy.zip.zip
1. Introduction to the Problem Statement.mp4
1. Introduction to the Problem Statement.vtt
2. Playing With Data.mp4
2. Playing With Data.vtt
3. Building Model Part1.mp4
3. Building Model Part1.vtt
4. Building Model Part2.mp4
4. Building Model Part2.vtt
5. Building Model Part3.mp4
5. Building Model Part3.vtt
6. Verification of Model.mp4
6. Verification of Model.vtt
12. Gradient Descent
1.1 Gradient+Descent+Updated.zip.zip
1. Pre-Req For Gradient Descent Part1.mp4
1. Pre-Req For Gradient Descent Part1.vtt
2. Pre-Req For Gradient Descent Part2.mp4
2. Pre-Req For Gradient Descent Part2.vtt
3. Cost Functions.mp4
3. Cost Functions.vtt
4. Defining Cost Functions More Formally.mp4
4. Defining Cost Functions More Formally.vtt
5. Gradient Descent.mp4
5. Gradient Descent.vtt
6. Optimisation.mp4
6. Optimisation.vtt
7. Closed Form Vs Gradient Descent.mp4
7. Closed Form Vs Gradient Descent.vtt
8. Gradient Descent case study.mp4
8. Gradient Descent case study.vtt
13. KNN
10. Case Study.mp4
10. Case Study.vtt
11. Classification Case1.mp4
11. Classification Case1.vtt
1.1 KNN.zip.zip
12. Classification Case2.mp4
12. Classification Case2.vtt
13. Classification Case3.mp4
13. Classification Case3.vtt
14. Classification Case4.mp4
14. Classification Case4.vtt
1. Introduction to Classification.mp4
1. Introduction to Classification.vtt
2. Defining Classification Mathematically.mp4
2. Defining Classification Mathematically.vtt
3. Introduction to KNN.mp4
3. Introduction to KNN.vtt
4. Accuracy of KNN.mp4
4. Accuracy of KNN.vtt
5. Effectiveness of KNN.mp4
5. Effectiveness of KNN.vtt
6. Distance Metrics.mp4
6. Distance Metrics.vtt
7. Distance Metrics Part2.mp4
7. Distance Metrics Part2.vtt
8. Finding k.mp4
8. Finding k.vtt
9. KNN on Regression.mp4
9. KNN on Regression.vtt
14. Model Performance Metrics
1. Performance Metrics Part1.mp4
1. Performance Metrics Part1.vtt
2. Performance Metrics Part2.mp4
2. Performance Metrics Part2.vtt
3. Performance Metrics Part3.mp4
3. Performance Metrics Part3.vtt
15. Model Selection Part1
1.1 CrossValidation_Linear Regression.zip.zip
1. Model Creation Case1.mp4
1. Model Creation Case1.vtt
2. Model Creation Case2.mp4
2. Model Creation Case2.vtt
3. Gridsearch Case study Part1.mp4
3. Gridsearch Case study Part1.vtt
4. Gridsearch Case study Part2.mp4
4. Gridsearch Case study Part2.vtt
16. Naive Bayes
10. Case Study 2 Part1.mp4
10. Case Study 2 Part1.vtt
11. Case Study 2 Part2.mp4
11. Case Study 2 Part2.vtt
1.1 NaiveBayes.zip.zip
1. Introduction to Naive Bayes.mp4
1. Introduction to Naive Bayes.vtt
2. Bayes Theorem.mp4
2. Bayes Theorem.vtt
3. Practical Example from NB with One Column.mp4
3. Practical Example from NB with One Column.vtt
4. Practical Example from NB with Multiple Columns.mp4
4. Practical Example from NB with Multiple Columns.vtt
5. Naive Bayes On Text Data Part1.mp4
5. Naive Bayes On Text Data Part1.vtt
6. Naive Bayes On Text Data Part2.mp4
6. Naive Bayes On Text Data Part2.vtt
7. Laplace Smoothing.mp4
7. Laplace Smoothing.vtt
8. Bernoulli Naive Bayes.mp4
8. Bernoulli Naive Bayes.vtt
9. Case Study 1.mp4
9. Case Study 1.vtt
17. Logistic Regression
1.1 LogisticReg.zip.zip
1. Introduction.mp4
1. Introduction.vtt
2. Sigmoid Function.mp4
2. Sigmoid Function.vtt
3. Log Odds.mp4
3. Log Odds.vtt
4. Case Study.mp4
4. Case Study.vtt
18. Support Vector Machine (SVM)
10. Kernel Part2.mp4
10. Kernel Part2.vtt
11. Case Study 2.mp4
11. Case Study 2.vtt
1.1 SVM.zip.zip
12. Case Study 3 Part1.mp4
12. Case Study 3 Part1.vtt
13. Case Study 3 Part2.mp4
13. Case Study 3 Part2.vtt
14. Case Study 4.mp4
14. Case Study 4.vtt
1. Introduction.mp4
1. Introduction.vtt
2. Hyperplane Part1.mp4
2. Hyperplane Part1.vtt
3. Hyperplane Part2.mp4
3. Hyperplane Part2.vtt
4. Maths Behind SVM.mp4
4. Maths Behind SVM.vtt
5. Support Vectors.mp4
5. Support Vectors.vtt
6. Slack Variable.mp4
6. Slack Variable.vtt
7. SVM Case Study Part1.mp4
7. SVM Case Study Part1.vtt
8. SVM Case Study Part2.mp4
8. SVM Case Study Part2.vtt
9. Kernel Part1.mp4
9. Kernel Part1.vtt
19. Decision Tree
10. DT Case Study Part2.mp4
10. DT Case Study Part2.vtt
1.1 DT_forudemy.zip.zip
1. Introduction.mp4
1. Introduction.vtt
2. Example of DT.mp4
2. Example of DT.vtt
3. Homogenity.mp4
3. Homogenity.vtt
4. Gini Index.mp4
4. Gini Index.vtt
5. Information Gain Part1.mp4
5. Information Gain Part1.vtt
6. Information Gain Part2.mp4
6. Information Gain Part2.vtt
7. Advantages and Disadvantages of DT.mp4
7. Advantages and Disadvantages of DT.vtt
8. Preventing Overfitting Issues in DT.mp4
8. Preventing Overfitting Issues in DT.vtt
9. DT Case Study Part1.mp4
9. DT Case Study Part1.vtt
1. Python Fundamentals
10. Functions.mp4
10. Functions.vtt
11. String Part1.mp4
11. String Part1.vtt
12. String Part2.mp4
12. String Part2.vtt
13. List Part1.mp4
13. List Part1.vtt
14. List Part2.mp4
14. List Part2.vtt
15. List Part3.mp4
15. List Part3.vtt
16. List Part4.mp4
16. List Part4.vtt
17. Tuples.mp4
17. Tuples.vtt
18. Sets.mp4
18. Sets.vtt
19. Dictionaries.mp4
19. Dictionaries.vtt
1. Introduction to the course.mp4
1. Introduction to the course.vtt
20. Comprehentions.mp4
20. Comprehentions.vtt
2. Introduction to Kaggle.mp4
2. Introduction to Kaggle.vtt
3.1 Python-code-udemy.zip.zip
3.2 Installing-Python.Teclov.pdf.pdf
3. Installation of Python and Anaconda.mp4
3. Installation of Python and Anaconda.vtt
4.1 Python-code-udemy.zip.zip
4. Python Introduction.mp4
4. Python Introduction.vtt
5. Variables in Python.mp4
5. Variables in Python.vtt
6. Numeric Operations in Python.mp4
6. Numeric Operations in Python.vtt
7. Logical Operations.mp4
7. Logical Operations.vtt
8. If else Loop.mp4
8. If else Loop.vtt
9. for while Loop.mp4
9. for while Loop.vtt
20. Ensembling
10. Adaboost Part2.mp4
10. Adaboost Part2.vtt
11. Adaboost Case Study.mp4
11. Adaboost Case Study.vtt
1.1 Boosting.zip.zip
1.2 RF_forudemy.zip.zip
12. XGBoost.mp4
12. XGBoost.vtt
13. Boosting Part1.mp4
13. Boosting Part1.vtt
14. Boosting Part2.mp4
14. Boosting Part2.vtt
15. XGboost Algorithm.mp4
15. XGboost Algorithm.vtt
16. Case Study Part1.mp4
16. Case Study Part1.vtt
17. Case Study Part2.mp4
17. Case Study Part2.vtt
18. Case Study Part3.mp4
18. Case Study Part3.vtt
1. Introduction to Ensembles.mp4
1. Introduction to Ensembles.vtt
2. Bagging.mp4
2. Bagging.vtt
3. Advantages.mp4
3. Advantages.vtt
4. Runtime.mp4
4. Runtime.vtt
5. Case study.mp4
5. Case study.vtt
6. Introduction to Boosting.mp4
6. Introduction to Boosting.vtt
7. Weak Learners.mp4
7. Weak Learners.vtt
8. Shallow Decision Tree.mp4
8. Shallow Decision Tree.vtt
9. Adaboost Part1.mp4
9. Adaboost Part1.vtt
21. Model Selection Part2
1. Model Selection Part1.mp4
1. Model Selection Part1.vtt
2. Model Selection Part2.mp4
2. Model Selection Part2.vtt
3. Model Selection Part3.mp4
22. Unsupervised Learning
10. Case Study Part2.mp4
10. Case Study Part2.vtt
11. More on Segmentation.mp4
11. More on Segmentation.vtt
1.1 Unsupervised.zip.zip
12. Hierarchial Clustering.mp4
12. Hierarchial Clustering.vtt
13. Case Study.mp4
13. Case Study.vtt
1. Introduction to Clustering.mp4
1. Introduction to Clustering.vtt
2. Segmentation.mp4
2. Segmentation.vtt
3. Kmeans.mp4
3. Kmeans.vtt
4. Maths Behind Kmeans.mp4
4. Maths Behind Kmeans.vtt
5. More Maths.mp4
5. More Maths.vtt
6. Kmeans plus.mp4
6. Kmeans plus.vtt
7. Value of K.mp4
7. Value of K.vtt
8. Hopkins test.mp4
8. Hopkins test.vtt
9. Case Study Part1.mp4
9. Case Study Part1.vtt
23. Dimension Reduction
1.1 PCA code for udemy.zip.zip
1. Introduction.mp4
1. Introduction.vtt
2. PCA.mp4
2. PCA.vtt
3. Maths Behind PCA.mp4
3. Maths Behind PCA.vtt
4. Case Study Part1.mp4
4. Case Study Part1.vtt
5. Case Study Part2.mp4
5. Case Study Part2.vtt
24. Advanced Machine Learning Algorithms
10. Adjusted R Square.mp4
10. Adjusted R Square.vtt
1.1 AdvanceReg.zip.zip
1. Introduction.mp4
1. Introduction.vtt
2. Example Part1.mp4
2. Example Part1.vtt
3. Example Part2.mp4
3. Example Part2.vtt
4. Optimal Solution.mp4
4. Optimal Solution.vtt
5. Case study.mp4
5. Case study.vtt
6. Regularization.mp4
6. Regularization.vtt
7. Ridge and Lasso.mp4
7. Ridge and Lasso.vtt
8. Case Study.mp4
8. Case Study.vtt
9. Model Selection.mp4
9. Model Selection.vtt
25. Deep Learning
1. Expectations.mp4
1. Expectations.vtt
2. Introduction.mp4
2. Introduction.vtt
3. History.mp4
3. History.vtt
4. Perceptron.mp4
4. Perceptron.vtt
5. Multi Layered Perceptron.mp4
5. Multi Layered Perceptron.vtt
6. Neural Network Playground.mp4
6. Neural Network Playground.vtt
26. Project Kaggle
10. Response encoding and one hot encoder.mp4
10. Response encoding and one hot encoder.vtt
11. Laplace Smoothing and Calibrated classifier.mp4
11. Laplace Smoothing and Calibrated classifier.vtt
1.1 training.zip.zip
12. Significance of first categorical column.mp4
12. Significance of first categorical column.vtt
1.2 Teclov Project - Medical treatment.ipynb.zip.zip
13. Second Categorical column.mp4
13. Second Categorical column.vtt
14. Third Categorical column.mp4
14. Third Categorical column.vtt
15. Data pre-processing before building machine learning model.mp4
15. Data pre-processing before building machine learning model.vtt
16. Building Machine Learning model part1.mp4
16. Building Machine Learning model part1.vtt
17. Building Machine Learning model part2.mp4
17. Building Machine Learning model part2.vtt
18. Building Machine Learning model part3.mp4
18. Building Machine Learning model part3.vtt
19. Building Machine Learning model part4.mp4
19. Building Machine Learning model part4.vtt
1. Introduction to the Problem Statement.mp4
1. Introduction to the Problem Statement.vtt
20. Building Machine Learning model part5.mp4
20. Building Machine Learning model part5.vtt
21. Building Machine Learning model part6.mp4
21. Building Machine Learning model part6.vtt
2. Playing With The Data.mp4
2. Playing With The Data.vtt
3. Translating the Problem In Machine Learning World.mp4
3. Translating the Problem In Machine Learning World.vtt
4. Dealing with Text Data.mp4
4. Dealing with Text Data.vtt
5. Train, Test And Cross Validation Split.mp4
5. Train, Test And Cross Validation Split.vtt
6. Understanding Evaluation Matrix Log Loss.mp4
6. Understanding Evaluation Matrix Log Loss.vtt
7. Building A Worst Model.mp4
7. Building A Worst Model.vtt
8. Evaluating Worst ML Model.mp4
8. Evaluating Worst ML Model.vtt
9. First Categorical column analysis.mp4
9. First Categorical column analysis.vtt
2. Numpy
1.1 Teclov-numpy.ipynb.zip.zip
1. Introduction.mp4
1. Introduction.vtt
2. Numpy Operations Part1.mp4
2. Numpy Operations Part1.vtt
3. Numpy Operations Part2.mp4
3. Numpy Operations Part2.vtt
3. Pandas
10. groupby.mp4
10. groupby.vtt
11. Merging Part2.mp4
11. Merging Part2.vtt
1.1 Pandas.zip.zip
12. Pivot Table.mp4
12. Pivot Table.vtt
1. Introduction.mp4
1. Introduction.vtt
2. Series.mp4
2. Series.vtt
3. DataFrame.mp4
3. DataFrame.vtt
4. Operations Part1.mp4
4. Operations Part1.vtt
5. Operations Part2.mp4
5. Operations Part2.vtt
6. Indexes.mp4
6. Indexes.vtt
7. loc and iloc.mp4
7. loc and iloc.vtt
8. Reading CSV.mp4
8. Reading CSV.vtt
9. Merging Part1.mp4
9. Merging Part1.vtt
4. Some Fun With Maths
1. Linear Algebra Vectors.mp4
1. Linear Algebra Vectors.vtt
2. Linear Algebra Matrix Part1.mp4
2. Linear Algebra Matrix Part1.vtt
3. Linear Algebra Matrix Part2.mp4
3. Linear Algebra Matrix Part2.vtt
4. Linear Algebra Going From 2D to nD Part1.mp4
4. Linear Algebra Going From 2D to nD Part1.vtt
5. Linear Algebra 2D to nD Part2.mp4
5. Linear Algebra 2D to nD Part2.vtt
5. Inferential Statistics
10. Normal Distribution.mp4
10. Normal Distribution.vtt
11. z Score.mp4
11. z Score.vtt
12. Sampling.mp4
12. Sampling.vtt
13. Sampling Distribution.mp4
13. Sampling Distribution.vtt
14. Central Limit Theorem.mp4
14. Central Limit Theorem.vtt
15. Confidence Interval Part1.mp4
15. Confidence Interval Part1.vtt
16. Confidence Interval Part2.mp4
16. Confidence Interval Part2.vtt
1. Inferential Statistics.mp4
1. Inferential Statistics.vtt
2. Probability Theory.mp4
2. Probability Theory.vtt
3. Probability Distribution.mp4
3. Probability Distribution.vtt
4. Expected Values Part1.mp4
4. Expected Values Part1.vtt
5. Expected Values Part2.mp4
5. Expected Values Part2.vtt
6. Without Experiment.mp4
6. Without Experiment.vtt
7. Binomial Distribution.mp4
7. Binomial Distribution.vtt
8. Commulative Distribution.mp4
8. Commulative Distribution.vtt
9. PDF.mp4
9. PDF.vtt
6. Hypothesis Testing
10. Types of Error.mp4
10. Types of Error.vtt
11. t- distribution Part1.mp4
11. t- distribution Part1.vtt
1.1 z-table.pdf.pdf
12. t- distribution Part2.mp4
12. t- distribution Part2.vtt
1.2 t-table.pdf.pdf
1. Introduction.mp4
1. Introduction.vtt
2. NULL And Alternate Hypothesis.mp4
2. NULL And Alternate Hypothesis.vtt
3. Examples.mp4
3. Examples.vtt
4. OneTwo Tailed Tests.mp4
4. OneTwo Tailed Tests.vtt
5. Critical Value Method.mp4
5. Critical Value Method.vtt
6. z Table.mp4
6. z Table.vtt
7. Examples.mp4
7. Examples.vtt
8. More Examples.mp4
8. More Examples.vtt
9. p Value.mp4
9. p Value.vtt
7. Data Visualisation
1.1 Datavisual.zip.zip
1. Matplotlib.mp4
1. Matplotlib.vtt
2. Seaborn.mp4
2. Seaborn.vtt
3. Case Study.mp4
3. Case Study.vtt
4. Seaborn On Time Series Data.mp4
4. Seaborn On Time Series Data.vtt
8. Exploratory Data Analysis
10. Univariate Analysis Part1.mp4
10. Univariate Analysis Part1.vtt
11. Univariate Analysis Part2.mp4
11. Univariate Analysis Part2.vtt
12. Segmented Analysis.mp4
12. Segmented Analysis.vtt
13. Bivariate Analysis.mp4
13. Bivariate Analysis.vtt
14. Derived Columns.mp4
14. Derived Columns.vtt
1. Introduction.mp4
1. Introduction.vtt
2. Data Sourcing and Cleaning part1.mp4
2. Data Sourcing and Cleaning part1.vtt
3. Data Sourcing and Cleaning part2.mp4
3. Data Sourcing and Cleaning part2.vtt
4. Data Sourcing and Cleaning part3.mp4
4. Data Sourcing and Cleaning part3.vtt
5. Data Sourcing and Cleaning part4.mp4
5. Data Sourcing and Cleaning part4.vtt
6. Data Sourcing and Cleaning part5.mp4
6. Data Sourcing and Cleaning part5.vtt
7. Data Sourcing and Cleaning part6.mp4
7. Data Sourcing and Cleaning part6.vtt
8. Data Cleaning part1.mp4
8. Data Cleaning part1.vtt
9. Data Cleaning part2.mp4
9. Data Cleaning part2.vtt
9. Simple Linear Regression
10. Residual Square Error (RSE).mp4
10. Residual Square Error (RSE).vtt
1.1 code-LR-Teclov.zip.zip
1. Introduction to Machine Learning.mp4
1. Introduction to Machine Learning.vtt
2. Types of Machine Learning.mp4
2. Types of Machine Learning.vtt
3. Introduction to Linear Regression (LR).mp4
3. Introduction to Linear Regression (LR).vtt
4. How LR Works.mp4
4. How LR Works.vtt
5. Some Fun With Maths Behind LR.mp4
5. Some Fun With Maths Behind LR.vtt
6. R Square.mp4
6. R Square.vtt
7. LR Case Study Part1.mp4
7. LR Case Study Part1.vtt
8. LR Case Study Part2.mp4
8. LR Case Study Part2.vtt
9. LR Case Study Part3.mp4
9. LR Case Study Part3.vtt
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