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[CourseClub NET] Coursera - Machine Learning
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Torrent Files List
001.Welcome (Size: 1.82 GB) (Files: 229)
001.Welcome
001. Welcome to Machine Learning!.mp4
001. Welcome to Machine Learning!.srt
002.Introduction
002. Welcome.mp4
002. Welcome.srt
003. What is Machine Learning.mp4
003. What is Machine Learning.srt
004. Supervised Learning.mp4
004. Supervised Learning.srt
005. Unsupervised Learning.mp4
005. Unsupervised Learning.srt
003.Model and Cost Function
006. Model Representation.mp4
006. Model Representation.srt
007. Cost Function.mp4
007. Cost Function.srt
008. Cost Function - Intuition I.mp4
008. Cost Function - Intuition I.srt
009. Cost Function - Intuition II.mp4
009. Cost Function - Intuition II.srt
004.Parameter Learning
010. Gradient Descent.mp4
010. Gradient Descent.srt
011. Gradient Descent Intuition.mp4
011. Gradient Descent Intuition.srt
012. Gradient Descent For Linear Regression.mp4
012. Gradient Descent For Linear Regression.srt
005.Linear Algebra Review
013. Matrices and Vectors.mp4
013. Matrices and Vectors.srt
014. Addition and Scalar Multiplication.mp4
014. Addition and Scalar Multiplication.srt
015. Matrix Vector Multiplication.mp4
015. Matrix Vector Multiplication.srt
016. Matrix Matrix Multiplication.mp4
016. Matrix Matrix Multiplication.srt
017. Matrix Multiplication Properties.mp4
017. Matrix Multiplication Properties.srt
018. Inverse and Transpose.mp4
018. Inverse and Transpose.srt
006.Multivariate Linear Regression
019. Multiple Features.mp4
019. Multiple Features.srt
020. Gradient Descent for Multiple Variables.mp4
020. Gradient Descent for Multiple Variables.srt
021. Gradient Descent in Practice I - Feature Scaling.mp4
021. Gradient Descent in Practice I - Feature Scaling.srt
022. Gradient Descent in Practice II - Learning Rate.mp4
022. Gradient Descent in Practice II - Learning Rate.srt
023. Features and Polynomial Regression.mp4
023. Features and Polynomial Regression.srt
007.Computing Parameters Analytically
024. Normal Equation.mp4
024. Normal Equation.srt
025. Normal Equation Noninvertibility.mp4
025. Normal Equation Noninvertibility.srt
008.Submitting Programming Assignments
026. Working on and Submitting Programming Assignments.mp4
026. Working on and Submitting Programming Assignments.srt
009.Octave Matlab Tutorial
027. Basic Operations.mp4
027. Basic Operations.srt
028. Moving Data Around.mp4
028. Moving Data Around.srt
029. Computing on Data.mp4
029. Computing on Data.srt
030. Plotting Data.mp4
030. Plotting Data.srt
031. Control Statements for, while, if statement.mp4
031. Control Statements for, while, if statement.srt
032. Vectorization.mp4
032. Vectorization.srt
010.Classification and Representation
033. Classification.mp4
033. Classification.srt
034. Hypothesis Representation.mp4
034. Hypothesis Representation.srt
035. Decision Boundary.mp4
035. Decision Boundary.srt
011.Logistic Regression Model
036. Cost Function.mp4
036. Cost Function.srt
037. Simplified Cost Function and Gradient Descent.mp4
037. Simplified Cost Function and Gradient Descent.srt
038. Advanced Optimization.mp4
038. Advanced Optimization.srt
012.Multiclass Classification
039. Multiclass Classification One-vs-all.mp4
039. Multiclass Classification One-vs-all.srt
013.Solving the Problem of Overfitting
040. The Problem of Overfitting.mp4
040. The Problem of Overfitting.srt
041. Cost Function.mp4
041. Cost Function.srt
042. Regularized Linear Regression.mp4
042. Regularized Linear Regression.srt
043. Regularized Logistic Regression.mp4
043. Regularized Logistic Regression.srt
014.Motivations
044. Non-linear Hypotheses.mp4
044. Non-linear Hypotheses.srt
045. Neurons and the Brain.mp4
045. Neurons and the Brain.srt
015.Neural Networks
046. Model Representation I.mp4
046. Model Representation I.srt
047. Model Representation II.mp4
047. Model Representation II.srt
016.Applications
048. Examples and Intuitions I.mp4
048. Examples and Intuitions I.srt
049. Examples and Intuitions II.mp4
049. Examples and Intuitions II.srt
050. Multiclass Classification.mp4
050. Multiclass Classification.srt
017.Cost Function and Backpropagation
051. Cost Function.mp4
051. Cost Function.srt
052. Backpropagation Algorithm.mp4
052. Backpropagation Algorithm.srt
053. Backpropagation Intuition.mp4
053. Backpropagation Intuition.srt
018.Backpropagation in Practice
054. Implementation Note Unrolling Parameters.mp4
054. Implementation Note Unrolling Parameters.srt
055. Gradient Checking.mp4
055. Gradient Checking.srt
056. Random Initialization.mp4
056. Random Initialization.srt
057. Putting It Together.mp4
057. Putting It Together.srt
019.Application of Neural Networks
058. Autonomous Driving.mp4
058. Autonomous Driving.srt
020.Evaluating a Learning Algorithm
059. Deciding What to Try Next.mp4
059. Deciding What to Try Next.srt
060. Evaluating a Hypothesis.mp4
060. Evaluating a Hypothesis.srt
061. Model Selection and Train Validation Test Sets.mp4
061. Model Selection and Train Validation Test Sets.srt
021.Bias vs. Variance
062. Diagnosing Bias vs. Variance.mp4
062. Diagnosing Bias vs. Variance.srt
063. Regularization and Bias Variance.mp4
063. Regularization and Bias Variance.srt
064. Learning Curves.mp4
064. Learning Curves.srt
065. Deciding What to Do Next Revisited.mp4
065. Deciding What to Do Next Revisited.srt
022.Building a Spam Classifier
066. Prioritizing What to Work On.mp4
066. Prioritizing What to Work On.srt
067. Error Analysis.mp4
067. Error Analysis.srt
023.Handling Skewed Data
068. Error Metrics for Skewed Classes.mp4
068. Error Metrics for Skewed Classes.srt
069. Trading Off Precision and Recall.mp4
069. Trading Off Precision and Recall.srt
024.Using Large Data Sets
070. Data For Machine Learning.mp4
070. Data For Machine Learning.srt
025.Large Margin Classification
071. Optimization Objective.mp4
071. Optimization Objective.srt
072. Large Margin Intuition.mp4
072. Large Margin Intuition.srt
073. Mathematics Behind Large Margin Classification.mp4
073. Mathematics Behind Large Margin Classification.srt
026.Kernels
074. Kernels I.mp4
074. Kernels I.srt
075. Kernels II.mp4
075. Kernels II.srt
027.SVMs in Practice
076. Using An SVM.mp4
076. Using An SVM.srt
028.Clustering
077. Unsupervised Learning Introduction.mp4
077. Unsupervised Learning Introduction.srt
078. K-Means Algorithm.mp4
078. K-Means Algorithm.srt
079. Optimization Objective.mp4
079. Optimization Objective.srt
080. Random Initialization.mp4
080. Random Initialization.srt
081. Choosing the Number of Clusters.mp4
081. Choosing the Number of Clusters.srt
029.Motivation
082. Motivation I Data Compression.mp4
082. Motivation I Data Compression.srt
083. Motivation II Visualization.mp4
083. Motivation II Visualization.srt
030.Principal Component Analysis
084. Principal Component Analysis Problem Formulation.mp4
084. Principal Component Analysis Problem Formulation.srt
085. Principal Component Analysis Algorithm.mp4
085. Principal Component Analysis Algorithm.srt
031.Applying PCA
086. Reconstruction from Compressed Representation.mp4
086. Reconstruction from Compressed Representation.srt
087. Choosing the Number of Principal Components.mp4
087. Choosing the Number of Principal Components.srt
088. Advice for Applying PCA.mp4
088. Advice for Applying PCA.srt
032.Density Estimation
089. Problem Motivation.mp4
089. Problem Motivation.srt
090. Gaussian Distribution.mp4
090. Gaussian Distribution.srt
091. Algorithm.mp4
091. Algorithm.srt
033.Building an Anomaly Detection System
092. Developing and Evaluating an Anomaly Detection System.mp4
092. Developing and Evaluating an Anomaly Detection System.srt
093. Anomaly Detection vs. Supervised Learning.mp4
093. Anomaly Detection vs. Supervised Learning.srt
094. Choosing What Features to Use.mp4
094. Choosing What Features to Use.srt
034.Multivariate Gaussian Distribution (Optional)
095. Multivariate Gaussian Distribution.mp4
095. Multivariate Gaussian Distribution.srt
096. Anomaly Detection using the Multivariate Gaussian Distribution.mp4
096. Anomaly Detection using the Multivariate Gaussian Distribution.srt
035.Predicting Movie Ratings
097. Problem Formulation.mp4
097. Problem Formulation.srt
098. Content Based Recommendations.mp4
098. Content Based Recommendations.srt
036.Collaborative Filtering
099. Collaborative Filtering.mp4
099. Collaborative Filtering.srt
100. Collaborative Filtering Algorithm.mp4
100. Collaborative Filtering Algorithm.srt
037.Low Rank Matrix Factorization
101. Vectorization Low Rank Matrix Factorization.mp4
101. Vectorization Low Rank Matrix Factorization.srt
102. Implementational Detail Mean Normalization.mp4
102. Implementational Detail Mean Normalization.srt
038.Gradient Descent with Large Datasets
103. Learning With Large Datasets.mp4
103. Learning With Large Datasets.srt
104. Stochastic Gradient Descent.mp4
104. Stochastic Gradient Descent.srt
105. Mini-Batch Gradient Descent.mp4
105. Mini-Batch Gradient Descent.srt
106. Stochastic Gradient Descent Convergence.mp4
106. Stochastic Gradient Descent Convergence.srt
039.Advanced Topics
107. Online Learning.mp4
107. Online Learning.srt
108. Map Reduce and Data Parallelism.mp4
108. Map Reduce and Data Parallelism.srt
040.Photo OCR
109. Problem Description and Pipeline.mp4
109. Problem Description and Pipeline.srt
110. Sliding Windows.mp4
110. Sliding Windows.srt
111. Getting Lots of Data and Artificial Data.mp4
111. Getting Lots of Data and Artificial Data.srt
112. Ceiling Analysis What Part of the Pipeline to Work on Next.mp4
112. Ceiling Analysis What Part of the Pipeline to Work on Next.srt
041.Conclusion
113. Summary and Thank You.mp4
113. Summary and Thank You.srt
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