Machine Learning, incl. Deep Learning, with R

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Bert Gollnick

15:23:46

200 View

1 - Introduction

1 - Course Overview.mp4

02:55

2 - AI 101.mp4

05:06

3 - Machine Learning 101.mp4

07:09

4 - Models.mp4

05:33

5 - Teaser Overview.mp4

00:28

6 - PCA-Teaser.zip

6 - PCA-Teaser-Final.html

6 - Teaser Lab.mp4

13:22

2 - R Refresher

7 - R and RStudio Installation.mp4

09:35

8 - How to get the code.mp4

01:37

9 - Rmarkdown Lab.mp4

09:26

10 - Piping 101.mp4

02:35

11 - Data Manipulation Lab.mp4

10:32

12 - Data Reshaping 101.mp4

03:20

13 - Data Reshaping Lab.mp4

11:43

14 - Packages Preparation Lab.mp4

01:48

3 - Regression Model Preparation and Regularization

15 - Section Overview.html

16 - How to get the code.mp4

01:37

4 - Regression

17 - Regression Types 101.mp4

03:40

18 - Univariate Regression 101.mp4

05:48

19 - Univariate Regression Interactive.mp4

04:01

20 - Univariate Regression Lab.mp4

12:10

21 - Univariate Regression Exercise.mp4

02:20

22 - Univariate Regression Solution.mp4

07:51

23 - Polynomial Regression 101.mp4

02:12

24 - Polynomial Regression Lab.mp4

13:59

25 - Multivariate Regression 101.mp4

04:41

26 - Multivariate Regression Lab.mp4

14:09

27 - Multivariate Regression Exercise.mp4

02:15

28 - Multivariate Regression Solution.mp4

13:12

5 - Model Preparation and Evaluation

29 - Underfitting Overfitting 101.mp4

11:19

30 - Train Validation Test Split 101.mp4

02:56

31 - Train Validation Test Split Interactive.mp4

07:45

32 - Train Validation Test Split Lab.mp4

12:51

33 - Resampling Techniques 101.mp4

04:52

34 - Resampling Techniques Lab.mp4

18:06

6 - Regularization

35 - Regularization 101.mp4

05:57

36 - Regularization Lab.mp4

17:37

7 - Classification

37 - Classification Introduction.html

38 - How to get the code.mp4

01:37

8 - Classification Basics

39 - Confusion Matrix 101.mp4

06:16

40 - ROC Curve 101.mp4

07:11

41 - ROC Curve Interactive.mp4

06:28

42 - ROC Curve Lab Intro.mp4

01:54

43 - ROC Curve Lab 13 Data Prep Modeling.mp4

11:19

44 - ROC Curve Lab 23 Confusion Matrix and ROC.mp4

05:56

45 - ROC Curve Lab 33 ROC AUC Cost Function.mp4

12:07

9 - Decision Trees

46 - Decision Trees 101.mp4

05:54

47 - Decision Trees Lab Intro.mp4

01:31

48 - Decision Trees Lab Coding.mp4

14:37

49 - Decision Trees Exercise.mp4

01:47

10 - Random Forests

50 - Random Forests 101.mp4

02:55

51 - Random Forests Interactive.mp4

03:41

52 - Random Forest Lab Intro.mp4

01:52

53 - Random Forest Lab Coding 12.mp4

11:39

54 - Random Forest Lab Coding 22.mp4

08:58

55 - Random Forest Exercise.mp4

02:29

11 - Logistic Regression

56 - Logistic Regression 101.mp4

07:33

57 - Logistic Regression Lab Intro.mp4

00:59

58 - Logistic Regression Lab Coding 12.mp4

08:52

59 - Logistic Regression Lab Coding 22.mp4

06:59

60 - Logistic Regression Exercise.mp4

01:15

12 - Support Vector Machines

61 - Support Vector Machines 101.mp4

05:17

62 - Support Vector Machines Lab Intro.mp4

01:26

63 - Support Vector Machines Lab Coding 12.mp4

08:24

64 - Support Vector Machines Lab Coding 22.mp4

04:58

65 - Support Vector Machines Exercise.mp4

02:16

13 - Ensemble Models

66 - Ensemble Models 101.mp4

03:16

14 - Association Rules

67 - Association Rules 101.mp4

05:50

68 - How to get the code.mp4

01:37

15 - Apriori

69 - Apriori 101.mp4

08:12

70 - Apriori Lab Intro.mp4

01:56

71 - Apriori Lab Coding 12.mp4

07:33

72 - Apriori Lab Coding 22.mp4

10:52

73 - Apriori Exercise.mp4

02:22

74 - Apriori Solution.mp4

10:44

16 - Clustering

75 - Clustering Overview.mp4

02:51

76 - How to get the code.mp4

01:37

17 - kmeans

77 - kmeans 101.mp4

07:23

78 - kmeans Lab.mp4

15:36

79 - kmeans Exercise.mp4

03:17

80 - kmeans Solution.mp4

10:45

18 - Hierarchical Clustering

81 - Hierarchical Clustering 101.mp4

08:04

82 - Hierarchical Clustering Interactive.mp4

06:39

83 - Hierarchical Clustering Lab.mp4

18:38

19 - Dbscan

84 - Dbscan 101.mp4

04:49

85 - Dbscan Lab.mp4

13:53

20 - Dimensionality Reduction

86 - Dimensionality Reduction Overview.html

21 - Principal Component Analysis PCA

87 - PCA 101.mp4

08:41

88 - PCA Lab.mp4

14:45

89 - PCA Exercise.mp4

02:08

90 - PCA Solution.mp4

09:19

22 - tSNE

91 - tSNE 101.mp4

05:47

92 - tSNE Lab Sphere.mp4

06:23

93 - tSNE Lab Mnist.mp4

06:45

23 - Factor Analysis

94 - Factor Analysis 101.mp4

09:27

95 - Factor Analysis Lab Intro.mp4

01:37

96 - Factor Analysis Lab Coding 12.mp4

08:02

97 - Factor Analysis Lab Coding 22.mp4

08:19

98 - Factor Analysis Exercise.mp4

01:46

24 - Reinforcement Learning

99 - Reinforcement Learning 101.mp4

07:41

100 - Upper Confidence Bound 101.mp4

12:46

101 - Upper Confidence Bound Interactive.mp4

07:14

102 - How to get the code.mp4

01:37

103 - Upper Confidence Bound Lab Intro.mp4

01:58

104 - Upper Confidence Bound Lab Coding 12.mp4

14:22

105 - Upper Confidence Bound Lab Coding 22.mp4

06:15

25 - Deep Learning

106 - Deep Learning General Overview.mp4

03:41

107 - Deep Learning Modeling 101.mp4

03:33

108 - Performance.mp4

02:33

109 - From Perceptron to Neural Networks.mp4

03:46

110 - Layer Types.mp4

03:57

111 - Activation Functions.mp4

04:14

112 - Loss Function.mp4

03:33

113 - Optimizer.mp4

06:16

114 - Deep Learning Frameworks.mp4

02:23

115 - How to get the code.mp4

01:37

116 - Python and Keras Installation.mp4

07:00

26 - Deep Learning Regression

117 - MultiTarget Regression Lab Intro.mp4

01:31

118 - MultiTarget Regression Lab Coding 12.mp4

11:33

119 - MultiTarget Regression Lab Coding 22.mp4

09:21

27 - Deep Learning Classification

120 - Binary Classification Lab Intro.mp4

01:34

121 - Binary Classification Lab Coding 12.mp4

11:44

122 - Binary Classification Lab Coding 22.mp4

06:30

123 - MultiLabel Classification Lab Intro.mp4

02:50

124 - MultiLabel Classification Lab Coding 13.mp4

10:01

125 - MultiLabel Classification Lab Coding 23.mp4

11:30

126 - MultiLabel Classification Lab Coding 33.mp4

05:38

28 - Convolutional Neural Networks

127 - Convolutional Neural Networks 101.mp4

10:04

128 - Convolutional Neural Networks Interactive.mp4

03:44

129 - Convolutional Neural Networks Lab Intro.mp4

01:32

130 - Convolutional Neural Networks Lab Coding.mp4

18:46

131 - Convolutional Neural Networks Exercise.mp4

02:26

132 - Semantic Segmentation 101.mp4

07:42

133 - Semantic Segmentation Lab Intro.mp4

03:01

134 - Semantic Segmentation Lab Coding.mp4

03:01

29 - Autoencoders

135 - Autoencoders 101.mp4

02:39

136 - Autoencoders Lab Intro.mp4

01:42

137 - Autoencoders Lab Coding.mp4

10:58

30 - Transfer Learning and Pretrained Models

138 - Transfer Learning and Pretrained Models 101.mp4

04:52

139 - Transfer Learning and Pretrained Models Lab Introduction.mp4

01:45

140 - Transfer Learning and Pretrained Models Lab Coding.mp4

09:35

31 - Recurrent Neural Networks

141 - Recurrent Neural Networks 101.mp4

06:58

142 - LSTM Univariate Multistep Timeseries Prediction Intro.mp4

01:45

143 - LSTM Univariate Multistep Timeseries Prediction Coding.mp4

13:13

144 - LSTM Multivariate Multistep Timeseries Prediction Intro.mp4

01:38

145 - LSTM Multivariate Multistep Timeseries Prediction Coding.mp4

12:17

32 - Bonus

146 - Congratulations and thank you.html

147 - Bonus lecture.html

Description

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

What You'll Learn?

You will learn to build state-of-the-art Machine Learning models with R.
Deep Learning models with Keras for Regression and Classification tasks
Convolutional Neural Networks with Keras for image classification
Regression Models (e.g. univariate, polynomial, multivariate)
Classification Models (e.g. Confusion Matrix, ROC, Logistic Regression, Decision Trees, Random Forests, SVM, Ensemble Learning)
Autoencoders with Keras
Pretrained Models and Transfer Learning with Keras
Regularization Techniques
Recurrent Neural Networks, especially LSTM
Association Rules (e.g. Apriori)
Clustering techniques (e.g. kmeans, hierarchical clustering, dbscan)
Dimensionality Reduction techniques (e.g. Principal Component Analysis, Factor Analysis, t-SNE)
Reinforcement Learning techniques (e.g. Upper Confidence Bound)
You will know how to evaluate your model, what underfitting and overfitting is, why resampling techniques are important, and how you can split your dataset into parts (train/validation/test).
We will understand the theory behind deep neural networks.
We will understand and implement convolutional neural networks - the most powerful technique for image recognition.

Who is this for?

R beginners and professionals with interest in Machine Learning and/or Deep Learning

What You Need to Know?

Basic R Programming knowledge is helpful, but not required.

More details

Description
Did you ever wonder how machines "learn" - in this course you will find out.
We will cover all fields of Machine Learning: Regression and Classification techniques, Clustering, Association Rules, Reinforcement Learning, and, possibly most importantly, Deep Learning for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, ...
For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions. Here, you will understand how the algorithm works. Then we implement it together in lab sessions. We develop code, before I encourage you to work on exercise on your own, before you watch my solution examples. With this knowledge you can clearly identify a problem at hand and develop a plan of attack to solve it.
You will understand the advantages and disadvantages of different models and when to use which one. Furthermore, you will know how to take your knowledge into the real world.
You will get access to an interactive learning platform that will help you to understand the concepts much better.
In this course code will never come out of thin air via copy/paste. We will develop every important line of code together and I will tell you why and how we implement it.
Take a look at some sample lectures. Or visit some of my interactive learning boards. Furthermore, there is a 30 day money back warranty, so there is no risk for you taking the course right now. Donâ€™t wait. See you in the course.
Who this course is for:
R beginners and professionals with interest in Machine Learning and/or Deep Learning

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I am a hands-on Data Scientist with a lot of domain knowledge on Renewable Energies, especially Wind Energy.Currently I work for a leading manufacturer of wind turbines. I provide trainings on Data Science and Machine Learning with R and Python since many years.I studied Aeronautics, and Economics. My main interests are Machine Learning, Data Science, and Blockchain.

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