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[FreeCoursesOnline.Me] Coursera - Machine Learning

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