Machine Learning, Incl. Deep Learning, With R

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.
Requirements
Basic R Programming knowledge is helpful, but not required.
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. Don’t wait. See you in the course.

Overview

Section 1: Introduction

Lecture 1 Course Overview

Lecture 2 AI 101

Lecture 3 Machine Learning 101

Lecture 4 Models

Lecture 5 Teaser Overview

Lecture 6 Teaser Lab

Section 2: R Refresher

Lecture 7 R and RStudio Installation

Lecture 8 How to get the code

Lecture 9 Rmarkdown Lab

Lecture 10 Piping 101

Lecture 11 Data Manipulation Lab

Lecture 12 Data Reshaping 101

Lecture 13 Data Reshaping Lab

Lecture 14 Packages Preparation Lab

Section 3: ––- Regression, Model Preparation, and Regularization ––-

Lecture 15 Section Overview

Lecture 16 How to get the code

Section 4: Regression

Lecture 17 Regression Types 101

Lecture 18 Univariate Regression 101

Lecture 19 Univariate Regression Interactive

Lecture 20 Univariate Regression Lab

Lecture 21 Univariate Regression Exercise

Lecture 22 Univariate Regression Solution

Lecture 23 Polynomial Regression 101

Lecture 24 Polynomial Regression Lab

Lecture 25 Multivariate Regression 101

Lecture 26 Multivariate Regression Lab

Lecture 27 Multivariate Regression Exercise

Lecture 28 Multivariate Regression Solution

Section 5: Model Preparation and Evaluation

Lecture 29 Underfitting Overfitting 101

Lecture 30 Train / Validation / Test Split 101

Lecture 31 Train / Validation / Test Split Interactive

Lecture 32 Train / Validation / Test Split Lab

Lecture 33 Resampling Techniques 101

Lecture 34 Resampling Techniques Lab

Section 6: Regularization

Lecture 35 Regularization 101

Lecture 36 Regularization Lab

Section 7: ––- Classification ––-

Lecture 37 Classification Introduction

Lecture 38 How to get the code

Section 8: Classification Basics

Lecture 39 Confusion Matrix 101

Lecture 40 ROC Curve 101

Lecture 41 ROC Curve Interactive

Lecture 42 ROC Curve Lab Intro

Lecture 43 ROC Curve Lab 1/3 (Data Prep, Modeling)

Lecture 44 ROC Curve Lab 2/3 (Confusion Matrix and ROC)

Lecture 45 ROC Curve Lab 3/3 (ROC, AUC, Cost Function)

Section 9: Decision Trees

Lecture 46 Decision Trees 101

Lecture 47 Decision Trees Lab (Intro)

Lecture 48 Decision Trees Lab (Coding)

Lecture 49 Decision Trees Exercise

Section 10: Random Forests

Lecture 50 Random Forests 101

Lecture 51 Random Forests Interactive

Lecture 52 Random Forest Lab (Intro)

Lecture 53 Random Forest Lab (Coding 1/2)

Lecture 54 Random Forest Lab (Coding 2/2)

Lecture 55 Random Forest Exercise

Section 11: Logistic Regression

Lecture 56 Logistic Regression 101

Lecture 57 Logistic Regression Lab (Intro)

Lecture 58 Logistic Regression Lab (Coding 1/2)

Lecture 59 Logistic Regression Lab (Coding 2/2)

Lecture 60 Logistic Regression Exercise

Section 12: Support Vector Machines

Lecture 61 Support Vector Machines 101

Lecture 62 Support Vector Machines Lab (Intro)

Lecture 63 Support Vector Machines Lab (Coding 1/2)

Lecture 64 Support Vector Machines Lab (Coding 2/2)

Lecture 65 Support Vector Machines Exercise

Section 13: Ensemble Models

Lecture 66 Ensemble Models 101

Section 14: ––- Association Rules ––-

Lecture 67 Association Rules 101

Lecture 68 How to get the code

Section 15: Apriori

Lecture 69 Apriori 101

Lecture 70 Apriori Lab (Intro)

Lecture 71 Apriori Lab (Coding 1/2)

Lecture 72 Apriori Lab (Coding 2/2)

Lecture 73 Apriori Exercise

Lecture 74 Apriori Solution

Section 16: ––- Clustering ––-

Lecture 75 Clustering Overview

Lecture 76 How to get the code

Section 17: kmeans

Lecture 77 kmeans 101

Lecture 78 kmeans Lab

Lecture 79 kmeans Exercise

Lecture 80 kmeans Solution

Section 18: Hierarchical Clustering

Lecture 81 Hierarchical Clustering 101

Lecture 82 Hierarchical Clustering Interactive

Lecture 83 Hierarchical Clustering Lab

Section 19: Dbscan

Lecture 84 Dbscan 101

Lecture 85 Dbscan Lab

Section 20: ––- Dimensionality Reduction ––-

Lecture 86 Dimensionality Reduction Overview

Section 21: Principal Component Analysis (PCA)

Lecture 87 PCA 101

Lecture 88 PCA Lab

Lecture 89 PCA Exercise

Lecture 90 PCA Solution

Section 22: t-SNE

Lecture 91 t-SNE 101

Lecture 92 t-SNE Lab (Sphere)

Lecture 93 t-SNE Lab (Mnist)

Section 23: Factor Analysis

Lecture 94 Factor Analysis 101

Lecture 95 Factor Analysis Lab (Intro)

Lecture 96 Factor Analysis Lab (Coding 1/2)

Lecture 97 Factor Analysis Lab (Coding 2/2)

Lecture 98 Factor Analysis Exercise

Section 24: ––- Reinforcement Learning ––-

Lecture 99 Reinforcement Learning 101

Lecture 100 Upper Confidence Bound 101

Lecture 101 Upper Confidence Bound Interactive

Lecture 102 How to get the code

Lecture 103 Upper Confidence Bound Lab (Intro)

Lecture 104 Upper Confidence Bound Lab (Coding 1/2)

Lecture 105 Upper Confidence Bound Lab (Coding 2/2)

Section 25: ––- Deep Learning ––-

Lecture 106 Deep Learning General Overview

Lecture 107 Deep Learning Modeling 101

Lecture 108 Performance

Lecture 109 From Perceptron to Neural Networks

Lecture 110 Layer Types

Lecture 111 Activation Functions

Lecture 112 Loss Function

Lecture 113 Optimizer

Lecture 114 Deep Learning Frameworks

Lecture 115 How to get the code

Lecture 116 Python and Keras Installation

Section 26: Deep Learning Regression

Lecture 117 Multi-Target Regression Lab (Intro)

Lecture 118 Multi-Target Regression Lab (Coding 1/2)

Lecture 119 Multi-Target Regression Lab (Coding 2/2)

Section 27: Deep Learning Classification

Lecture 120 Binary Classification Lab (Intro)

Lecture 121 Binary Classification Lab (Coding 1/2)

Lecture 122 Binary Classification Lab (Coding 2/2)

Lecture 123 Multi-Label Classification Lab (Intro)

Lecture 124 Multi-Label Classification Lab (Coding 1/3)

Lecture 125 Multi-Label Classification Lab (Coding 2/3)

Lecture 126 Multi-Label Classification Lab (Coding 3/3)

Section 28: Convolutional Neural Networks

Lecture 127 Convolutional Neural Networks 101

Lecture 128 Convolutional Neural Networks Interactive

Lecture 129 Convolutional Neural Networks Lab (Intro)

Lecture 130 Convolutional Neural Networks Lab (Coding)

Lecture 131 Convolutional Neural Networks Exercise

Lecture 132 Semantic Segmentation 101

Lecture 133 Semantic Segmentation Lab (Intro)

Lecture 134 Semantic Segmentation Lab (Coding)

Section 29: Autoencoders

Lecture 135 Autoencoders 101

Lecture 136 Autoencoders Lab (Intro)

Lecture 137 Autoencoders Lab (Coding)

Section 30: Transfer Learning and Pretrained Models

Lecture 138 Transfer Learning and Pretrained Models 101

Lecture 139 Transfer Learning and Pretrained Models Lab (Introduction)

Lecture 140 Transfer Learning and Pretrained Models Lab (Coding)

Section 31: Recurrent Neural Networks

Lecture 141 Recurrent Neural Networks 101

Lecture 142 LSTM: Univariate, Multistep Timeseries Prediction (Intro)

Lecture 143 LSTM: Univariate, Multistep Timeseries Prediction (Coding)

Lecture 144 LSTM: Multivariate, Multistep Timeseries Prediction (Intro)

Lecture 145 LSTM: Multivariate, Multistep Timeseries Prediction (Coding)

Section 32: Bonus

Lecture 146 Congratulations and thank you

Lecture 147 Bonus lecture

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