Machine Learning with R Course

Machine Learning with R

Understand machine learning models and how to implement them in R from an expert in Data Science. (All code included)

Created by : Rated :4.5 44488 views

Last Updated: 2024-11-19 09:11:11 English Course Type: Self Learning

You will learn to build state-of-the-art Machine Learning models with R.
We will implement Deep Learning models with Keras for Regression and Classification tasks.
Regression Models (e.g. univariate, polynomial, multivariate)
Regularization Techniques
Classification Models (e.g. Confusion Matrix, ROC, Logistic Regression, Decision Trees, Random Forests, SVM, Ensemble Learning)
Association Rules (e.g. Apriori)
Clustering techniques (e.g. kmeans, hierarchical clustering, dbscan)
Dimensionality Reduction techniques (e.g. Principal Component Analysis, Factor Analysis)
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.

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, finally, Deep Learning.

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.

Basic R knowledge

R beginners and professionals with interest in Machine Learning

Section 1 : Introduction 6 Lectures 00:34:37
Lecture 1 :
Course Overview Preview
Lecture 2 :
AI 101
Lecture 3 :
Machine Learning 101
Lecture 4 :
Models
Lecture 5 :
Teaser Overview
Lecture 6 :
Teaser Lab
Section 2 : R Refresher 7 Lectures 00:41:08
Lecture 1 :
How to get the code
Lecture 2 :
Rmarkdown Lab
Lecture 3 :
Piping 101
Lecture 4 :
Data Manipulation Lab
Lecture 5 :
Data Reshaping 101
Lecture 6 :
Data Reshaping Lab
Lecture 7 :
Packages Preparation Lab
Section 3 : ----- Regression, Model Preparation, and Regularization ----- 2 Lectures 00:01:38
Lecture 1 :
Section Overview
Lecture 2 :
How to get the code
Section 4 : Regression 13 Lectures 01:26:29
Lecture 1 :
Regression Types 101
Lecture 2 :
Univariate Regression 101
Lecture 3 :
Univariate Regression Interactive
Lecture 4 :
Univariate Regression Lab
Lecture 5 :
Univariate Regression Exercise
Lecture 6 :
Univariate Regression Solution
Lecture 7 :
Polynomial Regression 101
Lecture 8 :
Polynomial Regression Lab
Lecture 9 :
Multivariate Regression 101
Lecture 10 :
Multivariate Regression Lab
Lecture 11 :
Multivariate Regression Exercise
Lecture 12 :
Multivariate Regression Solution
Lecture 13 :
Regression Quiz
This quiz tests your knowledge on regression.
Section 5 : Model Preparation and Evaluation 6 Lectures 00:57:55
Lecture 1 :
Underfitting Overfitting 101
Lecture 2 :
Train / Validation / Test Split 101
Lecture 3 :
Train / Validation / Test Split Interactive
Lecture 4 :
Train / Validation / Test Split Lab
Lecture 5 :
Resampling Techniques 101
Lecture 6 :
Resampling Techniques Lab
Section 6 : Regularization 2 Lectures 00:23:36
Lecture 1 :
Regularization 101
Lecture 2 :
Regularization Lab
Section 7 : ----- Classification ----- 2 Lectures 00:01:38
Lecture 1 :
Classification Introduction
Lecture 2 :
How to get the code
Section 8 : Classification Basics 7 Lectures 00:51:17
Lecture 1 :
Confusion Matrix 101
Lecture 2 :
ROC Curve 101
Lecture 3 :
ROC Curve Interactive
Lecture 4 :
ROC Curve Lab (Intro)
Lecture 5 :
ROC Curve Lab (Coding 1/3)
Lecture 6 :
ROC Curve Lab (Coding 2/3)
Lecture 7 :
ROC Curve Lab (Coding 3/3)
Section 9 : Decision Trees 3 Lectures 00:22:04
Lecture 1 :
Decision Trees 101
Lecture 2 :
Decision Trees Lab (Intro)
Lecture 3 :
Decision Trees Lab (Coding)
Section 10 : Random Forests 5 Lectures 00:29:10
Lecture 1 :
Random Forests 101
Lecture 2 :
Random Forests Interactive
Lecture 3 :
Random Forests Lab (Intro)
Lecture 4 :
Random Forests Lab (Coding 1/2)
Lecture 5 :
Random Forests Lab (Coding 2/2)
Section 11 : Logistic Regression 4 Lectures 00:24:27
Lecture 1 :
Logistic Regression 101
Lecture 2 :
Logistic Regression Lab (Intro)
Lecture 3 :
Logistic Regression Lab (Coding 1/2)
Lecture 4 :
Logistic Regression Lab (Coding 2/2)
Section 12 : Support Vector Machines 4 Lectures 00:20:08
Lecture 1 :
Support Vector Machines 101
Lecture 2 :
Support Vector Machines Lab (Intro)
Lecture 3 :
Support Vector Machines Lab (Coding 1/2)
Lecture 4 :
Support Vector Machines Lab (Coding 2/2)
Section 13 : Ensemble Models 2 Lectures 00:03:17
Lecture 1 :
Ensemble Models 101
Lecture 2 :
Classification Quiz
This quiz tests your knowledge on classification techniques.
Section 14 : ----- Association Rules ----- 2 Lectures 00:07:29
Lecture 1 :
Association Rules 101
Lecture 2 :
How to get the code
Section 15 : Apriori 6 Lectures 00:41:44
Lecture 1 :
Apriori 101
Lecture 2 :
Apriori Lab (Intro)
Lecture 3 :
Apriori Lab (Coding 1/2)
Lecture 4 :
Apriori Lab (Coding 2/2)
Lecture 5 :
Apriori Exercise
Lecture 6 :
Apriori Solution
Section 16 : ----- Clustering ----- 2 Lectures 00:04:30
Lecture 1 :
Clustering Overview
Lecture 2 :
How to get the code
Section 17 : kmeans 4 Lectures 00:37:05
Lecture 1 :
kmeans 101
Lecture 2 :
kmeans Lab
Lecture 3 :
kmeans Exercise
Lecture 4 :
kmeans Solution
Section 18 : Hierarchical Clustering 3 Lectures 00:33:22
Lecture 1 :
Hierarchical Clustering 101
Lecture 2 :
Hierarchical Clustering Interactive
Lecture 3 :
Hierarchical Clustering Lab
Section 19 : Dbscan 3 Lectures 00:18:44
Lecture 1 :
Dbscan 101
Lecture 2 :
Dbscan Lab
Lecture 3 :
Clustering Quiz
This quiz tests your knowledge on clustering.
Section 20 : ----- Dimensionality Reduction ----- 1 Lectures
Lecture 1 :
Dimensionality Reduction Overview
Section 21 : Principal Component Analysis (PCA) 4 Lectures 00:34:57
Lecture 1 :
PCA 101
Lecture 2 :
PCA Lab
Lecture 3 :
PCA Exercise
Lecture 4 :
PCA Solution
Section 22 : Factor Analysis 5 Lectures 00:27:29
Lecture 1 :
Factor Analysis 101
Lecture 2 :
Factor Analysis Lab (Intro)
Lecture 3 :
Factor Analysis Lab (Coding 1/2)
Lecture 4 :
Factor Analysis Lab (Coding 2/2)
Lecture 5 :
Dimensionality Reduction Quiz
This quiz tests your knowledge on dimensionality reduction.
Section 23 : ----- Reinforcement Learning ----- 6 Lectures 00:44:16
Lecture 1 :
Upper Confidence Bound 101
Lecture 2 :
Upper Confidence Bound Interactive
Lecture 3 :
How to get the code
Lecture 4 :
Upper Confidence Bound Lab (Intro)
Lecture 5 :
Upper Confidence Bound Lab (Coding 1/2)
Lecture 6 :
Upper Confidence Bound Lab (Coding 2/2)
Section 24 : ----- Deep Learning ----- 8 Lectures 00:28:26
Lecture 1 :
Deep Learning 101
Lecture 2 :
Performance
Lecture 3 :
From Perceptron to Neural Networks
Lecture 4 :
How to get the code
Lecture 5 :
Activation Functions
Lecture 6 :
Optimizer
Lecture 7 :
Deep Learning Frameworks
Lecture 8 :
Layer Types
Section 25 : Deep Learning Regression 4 Lectures 00:22:27
Lecture 1 :
Workspace Preparation
Lecture 2 :
Multi-Target Regression Lab (Intro)
Lecture 3 :
Multi-Target Regression Lab (Coding 1/2)
Lecture 4 :
Multi-Target Regression Lab (Coding 2/2)
Section 26 : Convolutional Neural Networks 5 Lectures 00:34:09
Lecture 1 :
Convolutional Neural Networks 101
Lecture 2 :
Convolutional Neural Networks Interactive
Lecture 3 :
Deep Learning Quiz
This quiz tests your knowledge on Deep Learning.
Lecture 4 :
Convolutional Neural Networks Lab (Intro)
Lecture 5 :
Convolutional Neural Networks Lab (Coding)
Section 27 : Deep Learning Classification 7 Lectures 00:49:54
Lecture 1 :
Binary Classification Lab (Intro)
Lecture 2 :
Binary Classification Lab (Coding 1/2)
Lecture 3 :
Binary Classification Lab (Coding 2/2)
Lecture 4 :
Multi-Label Classification Lab (Intro)
Lecture 5 :
Multi-Label Classification Lab (Coding 1/3)
Lecture 6 :
Multi-Label Classification Lab (Coding 2/3)
Lecture 7 :
Multi-Label Classification Lab (Coding 3/3)
Section 28 : Bonus 1 Lectures
Lecture 1 :
Congratulations and Thank You!

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Bert Gollnick,

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1 Courses

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 since many years. I studied Aeronautics, and Economics. My main interests are Machine Learning, Data Science, and Blockchain.