FreeCoursesOnline Me Coursera Machine Learning | 1912B056A26877730EF548AFAC2BB75A9106F9DC

FreeCoursesOnline Me Coursera Machine Learning

Name	[FreeCoursesOnline.Me] Coursera - Machine Learning	DOWNLOAD Copy Link
Total Size	2.0 GB
Total Files	229
Last Seen	2024-11-14 23:47
Hash	1912B056A26877730EF548AFAC2BB75A9106F9DC

/001.Welcome/
001. Welcome to Machine Learning!.mp4	9.6 MB
001. Welcome to Machine Learning!.srt	2.4 KB
/002.Introduction/
002. Welcome.mp4	19.2 MB
002. Welcome.srt	9.7 KB
003. What is Machine Learning.mp4	12.0 MB
003. What is Machine Learning.srt	11.3 KB
004. Supervised Learning.mp4	17.5 MB
004. Supervised Learning.srt	19.3 KB
005. Unsupervised Learning.mp4	24.5 MB
005. Unsupervised Learning.srt	28.1 KB
/003.Model and Cost Function/
006. Model Representation.mp4	12.0 MB
006. Model Representation.srt	9.8 KB
007. Cost Function.mp4	12.1 MB
007. Cost Function.srt	10.4 KB
008. Cost Function - Intuition I.mp4	16.3 MB
008. Cost Function - Intuition I.srt	12.0 KB
009. Cost Function - Intuition II.mp4	17.8 MB
009. Cost Function - Intuition II.srt	11.0 KB
/004.Parameter Learning/
010. Gradient Descent.mp4	19.6 MB
010. Gradient Descent.srt	16.7 KB
011. Gradient Descent Intuition.mp4	17.4 MB
011. Gradient Descent Intuition.srt	16.3 KB
012. Gradient Descent For Linear Regression.mp4	17.2 MB
012. Gradient Descent For Linear Regression.srt	13.7 KB
/005.Linear Algebra Review/
013. Matrices and Vectors.mp4	12.5 MB
013. Matrices and Vectors.srt	15.3 KB
014. Addition and Scalar Multiplication.mp4	9.7 MB
014. Addition and Scalar Multiplication.srt	11.5 KB
015. Matrix Vector Multiplication.mp4	19.8 MB
015. Matrix Vector Multiplication.srt	23.4 KB
016. Matrix Matrix Multiplication.mp4	17.1 MB
016. Matrix Matrix Multiplication.srt	14.0 KB
017. Matrix Multiplication Properties.mp4	12.7 MB
017. Matrix Multiplication Properties.srt	11.8 KB
018. Inverse and Transpose.mp4	17.8 MB
018. Inverse and Transpose.srt	20.3 KB
/006.Multivariate Linear Regression/
019. Multiple Features.mp4	12.1 MB
019. Multiple Features.srt	14.0 KB
020. Gradient Descent for Multiple Variables.mp4	8.0 MB
020. Gradient Descent for Multiple Variables.srt	6.5 KB
021. Gradient Descent in Practice I - Feature Scaling.mp4	13.6 MB
021. Gradient Descent in Practice I - Feature Scaling.srt	16.4 KB
022. Gradient Descent in Practice II - Learning Rate.mp4	13.2 MB
022. Gradient Descent in Practice II - Learning Rate.srt	12.8 KB
023. Features and Polynomial Regression.mp4	12.1 MB
023. Features and Polynomial Regression.srt	15.3 KB
/007.Computing Parameters Analytically/
024. Normal Equation.mp4	24.8 MB
024. Normal Equation.srt	30.2 KB
025. Normal Equation Noninvertibility.mp4	9.2 MB
025. Normal Equation Noninvertibility.srt	8.9 KB
/008.Submitting Programming Assignments/
026. Working on and Submitting Programming Assignments.mp4	9.4 MB
026. Working on and Submitting Programming Assignments.srt	4.4 KB
/009.Octave Matlab Tutorial/
027. Basic Operations.mp4	26.1 MB
027. Basic Operations.srt	24.5 KB
028. Moving Data Around.mp4	31.0 MB
028. Moving Data Around.srt	27.6 KB
029. Computing on Data.mp4	20.8 MB
029. Computing on Data.srt	17.1 KB
030. Plotting Data.mp4	21.1 MB
030. Plotting Data.srt	16.7 KB
031. Control Statements for, while, if statement.mp4	25.0 MB
031. Control Statements for, while, if statement.srt	22.6 KB
032. Vectorization.mp4	23.3 MB
032. Vectorization.srt	17.7 KB
/010.Classification and Representation/
033. Classification.mp4	11.9 MB
033. Classification.srt	11.7 KB
034. Hypothesis Representation.mp4	11.7 MB
034. Hypothesis Representation.srt	9.8 KB
035. Decision Boundary.mp4	23.3 MB
035. Decision Boundary.srt	18.3 KB
/011.Logistic Regression Model/
036. Cost Function.mp4	16.6 MB
036. Cost Function.srt	13.7 KB
037. Simplified Cost Function and Gradient Descent.mp4	17.0 MB
037. Simplified Cost Function and Gradient Descent.srt	14.3 KB
038. Advanced Optimization.mp4	28.1 MB
038. Advanced Optimization.srt	26.9 KB
/012.Multiclass Classification/
039. Multiclass Classification One-vs-all.mp4	9.5 MB
039. Multiclass Classification One-vs-all.srt	9.5 KB
/013.Solving the Problem of Overfitting/
040. The Problem of Overfitting.mp4	15.7 MB
040. The Problem of Overfitting.srt	18.6 KB
041. Cost Function.mp4	16.3 MB
041. Cost Function.srt	19.1 KB
042. Regularized Linear Regression.mp4	16.4 MB
042. Regularized Linear Regression.srt	14.5 KB
043. Regularized Logistic Regression.mp4	17.6 MB
043. Regularized Logistic Regression.srt	16.6 KB
/014.Motivations/
044. Non-linear Hypotheses.mp4	15.5 MB
044. Non-linear Hypotheses.srt	18.4 KB
045. Neurons and the Brain.mp4	15.3 MB
045. Neurons and the Brain.srt	15.8 KB
/015.Neural Networks/
046. Model Representation I.mp4	18.9 MB
046. Model Representation I.srt	14.8 KB
047. Model Representation II.mp4	19.3 MB
047. Model Representation II.srt	21.6 KB
/016.Applications/
048. Examples and Intuitions I.mp4	10.6 MB
048. Examples and Intuitions I.srt	8.7 KB
049. Examples and Intuitions II.mp4	21.9 MB
049. Examples and Intuitions II.srt	11.7 KB
050. Multiclass Classification.mp4	7.3 MB
050. Multiclass Classification.srt	7.2 KB
/017.Cost Function and Backpropagation/
051. Cost Function.mp4	10.7 MB
051. Cost Function.srt	9.1 KB
052. Backpropagation Algorithm.mp4	20.0 MB
052. Backpropagation Algorithm.srt	22.0 KB
053. Backpropagation Intuition.mp4	23.3 MB
053. Backpropagation Intuition.srt	18.1 KB
/018.Backpropagation in Practice/
054. Implementation Note Unrolling Parameters.mp4	13.5 MB
054. Implementation Note Unrolling Parameters.srt	14.4 KB
055. Gradient Checking.mp4	19.2 MB
055. Gradient Checking.srt	17.4 KB
056. Random Initialization.mp4	10.3 MB
056. Random Initialization.srt	10.6 KB
057. Putting It Together.mp4	24.7 MB
057. Putting It Together.srt	26.8 KB
/019.Application of Neural Networks/
058. Autonomous Driving.mp4	29.7 MB
058. Autonomous Driving.srt	7.0 KB
/020.Evaluating a Learning Algorithm/
059. Deciding What to Try Next.mp4	9.8 MB
059. Deciding What to Try Next.srt	12.0 KB
060. Evaluating a Hypothesis.mp4	11.6 MB
060. Evaluating a Hypothesis.srt	11.2 KB
061. Model Selection and Train Validation Test Sets.mp4	20.0 MB
061. Model Selection and Train Validation Test Sets.srt	17.3 KB
/021.Bias vs. Variance/
062. Diagnosing Bias vs. Variance.mp4	12.8 MB
062. Diagnosing Bias vs. Variance.srt	11.5 KB
063. Regularization and Bias Variance.mp4	17.2 MB
063. Regularization and Bias Variance.srt	15.3 KB
064. Learning Curves.mp4	17.2 MB
064. Learning Curves.srt	23.9 KB
065. Deciding What to Do Next Revisited.mp4	12.0 MB
065. Deciding What to Do Next Revisited.srt	13.6 KB
/022.Building a Spam Classifier/
066. Prioritizing What to Work On.mp4	15.8 MB
066. Prioritizing What to Work On.srt	19.0 KB
067. Error Analysis.mp4	22.3 MB
067. Error Analysis.srt	19.8 KB
/023.Handling Skewed Data/
068. Error Metrics for Skewed Classes.mp4	18.8 MB
068. Error Metrics for Skewed Classes.srt	21.3 KB
069. Trading Off Precision and Recall.mp4	22.3 MB
069. Trading Off Precision and Recall.srt	20.1 KB
/024.Using Large Data Sets/
070. Data For Machine Learning.mp4	18.2 MB
070. Data For Machine Learning.srt	22.4 KB
/025.Large Margin Classification/
071. Optimization Objective.mp4	23.0 MB
071. Optimization Objective.srt	20.3 KB
072. Large Margin Intuition.mp4	15.9 MB
072. Large Margin Intuition.srt	20.6 KB
073. Mathematics Behind Large Margin Classification.mp4	29.9 MB
073. Mathematics Behind Large Margin Classification.srt	34.6 KB
/026.Kernels/
074. Kernels I.mp4	23.9 MB
074. Kernels I.srt	28.0 KB
075. Kernels II.mp4	23.7 MB
075. Kernels II.srt	29.6 KB
/027.SVMs in Practice/
076. Using An SVM.mp4	33.5 MB
076. Using An SVM.srt	42.1 KB
/028.Clustering/
077. Unsupervised Learning Introduction.mp4	5.4 MB
077. Unsupervised Learning Introduction.srt	5.1 KB
078. K-Means Algorithm.mp4	18.5 MB
078. K-Means Algorithm.srt	25.3 KB
079. Optimization Objective.mp4	11.4 MB
079. Optimization Objective.srt	9.5 KB
080. Random Initialization.mp4	11.7 MB
080. Random Initialization.srt	15.7 KB
081. Choosing the Number of Clusters.mp4	12.8 MB
081. Choosing the Number of Clusters.srt	17.3 KB
/029.Motivation/
082. Motivation I Data Compression.mp4	22.5 MB
082. Motivation I Data Compression.srt	19.4 KB
083. Motivation II Visualization.mp4	8.7 MB
083. Motivation II Visualization.srt	9.8 KB
/030.Principal Component Analysis/
084. Principal Component Analysis Problem Formulation.mp4	14.7 MB
084. Principal Component Analysis Problem Formulation.srt	13.4 KB
085. Principal Component Analysis Algorithm.mp4	25.5 MB
085. Principal Component Analysis Algorithm.srt	27.6 KB
/031.Applying PCA/
086. Reconstruction from Compressed Representation.mp4	7.5 MB
086. Reconstruction from Compressed Representation.srt	5.2 KB
087. Choosing the Number of Principal Components.mp4	16.4 MB
087. Choosing the Number of Principal Components.srt	20.4 KB
088. Advice for Applying PCA.mp4	20.7 MB
088. Advice for Applying PCA.srt	25.4 KB
/032.Density Estimation/
089. Problem Motivation.mp4	11.1 MB
089. Problem Motivation.srt	15.5 KB
090. Gaussian Distribution.mp4	15.9 MB
090. Gaussian Distribution.srt	14.9 KB
091. Algorithm.mp4	19.9 MB
091. Algorithm.srt	22.7 KB
/033.Building an Anomaly Detection System/
092. Developing and Evaluating an Anomaly Detection System.mp4	21.5 MB
092. Developing and Evaluating an Anomaly Detection System.srt	26.4 KB
093. Anomaly Detection vs. Supervised Learning.mp4	13.8 MB
093. Anomaly Detection vs. Supervised Learning.srt	11.5 KB
094. Choosing What Features to Use.mp4	20.0 MB
094. Choosing What Features to Use.srt	24.3 KB
/034.Multivariate Gaussian Distribution (Optional)/
095. Multivariate Gaussian Distribution.mp4	22.9 MB
095. Multivariate Gaussian Distribution.srt	26.5 KB
096. Anomaly Detection using the Multivariate Gaussian Distribution.mp4	23.5 MB
096. Anomaly Detection using the Multivariate Gaussian Distribution.srt	25.4 KB
/035.Predicting Movie Ratings/
097. Problem Formulation.mp4	17.2 MB
097. Problem Formulation.srt	16.3 KB
098. Content Based Recommendations.mp4	24.3 MB
098. Content Based Recommendations.srt	20.0 KB
/036.Collaborative Filtering/
099. Collaborative Filtering.mp4	16.3 MB
099. Collaborative Filtering.srt	19.5 KB
100. Collaborative Filtering Algorithm.mp4	15.4 MB
100. Collaborative Filtering Algorithm.srt	15.9 KB
/037.Low Rank Matrix Factorization/
101. Vectorization Low Rank Matrix Factorization.mp4	13.4 MB
101. Vectorization Low Rank Matrix Factorization.srt	15.7 KB
102. Implementational Detail Mean Normalization.mp4	13.5 MB
102. Implementational Detail Mean Normalization.srt	16.0 KB
/038.Gradient Descent with Large Datasets/
103. Learning With Large Datasets.mp4	9.0 MB
103. Learning With Large Datasets.srt	7.8 KB
104. Stochastic Gradient Descent.mp4	22.0 MB
104. Stochastic Gradient Descent.srt	18.0 KB
105. Mini-Batch Gradient Descent.mp4	10.2 MB
105. Mini-Batch Gradient Descent.srt	7.7 KB
106. Stochastic Gradient Descent Convergence.mp4	19.0 MB
106. Stochastic Gradient Descent Convergence.srt	16.0 KB
/039.Advanced Topics/
107. Online Learning.mp4	21.5 MB
107. Online Learning.srt	26.7 KB
108. Map Reduce and Data Parallelism.mp4	22.3 MB
108. Map Reduce and Data Parallelism.srt	27.9 KB
/040.Photo OCR/
109. Problem Description and Pipeline.mp4	10.9 MB
109. Problem Description and Pipeline.srt	14.2 KB
110. Sliding Windows.mp4	23.0 MB
110. Sliding Windows.srt	30.4 KB
111. Getting Lots of Data and Artificial Data.mp4	26.5 MB
111. Getting Lots of Data and Artificial Data.srt	34.0 KB
112. Ceiling Analysis What Part of the Pipeline to Work on Next.mp4	23.0 MB
112. Ceiling Analysis What Part of the Pipeline to Work on Next.srt	22.3 KB
/041.Conclusion/
113. Summary and Thank You.mp4	9.5 MB
113. Summary and Thank You.srt	7.9 KB
/
[FreeCoursesOnline.Me].url	0.1 KB
[FreeTutorials.Us].url	0.1 KB
[FTU Forum].url	0.3 KB
Total files 229