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Machine Learning, Incl. Deep Learning, With R - Panter - 08.11.2022
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Machine Learning, Incl. Deep Learning, With R
Last updated 11/2019
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.27 GB | Duration: 15h 24m
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
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