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027.SVMs in Practice/076. Using An SVM.mp4 31.99 MB
009.Octave Matlab Tutorial/028. Moving Data Around.mp4 29.53 MB
025.Large Margin Classification/073. Mathematics Behind Large Margin Classification.mp4 28.48 MB
019.Application of Neural Networks/058. Autonomous Driving.mp4 28.3 MB
011.Logistic Regression Model/038. Advanced Optimization.mp4 26.77 MB
040.Photo OCR/111. Getting Lots of Data and Artificial Data.mp4 25.3 MB
009.Octave Matlab Tutorial/027. Basic Operations.mp4 24.9 MB
030.Principal Component Analysis/085. Principal Component Analysis Algorithm.mp4 24.29 MB
009.Octave Matlab Tutorial/031. Control Statements for, while, if statement.mp4 23.88 MB
007.Computing Parameters Analytically/024. Normal Equation.mp4 23.63 MB
018.Backpropagation in Practice/057. Putting It Together.mp4 23.55 MB
002.Introduction/005. Unsupervised Learning.mp4 23.33 MB
035.Predicting Movie Ratings/098. Content Based Recommendations.mp4 23.19 MB
026.Kernels/074. Kernels I.mp4 22.81 MB
026.Kernels/075. Kernels II.mp4 22.63 MB
034.Multivariate Gaussian Distribution (Optional)/096. Anomaly Detection using the Multivariate Gaussian Distribution.mp4 22.42 MB
009.Octave Matlab Tutorial/032. Vectorization.mp4 22.27 MB
017.Cost Function and Backpropagation/053. Backpropagation Intuition.mp4 22.23 MB
010.Classification and Representation/035. Decision Boundary.mp4 22.19 MB
040.Photo OCR/110. Sliding Windows.mp4 21.93 MB
040.Photo OCR/112. Ceiling Analysis What Part of the Pipeline to Work on Next.mp4 21.92 MB
025.Large Margin Classification/071. Optimization Objective.mp4 21.89 MB
034.Multivariate Gaussian Distribution (Optional)/095. Multivariate Gaussian Distribution.mp4 21.86 MB
029.Motivation/082. Motivation I Data Compression.mp4 21.45 MB
023.Handling Skewed Data/069. Trading Off Precision and Recall.mp4 21.3 MB
022.Building a Spam Classifier/067. Error Analysis.mp4 21.27 MB
039.Advanced Topics/108. Map Reduce and Data Parallelism.mp4 21.23 MB
038.Gradient Descent with Large Datasets/104. Stochastic Gradient Descent.mp4 20.99 MB
016.Applications/049. Examples and Intuitions II.mp4 20.93 MB
033.Building an Anomaly Detection System/092. Developing and Evaluating an Anomaly Detection System.mp4 20.53 MB
039.Advanced Topics/107. Online Learning.mp4 20.51 MB
009.Octave Matlab Tutorial/030. Plotting Data.mp4 20.08 MB
009.Octave Matlab Tutorial/029. Computing on Data.mp4 19.81 MB
031.Applying PCA/088. Advice for Applying PCA.mp4 19.74 MB
033.Building an Anomaly Detection System/094. Choosing What Features to Use.mp4 19.09 MB
017.Cost Function and Backpropagation/052. Backpropagation Algorithm.mp4 19.07 MB
020.Evaluating a Learning Algorithm/061. Model Selection and Train Validation Test Sets.mp4 19.04 MB
032.Density Estimation/091. Algorithm.mp4 18.94 MB
005.Linear Algebra Review/015. Matrix Vector Multiplication.mp4 18.93 MB
004.Parameter Learning/010. Gradient Descent.mp4 18.72 MB
015.Neural Networks/047. Model Representation II.mp4 18.4 MB
018.Backpropagation in Practice/055. Gradient Checking.mp4 18.35 MB
002.Introduction/002. Welcome.mp4 18.28 MB
038.Gradient Descent with Large Datasets/106. Stochastic Gradient Descent Convergence.mp4 18.11 MB
015.Neural Networks/046. Model Representation I.mp4 18 MB
023.Handling Skewed Data/068. Error Metrics for Skewed Classes.mp4 17.95 MB
028.Clustering/078. K-Means Algorithm.mp4 17.67 MB
024.Using Large Data Sets/070. Data For Machine Learning.mp4 17.31 MB
005.Linear Algebra Review/018. Inverse and Transpose.mp4 17.01 MB
003.Model and Cost Function/009. Cost Function - Intuition II.mp4 16.99 MB
013.Solving the Problem of Overfitting/043. Regularized Logistic Regression.mp4 16.77 MB
002.Introduction/004. Supervised Learning.mp4 16.68 MB
004.Parameter Learning/011. Gradient Descent Intuition.mp4 16.61 MB
004.Parameter Learning/012. Gradient Descent For Linear Regression.mp4 16.43 MB
035.Predicting Movie Ratings/097. Problem Formulation.mp4 16.41 MB
021.Bias vs. Variance/064. Learning Curves.mp4 16.39 MB
021.Bias vs. Variance/063. Regularization and Bias Variance.mp4 16.39 MB
005.Linear Algebra Review/016. Matrix Matrix Multiplication.mp4 16.29 MB
011.Logistic Regression Model/037. Simplified Cost Function and Gradient Descent.mp4 16.26 MB
011.Logistic Regression Model/036. Cost Function.mp4 15.83 MB
031.Applying PCA/087. Choosing the Number of Principal Components.mp4 15.64 MB
013.Solving the Problem of Overfitting/042. Regularized Linear Regression.mp4 15.63 MB
003.Model and Cost Function/008. Cost Function - Intuition I.mp4 15.53 MB
036.Collaborative Filtering/099. Collaborative Filtering.mp4 15.52 MB
013.Solving the Problem of Overfitting/041. Cost Function.mp4 15.51 MB
025.Large Margin Classification/072. Large Margin Intuition.mp4 15.21 MB
032.Density Estimation/090. Gaussian Distribution.mp4 15.19 MB
022.Building a Spam Classifier/066. Prioritizing What to Work On.mp4 15.06 MB
013.Solving the Problem of Overfitting/040. The Problem of Overfitting.mp4 14.93 MB
014.Motivations/044. Non-linear Hypotheses.mp4 14.74 MB
036.Collaborative Filtering/100. Collaborative Filtering Algorithm.mp4 14.71 MB
014.Motivations/045. Neurons and the Brain.mp4 14.57 MB
030.Principal Component Analysis/084. Principal Component Analysis Problem Formulation.mp4 13.98 MB
033.Building an Anomaly Detection System/093. Anomaly Detection vs. Supervised Learning.mp4 13.15 MB
006.Multivariate Linear Regression/021. Gradient Descent in Practice I - Feature Scaling.mp4 12.94 MB
018.Backpropagation in Practice/054. Implementation Note Unrolling Parameters.mp4 12.92 MB
037.Low Rank Matrix Factorization/102. Implementational Detail Mean Normalization.mp4 12.91 MB
037.Low Rank Matrix Factorization/101. Vectorization Low Rank Matrix Factorization.mp4 12.82 MB
006.Multivariate Linear Regression/022. Gradient Descent in Practice II - Learning Rate.mp4 12.56 MB
028.Clustering/081. Choosing the Number of Clusters.mp4 12.22 MB
021.Bias vs. Variance/062. Diagnosing Bias vs. Variance.mp4 12.18 MB
005.Linear Algebra Review/017. Matrix Multiplication Properties.mp4 12.15 MB
005.Linear Algebra Review/013. Matrices and Vectors.mp4 11.94 MB
006.Multivariate Linear Regression/019. Multiple Features.mp4 11.58 MB
006.Multivariate Linear Regression/023. Features and Polynomial Regression.mp4 11.54 MB
003.Model and Cost Function/007. Cost Function.mp4 11.51 MB
021.Bias vs. Variance/065. Deciding What to Do Next Revisited.mp4 11.43 MB
003.Model and Cost Function/006. Model Representation.mp4 11.42 MB
002.Introduction/003. What is Machine Learning.mp4 11.41 MB
010.Classification and Representation/033. Classification.mp4 11.32 MB
010.Classification and Representation/034. Hypothesis Representation.mp4 11.17 MB
028.Clustering/080. Random Initialization.mp4 11.15 MB
020.Evaluating a Learning Algorithm/060. Evaluating a Hypothesis.mp4 11.05 MB
028.Clustering/079. Optimization Objective.mp4 10.92 MB
032.Density Estimation/089. Problem Motivation.mp4 10.56 MB
040.Photo OCR/109. Problem Description and Pipeline.mp4 10.42 MB
017.Cost Function and Backpropagation/051. Cost Function.mp4 10.25 MB
016.Applications/048. Examples and Intuitions I.mp4 10.07 MB
018.Backpropagation in Practice/056. Random Initialization.mp4 9.81 MB
038.Gradient Descent with Large Datasets/105. Mini-Batch Gradient Descent.mp4 9.75 MB
020.Evaluating a Learning Algorithm/059. Deciding What to Try Next.mp4 9.35 MB
005.Linear Algebra Review/014. Addition and Scalar Multiplication.mp4 9.27 MB
001.Welcome/001. Welcome to Machine Learning!.mp4 9.13 MB
041.Conclusion/113. Summary and Thank You.mp4 9.08 MB
012.Multiclass Classification/039. Multiclass Classification One-vs-all.mp4 9.07 MB
008.Submitting Programming Assignments/026. Working on and Submitting Programming Assignments.mp4 8.96 MB
007.Computing Parameters Analytically/025. Normal Equation Noninvertibility.mp4 8.8 MB
038.Gradient Descent with Large Datasets/103. Learning With Large Datasets.mp4 8.54 MB
029.Motivation/083. Motivation II Visualization.mp4 8.3 MB
006.Multivariate Linear Regression/020. Gradient Descent for Multiple Variables.mp4 7.62 MB
031.Applying PCA/086. Reconstruction from Compressed Representation.mp4 7.16 MB
016.Applications/050. Multiclass Classification.mp4 7 MB
028.Clustering/077. Unsupervised Learning Introduction.mp4 5.16 MB
027.SVMs in Practice/076. Using An SVM.srt 41.09 KB
025.Large Margin Classification/073. Mathematics Behind Large Margin Classification.srt 33.8 KB
040.Photo OCR/111. Getting Lots of Data and Artificial Data.srt 33.19 KB
040.Photo OCR/110. Sliding Windows.srt 29.68 KB
007.Computing Parameters Analytically/024. Normal Equation.srt 29.45 KB
026.Kernels/075. Kernels II.srt 28.95 KB
002.Introduction/005. Unsupervised Learning.srt 27.45 KB
026.Kernels/074. Kernels I.srt 27.38 KB
039.Advanced Topics/108. Map Reduce and Data Parallelism.srt 27.22 KB
009.Octave Matlab Tutorial/028. Moving Data Around.srt 26.94 KB
030.Principal Component Analysis/085. Principal Component Analysis Algorithm.srt 26.91 KB
011.Logistic Regression Model/038. Advanced Optimization.srt 26.27 KB
018.Backpropagation in Practice/057. Putting It Together.srt 26.13 KB
039.Advanced Topics/107. Online Learning.srt 26.09 KB
034.Multivariate Gaussian Distribution (Optional)/095. Multivariate Gaussian Distribution.srt 25.84 KB
033.Building an Anomaly Detection System/092. Developing and Evaluating an Anomaly Detection System.srt 25.77 KB
034.Multivariate Gaussian Distribution (Optional)/096. Anomaly Detection using the Multivariate Gaussian Distribution.srt 24.84 KB
031.Applying PCA/088. Advice for Applying PCA.srt 24.83 KB
028.Clustering/078. K-Means Algorithm.srt 24.74 KB
009.Octave Matlab Tutorial/027. Basic Operations.srt 23.89 KB
033.Building an Anomaly Detection System/094. Choosing What Features to Use.srt 23.72 KB
021.Bias vs. Variance/064. Learning Curves.srt 23.34 KB
005.Linear Algebra Review/015. Matrix Vector Multiplication.srt 22.84 KB
032.Density Estimation/091. Algorithm.srt 22.13 KB
009.Octave Matlab Tutorial/031. Control Statements for, while, if statement.srt 22.02 KB
024.Using Large Data Sets/070. Data For Machine Learning.srt 21.85 KB
040.Photo OCR/112. Ceiling Analysis What Part of the Pipeline to Work on Next.srt 21.77 KB
017.Cost Function and Backpropagation/052. Backpropagation Algorithm.srt 21.51 KB
015.Neural Networks/047. Model Representation II.srt 21.13 KB
023.Handling Skewed Data/068. Error Metrics for Skewed Classes.srt 20.8 KB
025.Large Margin Classification/072. Large Margin Intuition.srt 20.07 KB
031.Applying PCA/087. Choosing the Number of Principal Components.srt 19.92 KB
005.Linear Algebra Review/018. Inverse and Transpose.srt 19.86 KB
025.Large Margin Classification/071. Optimization Objective.srt 19.83 KB
023.Handling Skewed Data/069. Trading Off Precision and Recall.srt 19.67 KB
035.Predicting Movie Ratings/098. Content Based Recommendations.srt 19.51 KB
022.Building a Spam Classifier/067. Error Analysis.srt 19.29 KB
036.Collaborative Filtering/099. Collaborative Filtering.srt 19.09 KB
029.Motivation/082. Motivation I Data Compression.srt 18.98 KB
002.Introduction/004. Supervised Learning.srt 18.87 KB
013.Solving the Problem of Overfitting/041. Cost Function.srt 18.61 KB
022.Building a Spam Classifier/066. Prioritizing What to Work On.srt 18.54 KB
013.Solving the Problem of Overfitting/040. The Problem of Overfitting.srt 18.19 KB
014.Motivations/044. Non-linear Hypotheses.srt 17.95 KB
010.Classification and Representation/035. Decision Boundary.srt 17.88 KB
017.Cost Function and Backpropagation/053. Backpropagation Intuition.srt 17.68 KB
038.Gradient Descent with Large Datasets/104. Stochastic Gradient Descent.srt 17.57 KB
009.Octave Matlab Tutorial/032. Vectorization.srt 17.32 KB
018.Backpropagation in Practice/055. Gradient Checking.srt 16.96 KB
020.Evaluating a Learning Algorithm/061. Model Selection and Train Validation Test Sets.srt 16.93 KB
028.Clustering/081. Choosing the Number of Clusters.srt 16.92 KB
009.Octave Matlab Tutorial/029. Computing on Data.srt 16.68 KB
009.Octave Matlab Tutorial/030. Plotting Data.srt 16.34 KB
004.Parameter Learning/010. Gradient Descent.srt 16.31 KB
013.Solving the Problem of Overfitting/043. Regularized Logistic Regression.srt 16.19 KB
006.Multivariate Linear Regression/021. Gradient Descent in Practice I - Feature Scaling.srt 16.02 KB
004.Parameter Learning/011. Gradient Descent Intuition.srt 15.94 KB
035.Predicting Movie Ratings/097. Problem Formulation.srt 15.87 KB
038.Gradient Descent with Large Datasets/106. Stochastic Gradient Descent Convergence.srt 15.67 KB
037.Low Rank Matrix Factorization/102. Implementational Detail Mean Normalization.srt 15.63 KB
036.Collaborative Filtering/100. Collaborative Filtering Algorithm.srt 15.55 KB
014.Motivations/045. Neurons and the Brain.srt 15.48 KB
037.Low Rank Matrix Factorization/101. Vectorization Low Rank Matrix Factorization.srt 15.38 KB
028.Clustering/080. Random Initialization.srt 15.33 KB
032.Density Estimation/089. Problem Motivation.srt 15.11 KB
006.Multivariate Linear Regression/023. Features and Polynomial Regression.srt 14.99 KB
005.Linear Algebra Review/013. Matrices and Vectors.srt 14.94 KB
021.Bias vs. Variance/063. Regularization and Bias Variance.srt 14.92 KB
032.Density Estimation/090. Gaussian Distribution.srt 14.54 KB
015.Neural Networks/046. Model Representation I.srt 14.42 KB
013.Solving the Problem of Overfitting/042. Regularized Linear Regression.srt 14.18 KB
018.Backpropagation in Practice/054. Implementation Note Unrolling Parameters.srt 14.04 KB
011.Logistic Regression Model/037. Simplified Cost Function and Gradient Descent.srt 13.96 KB
040.Photo OCR/109. Problem Description and Pipeline.srt 13.88 KB
006.Multivariate Linear Regression/019. Multiple Features.srt 13.71 KB
005.Linear Algebra Review/016. Matrix Matrix Multiplication.srt 13.66 KB
004.Parameter Learning/012. Gradient Descent For Linear Regression.srt 13.4 KB
011.Logistic Regression Model/036. Cost Function.srt 13.37 KB
021.Bias vs. Variance/065. Deciding What to Do Next Revisited.srt 13.31 KB
030.Principal Component Analysis/084. Principal Component Analysis Problem Formulation.srt 13.05 KB
006.Multivariate Linear Regression/022. Gradient Descent in Practice II - Learning Rate.srt 12.48 KB
003.Model and Cost Function/008. Cost Function - Intuition I.srt 11.74 KB
020.Evaluating a Learning Algorithm/059. Deciding What to Try Next.srt 11.74 KB
005.Linear Algebra Review/017. Matrix Multiplication Properties.srt 11.49 KB
016.Applications/049. Examples and Intuitions II.srt 11.44 KB
010.Classification and Representation/033. Classification.srt 11.43 KB
005.Linear Algebra Review/014. Addition and Scalar Multiplication.srt 11.28 KB
033.Building an Anomaly Detection System/093. Anomaly Detection vs. Supervised Learning.srt 11.23 KB
021.Bias vs. Variance/062. Diagnosing Bias vs. Variance.srt 11.21 KB
002.Introduction/003. What is Machine Learning.srt 10.99 KB
020.Evaluating a Learning Algorithm/060. Evaluating a Hypothesis.srt 10.94 KB
003.Model and Cost Function/009. Cost Function - Intuition II.srt 10.79 KB
018.Backpropagation in Practice/056. Random Initialization.srt 10.35 KB
003.Model and Cost Function/007. Cost Function.srt 10.18 KB
010.Classification and Representation/034. Hypothesis Representation.srt 9.61 KB
029.Motivation/083. Motivation II Visualization.srt 9.59 KB
003.Model and Cost Function/006. Model Representation.srt 9.58 KB
002.Introduction/002. Welcome.srt 9.52 KB
028.Clustering/079. Optimization Objective.srt 9.25 KB
012.Multiclass Classification/039. Multiclass Classification One-vs-all.srt 9.24 KB
017.Cost Function and Backpropagation/051. Cost Function.srt 8.87 KB
007.Computing Parameters Analytically/025. Normal Equation Noninvertibility.srt 8.65 KB
016.Applications/048. Examples and Intuitions I.srt 8.51 KB
041.Conclusion/113. Summary and Thank You.srt 7.7 KB
038.Gradient Descent with Large Datasets/103. Learning With Large Datasets.srt 7.59 KB
038.Gradient Descent with Large Datasets/105. Mini-Batch Gradient Descent.srt 7.54 KB
016.Applications/050. Multiclass Classification.srt 7 KB
019.Application of Neural Networks/058. Autonomous Driving.srt 6.88 KB
006.Multivariate Linear Regression/020. Gradient Descent for Multiple Variables.srt 6.37 KB
031.Applying PCA/086. Reconstruction from Compressed Representation.srt 5.08 KB
028.Clustering/077. Unsupervised Learning Introduction.srt 5.01 KB
008.Submitting Programming Assignments/026. Working on and Submitting Programming Assignments.srt 4.26 KB
001.Welcome/001. Welcome to Machine Learning!.srt 2.39 KB
[FCS Forum].url 133 B
[FreeCourseSite.com].url 127 B
[CourseClub.NET].url 123 B
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