Machine Learning, incl. Deep Learning, with R

سرفصل های دوره

Statistical Machine Learning Techniques, and Deep Learning with Keras, and much more. (All R code included)

1 - Introduction

1 - Course Overview

2 - AI 101

3 - Machine Learning 101

4 - Models

5 - Teaser Overview

6 - PCA-Teaser.zip

6 - PCA-Teaser-Final.html

6 - Teaser Lab

2 - R Refresher

7 - R and RStudio Installation

8 - How to get the code

9 - Rmarkdown Lab

10 - Piping 101

11 - Data Manipulation Lab

12 - Data Reshaping 101

13 - Data Reshaping Lab

14 - Packages Preparation Lab

3 - Regression Model Preparation and Regularization

15 - Section Overview.html

16 - How to get the code

4 - Regression

17 - Regression Types 101

18 - Univariate Regression 101

19 - Univariate Regression Interactive

20 - Univariate Regression Lab

21 - Univariate Regression Exercise

22 - Univariate Regression Solution

23 - Polynomial Regression 101

24 - Polynomial Regression Lab

25 - Multivariate Regression 101

26 - Multivariate Regression Lab

27 - Multivariate Regression Exercise

28 - Multivariate Regression Solution

5 - Model Preparation and Evaluation

29 - Underfitting Overfitting 101

30 - Train Validation Test Split 101

31 - Train Validation Test Split Interactive

32 - Train Validation Test Split Lab

33 - Resampling Techniques 101

34 - Resampling Techniques Lab

6 - Regularization

35 - Regularization 101

36 - Regularization Lab

7 - Classification

37 - Classification Introduction.html

38 - How to get the code

8 - Classification Basics

39 - Confusion Matrix 101

40 - ROC Curve 101

41 - ROC Curve Interactive

42 - ROC Curve Lab Intro

43 - ROC Curve Lab 13 Data Prep Modeling

44 - ROC Curve Lab 23 Confusion Matrix and ROC

45 - ROC Curve Lab 33 ROC AUC Cost Function

9 - Decision Trees

46 - Decision Trees 101

47 - Decision Trees Lab Intro

48 - Decision Trees Lab Coding

49 - Decision Trees Exercise

10 - Random Forests

50 - Random Forests 101

51 - Random Forests Interactive

52 - Random Forest Lab Intro

53 - Random Forest Lab Coding 12

54 - Random Forest Lab Coding 22

55 - Random Forest Exercise

11 - Logistic Regression

56 - Logistic Regression 101

57 - Logistic Regression Lab Intro

58 - Logistic Regression Lab Coding 12

59 - Logistic Regression Lab Coding 22

60 - Logistic Regression Exercise

12 - Support Vector Machines

61 - Support Vector Machines 101

62 - Support Vector Machines Lab Intro

63 - Support Vector Machines Lab Coding 12

64 - Support Vector Machines Lab Coding 22

65 - Support Vector Machines Exercise

13 - Ensemble Models

66 - Ensemble Models 101

14 - Association Rules

67 - Association Rules 101

68 - How to get the code

15 - Apriori

69 - Apriori 101

70 - Apriori Lab Intro

71 - Apriori Lab Coding 12

72 - Apriori Lab Coding 22

73 - Apriori Exercise

74 - Apriori Solution

16 - Clustering

75 - Clustering Overview

76 - How to get the code

17 - kmeans

77 - kmeans 101

78 - kmeans Lab

79 - kmeans Exercise

80 - kmeans Solution

18 - Hierarchical Clustering

81 - Hierarchical Clustering 101

82 - Hierarchical Clustering Interactive

83 - Hierarchical Clustering Lab

19 - Dbscan

84 - Dbscan 101

85 - Dbscan Lab

20 - Dimensionality Reduction

86 - Dimensionality Reduction Overview.html

21 - Principal Component Analysis PCA

87 - PCA 101

88 - PCA Lab

89 - PCA Exercise

90 - PCA Solution

22 - tSNE

91 - tSNE 101

92 - tSNE Lab Sphere

93 - tSNE Lab Mnist

23 - Factor Analysis

94 - Factor Analysis 101

95 - Factor Analysis Lab Intro

96 - Factor Analysis Lab Coding 12

97 - Factor Analysis Lab Coding 22

98 - Factor Analysis Exercise

24 - Reinforcement Learning

99 - Reinforcement Learning 101

100 - Upper Confidence Bound 101

101 - Upper Confidence Bound Interactive

102 - How to get the code

103 - Upper Confidence Bound Lab Intro

104 - Upper Confidence Bound Lab Coding 12

105 - Upper Confidence Bound Lab Coding 22

25 - Deep Learning

106 - Deep Learning General Overview

107 - Deep Learning Modeling 101

108 - Performance

109 - From Perceptron to Neural Networks

110 - Layer Types

111 - Activation Functions

112 - Loss Function

113 - Optimizer

114 - Deep Learning Frameworks

115 - How to get the code

116 - Python and Keras Installation

26 - Deep Learning Regression

117 - MultiTarget Regression Lab Intro

118 - MultiTarget Regression Lab Coding 12

119 - MultiTarget Regression Lab Coding 22

27 - Deep Learning Classification

120 - Binary Classification Lab Intro

121 - Binary Classification Lab Coding 12

122 - Binary Classification Lab Coding 22

123 - MultiLabel Classification Lab Intro

124 - MultiLabel Classification Lab Coding 13

125 - MultiLabel Classification Lab Coding 23

126 - MultiLabel Classification Lab Coding 33

28 - Convolutional Neural Networks

127 - Convolutional Neural Networks 101

128 - Convolutional Neural Networks Interactive

129 - Convolutional Neural Networks Lab Intro

130 - Convolutional Neural Networks Lab Coding

131 - Convolutional Neural Networks Exercise

132 - Semantic Segmentation 101

133 - Semantic Segmentation Lab Intro

134 - Semantic Segmentation Lab Coding

29 - Autoencoders

135 - Autoencoders 101

136 - Autoencoders Lab Intro

137 - Autoencoders Lab Coding

30 - Transfer Learning and Pretrained Models

138 - Transfer Learning and Pretrained Models 101

139 - Transfer Learning and Pretrained Models Lab Introduction

140 - Transfer Learning and Pretrained Models Lab Coding

31 - Recurrent Neural Networks

141 - Recurrent Neural Networks 101

142 - LSTM Univariate Multistep Timeseries Prediction Intro

143 - LSTM Univariate Multistep Timeseries Prediction Coding

144 - LSTM Multivariate Multistep Timeseries Prediction Intro

145 - LSTM Multivariate Multistep Timeseries Prediction Coding

32 - Bonus

146 - Congratulations and thank you.html

147 - Bonus lecture.html

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