[FreeCoursesOnline.Me] Coursera - Machine Learning

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Last Access Time	April 10, 2025, 9:05 a.m.
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Keywords	FreeCoursesOnline Me Coursera - Machine Learning
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027.SVMs in Practice/076. Using An SVM.mp4 32.0 MB
009.Octave Matlab Tutorial/028. Moving Data Around.mp4 29.5 MB
025.Large Margin Classification/073. Mathematics Behind Large Margin Classification.mp4 28.5 MB
019.Application of Neural Networks/058. Autonomous Driving.mp4 28.3 MB
011.Logistic Regression Model/038. Advanced Optimization.mp4 26.8 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 24.3 MB
009.Octave Matlab Tutorial/031. Control Statements for, while, if statement.mp4 23.9 MB
007.Computing Parameters Analytically/024. Normal Equation.mp4 23.6 MB
018.Backpropagation in Practice/057. Putting It Together.mp4 23.5 MB
002.Introduction/005. Unsupervised Learning.mp4 23.3 MB
035.Predicting Movie Ratings/098. Content Based Recommendations.mp4 23.2 MB
026.Kernels/074. Kernels I.mp4 22.8 MB
026.Kernels/075. Kernels II.mp4 22.6 MB
034.Multivariate Gaussian Distribution (Optional)/096. Anomaly Detection using the Multivariate Gaussian Distribution.mp4 22.4 MB
009.Octave Matlab Tutorial/032. Vectorization.mp4 22.3 MB
017.Cost Function and Backpropagation/053. Backpropagation Intuition.mp4 22.2 MB
010.Classification and Representation/035. Decision Boundary.mp4 22.2 MB
040.Photo OCR/110. Sliding Windows.mp4 21.9 MB
040.Photo OCR/112. Ceiling Analysis What Part of the Pipeline to Work on Next.mp4 21.9 MB
025.Large Margin Classification/071. Optimization Objective.mp4 21.9 MB
034.Multivariate Gaussian Distribution (Optional)/095. Multivariate Gaussian Distribution.mp4 21.9 MB
029.Motivation/082. Motivation I Data Compression.mp4 21.5 MB
023.Handling Skewed Data/069. Trading Off Precision and Recall.mp4 21.3 MB
022.Building a Spam Classifier/067. Error Analysis.mp4 21.3 MB
039.Advanced Topics/108. Map Reduce and Data Parallelism.mp4 21.2 MB
038.Gradient Descent with Large Datasets/104. Stochastic Gradient Descent.mp4 21.0 MB
016.Applications/049. Examples and Intuitions II.mp4 20.9 MB
033.Building an Anomaly Detection System/092. Developing and Evaluating an Anomaly Detection System.mp4 20.5 MB
039.Advanced Topics/107. Online Learning.mp4 20.5 MB
009.Octave Matlab Tutorial/030. Plotting Data.mp4 20.1 MB
009.Octave Matlab Tutorial/029. Computing on Data.mp4 19.8 MB
031.Applying PCA/088. Advice for Applying PCA.mp4 19.7 MB
033.Building an Anomaly Detection System/094. Choosing What Features to Use.mp4 19.1 MB
017.Cost Function and Backpropagation/052. Backpropagation Algorithm.mp4 19.1 MB
020.Evaluating a Learning Algorithm/061. Model Selection and Train Validation Test Sets.mp4 19.0 MB
032.Density Estimation/091. Algorithm.mp4 18.9 MB
005.Linear Algebra Review/015. Matrix Vector Multiplication.mp4 18.9 MB
004.Parameter Learning/010. Gradient Descent.mp4 18.7 MB
015.Neural Networks/047. Model Representation II.mp4 18.4 MB
018.Backpropagation in Practice/055. Gradient Checking.mp4 18.4 MB
002.Introduction/002. Welcome.mp4 18.3 MB
038.Gradient Descent with Large Datasets/106. Stochastic Gradient Descent Convergence.mp4 18.1 MB
015.Neural Networks/046. Model Representation I.mp4 18.0 MB
023.Handling Skewed Data/068. Error Metrics for Skewed Classes.mp4 17.9 MB
028.Clustering/078. K-Means Algorithm.mp4 17.7 MB
024.Using Large Data Sets/070. Data For Machine Learning.mp4 17.3 MB
005.Linear Algebra Review/018. Inverse and Transpose.mp4 17.0 MB
003.Model and Cost Function/009. Cost Function - Intuition II.mp4 17.0 MB
013.Solving the Problem of Overfitting/043. Regularized Logistic Regression.mp4 16.8 MB
002.Introduction/004. Supervised Learning.mp4 16.7 MB
004.Parameter Learning/011. Gradient Descent Intuition.mp4 16.6 MB
004.Parameter Learning/012. Gradient Descent For Linear Regression.mp4 16.4 MB
035.Predicting Movie Ratings/097. Problem Formulation.mp4 16.4 MB
021.Bias vs. Variance/064. Learning Curves.mp4 16.4 MB
021.Bias vs. Variance/063. Regularization and Bias Variance.mp4 16.4 MB
005.Linear Algebra Review/016. Matrix Matrix Multiplication.mp4 16.3 MB
011.Logistic Regression Model/037. Simplified Cost Function and Gradient Descent.mp4 16.3 MB
011.Logistic Regression Model/036. Cost Function.mp4 15.8 MB
031.Applying PCA/087. Choosing the Number of Principal Components.mp4 15.6 MB
013.Solving the Problem of Overfitting/042. Regularized Linear Regression.mp4 15.6 MB
003.Model and Cost Function/008. Cost Function - Intuition I.mp4 15.5 MB
036.Collaborative Filtering/099. Collaborative Filtering.mp4 15.5 MB
013.Solving the Problem of Overfitting/041. Cost Function.mp4 15.5 MB
025.Large Margin Classification/072. Large Margin Intuition.mp4 15.2 MB
032.Density Estimation/090. Gaussian Distribution.mp4 15.2 MB
022.Building a Spam Classifier/066. Prioritizing What to Work On.mp4 15.1 MB
013.Solving the Problem of Overfitting/040. The Problem of Overfitting.mp4 14.9 MB
014.Motivations/044. Non-linear Hypotheses.mp4 14.7 MB
036.Collaborative Filtering/100. Collaborative Filtering Algorithm.mp4 14.7 MB
014.Motivations/045. Neurons and the Brain.mp4 14.6 MB
030.Principal Component Analysis/084. Principal Component Analysis Problem Formulation.mp4 14.0 MB
033.Building an Anomaly Detection System/093. Anomaly Detection vs. Supervised Learning.mp4 13.1 MB
006.Multivariate Linear Regression/021. Gradient Descent in Practice I - Feature Scaling.mp4 12.9 MB
018.Backpropagation in Practice/054. Implementation Note Unrolling Parameters.mp4 12.9 MB
037.Low Rank Matrix Factorization/102. Implementational Detail Mean Normalization.mp4 12.9 MB
037.Low Rank Matrix Factorization/101. Vectorization Low Rank Matrix Factorization.mp4 12.8 MB
006.Multivariate Linear Regression/022. Gradient Descent in Practice II - Learning Rate.mp4 12.6 MB
028.Clustering/081. Choosing the Number of Clusters.mp4 12.2 MB
021.Bias vs. Variance/062. Diagnosing Bias vs. Variance.mp4 12.2 MB
005.Linear Algebra Review/017. Matrix Multiplication Properties.mp4 12.2 MB
005.Linear Algebra Review/013. Matrices and Vectors.mp4 11.9 MB
006.Multivariate Linear Regression/019. Multiple Features.mp4 11.6 MB
006.Multivariate Linear Regression/023. Features and Polynomial Regression.mp4 11.5 MB
003.Model and Cost Function/007. Cost Function.mp4 11.5 MB
021.Bias vs. Variance/065. Deciding What to Do Next Revisited.mp4 11.4 MB
003.Model and Cost Function/006. Model Representation.mp4 11.4 MB
002.Introduction/003. What is Machine Learning.mp4 11.4 MB
010.Classification and Representation/033. Classification.mp4 11.3 MB
010.Classification and Representation/034. Hypothesis Representation.mp4 11.2 MB
028.Clustering/080. Random Initialization.mp4 11.1 MB
020.Evaluating a Learning Algorithm/060. Evaluating a Hypothesis.mp4 11.0 MB
028.Clustering/079. Optimization Objective.mp4 10.9 MB
032.Density Estimation/089. Problem Motivation.mp4 10.6 MB
040.Photo OCR/109. Problem Description and Pipeline.mp4 10.4 MB
017.Cost Function and Backpropagation/051. Cost Function.mp4 10.2 MB
016.Applications/048. Examples and Intuitions I.mp4 10.1 MB
018.Backpropagation in Practice/056. Random Initialization.mp4 9.8 MB
038.Gradient Descent with Large Datasets/105. Mini-Batch Gradient Descent.mp4 9.8 MB
020.Evaluating a Learning Algorithm/059. Deciding What to Try Next.mp4 9.4 MB
005.Linear Algebra Review/014. Addition and Scalar Multiplication.mp4 9.3 MB
001.Welcome/001. Welcome to Machine Learning!.mp4 9.1 MB
041.Conclusion/113. Summary and Thank You.mp4 9.1 MB
012.Multiclass Classification/039. Multiclass Classification One-vs-all.mp4 9.1 MB
008.Submitting Programming Assignments/026. Working on and Submitting Programming Assignments.mp4 9.0 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 8.5 MB
029.Motivation/083. Motivation II Visualization.mp4 8.3 MB
006.Multivariate Linear Regression/020. Gradient Descent for Multiple Variables.mp4 7.6 MB
031.Applying PCA/086. Reconstruction from Compressed Representation.mp4 7.2 MB
016.Applications/050. Multiclass Classification.mp4 7.0 MB
028.Clustering/077. Unsupervised Learning Introduction.mp4 5.2 MB
027.SVMs in Practice/076. Using An SVM.srt 41.1 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 33.2 KB
040.Photo OCR/110. Sliding Windows.srt 29.7 KB
007.Computing Parameters Analytically/024. Normal Equation.srt 29.5 KB
026.Kernels/075. Kernels II.srt 29.0 KB
002.Introduction/005. Unsupervised Learning.srt 27.5 KB
026.Kernels/074. Kernels I.srt 27.4 KB
039.Advanced Topics/108. Map Reduce and Data Parallelism.srt 27.2 KB
009.Octave Matlab Tutorial/028. Moving Data Around.srt 26.9 KB
030.Principal Component Analysis/085. Principal Component Analysis Algorithm.srt 26.9 KB
011.Logistic Regression Model/038. Advanced Optimization.srt 26.3 KB
018.Backpropagation in Practice/057. Putting It Together.srt 26.1 KB
039.Advanced Topics/107. Online Learning.srt 26.1 KB
034.Multivariate Gaussian Distribution (Optional)/095. Multivariate Gaussian Distribution.srt 25.8 KB
033.Building an Anomaly Detection System/092. Developing and Evaluating an Anomaly Detection System.srt 25.8 KB
034.Multivariate Gaussian Distribution (Optional)/096. Anomaly Detection using the Multivariate Gaussian Distribution.srt 24.8 KB
031.Applying PCA/088. Advice for Applying PCA.srt 24.8 KB
028.Clustering/078. K-Means Algorithm.srt 24.7 KB
009.Octave Matlab Tutorial/027. Basic Operations.srt 23.9 KB
033.Building an Anomaly Detection System/094. Choosing What Features to Use.srt 23.7 KB
021.Bias vs. Variance/064. Learning Curves.srt 23.3 KB
005.Linear Algebra Review/015. Matrix Vector Multiplication.srt 22.8 KB
032.Density Estimation/091. Algorithm.srt 22.1 KB
009.Octave Matlab Tutorial/031. Control Statements for, while, if statement.srt 22.0 KB
024.Using Large Data Sets/070. Data For Machine Learning.srt 21.8 KB
040.Photo OCR/112. Ceiling Analysis What Part of the Pipeline to Work on Next.srt 21.8 KB
017.Cost Function and Backpropagation/052. Backpropagation Algorithm.srt 21.5 KB
015.Neural Networks/047. Model Representation II.srt 21.1 KB
023.Handling Skewed Data/068. Error Metrics for Skewed Classes.srt 20.8 KB
025.Large Margin Classification/072. Large Margin Intuition.srt 20.1 KB
031.Applying PCA/087. Choosing the Number of Principal Components.srt 19.9 KB
005.Linear Algebra Review/018. Inverse and Transpose.srt 19.9 KB
025.Large Margin Classification/071. Optimization Objective.srt 19.8 KB
023.Handling Skewed Data/069. Trading Off Precision and Recall.srt 19.7 KB
035.Predicting Movie Ratings/098. Content Based Recommendations.srt 19.5 KB
022.Building a Spam Classifier/067. Error Analysis.srt 19.3 KB
036.Collaborative Filtering/099. Collaborative Filtering.srt 19.1 KB
029.Motivation/082. Motivation I Data Compression.srt 19.0 KB
002.Introduction/004. Supervised Learning.srt 18.9 KB
013.Solving the Problem of Overfitting/041. Cost Function.srt 18.6 KB
022.Building a Spam Classifier/066. Prioritizing What to Work On.srt 18.5 KB
013.Solving the Problem of Overfitting/040. The Problem of Overfitting.srt 18.2 KB
014.Motivations/044. Non-linear Hypotheses.srt 18.0 KB
010.Classification and Representation/035. Decision Boundary.srt 17.9 KB
017.Cost Function and Backpropagation/053. Backpropagation Intuition.srt 17.7 KB
038.Gradient Descent with Large Datasets/104. Stochastic Gradient Descent.srt 17.6 KB
009.Octave Matlab Tutorial/032. Vectorization.srt 17.3 KB
018.Backpropagation in Practice/055. Gradient Checking.srt 17.0 KB
020.Evaluating a Learning Algorithm/061. Model Selection and Train Validation Test Sets.srt 16.9 KB
028.Clustering/081. Choosing the Number of Clusters.srt 16.9 KB
009.Octave Matlab Tutorial/029. Computing on Data.srt 16.7 KB
009.Octave Matlab Tutorial/030. Plotting Data.srt 16.3 KB
004.Parameter Learning/010. Gradient Descent.srt 16.3 KB
013.Solving the Problem of Overfitting/043. Regularized Logistic Regression.srt 16.2 KB
006.Multivariate Linear Regression/021. Gradient Descent in Practice I - Feature Scaling.srt 16.0 KB
004.Parameter Learning/011. Gradient Descent Intuition.srt 15.9 KB
035.Predicting Movie Ratings/097. Problem Formulation.srt 15.9 KB
038.Gradient Descent with Large Datasets/106. Stochastic Gradient Descent Convergence.srt 15.7 KB
037.Low Rank Matrix Factorization/102. Implementational Detail Mean Normalization.srt 15.6 KB
036.Collaborative Filtering/100. Collaborative Filtering Algorithm.srt 15.6 KB
014.Motivations/045. Neurons and the Brain.srt 15.5 KB
037.Low Rank Matrix Factorization/101. Vectorization Low Rank Matrix Factorization.srt 15.4 KB
028.Clustering/080. Random Initialization.srt 15.3 KB
032.Density Estimation/089. Problem Motivation.srt 15.1 KB
006.Multivariate Linear Regression/023. Features and Polynomial Regression.srt 15.0 KB
005.Linear Algebra Review/013. Matrices and Vectors.srt 14.9 KB
021.Bias vs. Variance/063. Regularization and Bias Variance.srt 14.9 KB
032.Density Estimation/090. Gaussian Distribution.srt 14.5 KB
015.Neural Networks/046. Model Representation I.srt 14.4 KB
013.Solving the Problem of Overfitting/042. Regularized Linear Regression.srt 14.2 KB
018.Backpropagation in Practice/054. Implementation Note Unrolling Parameters.srt 14.0 KB
011.Logistic Regression Model/037. Simplified Cost Function and Gradient Descent.srt 14.0 KB
040.Photo OCR/109. Problem Description and Pipeline.srt 13.9 KB
006.Multivariate Linear Regression/019. Multiple Features.srt 13.7 KB
005.Linear Algebra Review/016. Matrix Matrix Multiplication.srt 13.7 KB
004.Parameter Learning/012. Gradient Descent For Linear Regression.srt 13.4 KB
011.Logistic Regression Model/036. Cost Function.srt 13.4 KB
021.Bias vs. Variance/065. Deciding What to Do Next Revisited.srt 13.3 KB
030.Principal Component Analysis/084. Principal Component Analysis Problem Formulation.srt 13.0 KB
006.Multivariate Linear Regression/022. Gradient Descent in Practice II - Learning Rate.srt 12.5 KB
003.Model and Cost Function/008. Cost Function - Intuition I.srt 11.7 KB
020.Evaluating a Learning Algorithm/059. Deciding What to Try Next.srt 11.7 KB
005.Linear Algebra Review/017. Matrix Multiplication Properties.srt 11.5 KB
016.Applications/049. Examples and Intuitions II.srt 11.4 KB
010.Classification and Representation/033. Classification.srt 11.4 KB
005.Linear Algebra Review/014. Addition and Scalar Multiplication.srt 11.3 KB
033.Building an Anomaly Detection System/093. Anomaly Detection vs. Supervised Learning.srt 11.2 KB
021.Bias vs. Variance/062. Diagnosing Bias vs. Variance.srt 11.2 KB
002.Introduction/003. What is Machine Learning.srt 11.0 KB
020.Evaluating a Learning Algorithm/060. Evaluating a Hypothesis.srt 10.9 KB
003.Model and Cost Function/009. Cost Function - Intuition II.srt 10.8 KB
018.Backpropagation in Practice/056. Random Initialization.srt 10.3 KB
003.Model and Cost Function/007. Cost Function.srt 10.2 KB
010.Classification and Representation/034. Hypothesis Representation.srt 9.6 KB
029.Motivation/083. Motivation II Visualization.srt 9.6 KB
003.Model and Cost Function/006. Model Representation.srt 9.6 KB
002.Introduction/002. Welcome.srt 9.5 KB
028.Clustering/079. Optimization Objective.srt 9.3 KB
012.Multiclass Classification/039. Multiclass Classification One-vs-all.srt 9.2 KB
017.Cost Function and Backpropagation/051. Cost Function.srt 8.9 KB
007.Computing Parameters Analytically/025. Normal Equation Noninvertibility.srt 8.6 KB
016.Applications/048. Examples and Intuitions I.srt 8.5 KB
041.Conclusion/113. Summary and Thank You.srt 7.7 KB
038.Gradient Descent with Large Datasets/103. Learning With Large Datasets.srt 7.6 KB
038.Gradient Descent with Large Datasets/105. Mini-Batch Gradient Descent.srt 7.5 KB
016.Applications/050. Multiclass Classification.srt 7.0 KB
019.Application of Neural Networks/058. Autonomous Driving.srt 6.9 KB
006.Multivariate Linear Regression/020. Gradient Descent for Multiple Variables.srt 6.4 KB
031.Applying PCA/086. Reconstruction from Compressed Representation.srt 5.1 KB
028.Clustering/077. Unsupervised Learning Introduction.srt 5.0 KB
008.Submitting Programming Assignments/026. Working on and Submitting Programming Assignments.srt 4.3 KB
001.Welcome/001. Welcome to Machine Learning!.srt 2.4 KB
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[FreeCoursesOnline.Me] Coursera - Machine Learning

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