4.44 out of 5
4.44
39 reviews on Udemy

Machine Learning, incl. Deep Learning, with R

Statistical Machine Learning Techniques, and Deep Learning with Keras, and much more. (All R code included)
Instructor:
Bert Gollnick
5,005 students enrolled
English [Auto-generated]
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.

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.

Introduction

1
Course Overview

This ZIP-file includes a template, that we will work on together to find out how easy it is to interact with R and setting up a model.

You might also take a look at the file "PCA_Teaser_final.Rmd". This includes all code.

2
AI 101
3
Machine Learning 101
4
Models
5
Teaser Overview
6
Teaser Lab

R Refresher

1
R and RStudio Installation
2
How to get the code
3
Rmarkdown Lab
4
Piping 101
5
Data Manipulation Lab
6
Data Reshaping 101
7
Data Reshaping Lab
8
Packages Preparation Lab

----- Regression, Model Preparation, and Regularization -----

1
Section Overview
2
How to get the code

Regression

1
Regression Types 101
2
Univariate Regression 101
3
Univariate Regression Interactive
4
Univariate Regression Lab
5
Univariate Regression Exercise
6
Univariate Regression Solution
7
Polynomial Regression 101
8
Polynomial Regression Lab
9
Multivariate Regression 101
10
Multivariate Regression Lab
11
Multivariate Regression Exercise
12
Multivariate Regression Solution
13
Regression Quiz

Model Preparation and Evaluation

1
Underfitting Overfitting 101
2
Train / Validation / Test Split 101
3
Train / Validation / Test Split Interactive
4
Train / Validation / Test Split Lab
5
Resampling Techniques 101
6
Resampling Techniques Lab

Regularization

1
Regularization 101
2
Regularization Lab

----- Classification -----

1
Classification Introduction
2
How to get the code

Classification Basics

1
Confusion Matrix 101
2
ROC Curve 101
3
ROC Curve Interactive
4
ROC Curve Lab Intro
5
ROC Curve Lab 1/3 (Data Prep, Modeling)
6
ROC Curve Lab 2/3 (Confusion Matrix,ROC)
7
ROC Curve Lab 3/3 (ROC, AUC, Cost Function)

Decision Trees

1
Decision Trees 101
2
Decision Trees Lab (Intro)
3
Decision Trees Lab (Coding)
4
Decision Trees Exercise

Random Forests

1
Random Forests 101
2
Random Forests Interactive
3
Random Forest Lab (Intro)
4
Random Forest Lab (Coding 1/2)
5
Random Forest Lab (Coding 2/2)
6
Random Forest Exercise

Logistic Regression

1
Logistic Regression 101
2
Logistic Regression Lab (Intro)
3
Logistic Regression Lab (Coding 1/2)
4
Logistic Regression Lab (Coding 2/2)
5
Logistic Regression Exercise

Support Vector Machines

1
Support Vector Machines 101
2
Support Vector Machines Lab (Intro)
3
Support Vector Machines Lab (Coding 1/2)
4
Support Vector Machines Lab (Coding 2/2)
5
Support Vector Machines Exercise

Ensemble Models

1
Ensemble Models 101
2
Classification Quiz

----- Association Rules -----

1
Association Rules 101
2
How to get the code

Apriori

1
Apriori 101
2
Apriori Lab (Intro)
3
Apriori Lab (Coding 1/2)
4
Apriori Lab (Coding 2/2)
5
Apriori Exercise
6
Apriori Solution

----- Clustering -----

1
Clustering Overview
2
How to get the code

kmeans

1
kmeans 101
2
kmeans Lab
3
kmeans Exercise
4
kmeans Solution

Hierarchical Clustering

You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
4.4
4.4 out of 5
39 Ratings

Detailed Rating

Stars 5
18
Stars 4
19
Stars 3
2
Stars 2
1
Stars 1
0
0316a4169eb5adb72081a0978c414361
30-Day Money-Back Guarantee

Includes

15 hours on-demand video
4 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion