Machine Learning, incl. Deep Learning, with R
4.1 (67 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,609 students enrolled

Machine Learning, incl. Deep Learning, with R

Statistical Machine Learning Techniques, and Deep Learning with Keras, and much more. (All R code included)
4.1 (67 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,609 students enrolled
Created by Bert Gollnick
Last updated 11/2019
English
English [Auto]
Current price: $20.99 Original price: $29.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 15.5 hours on-demand video
  • 5 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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.
Course content
Expand all 147 lectures 15:24:26
+ Introduction
6 lectures 34:32

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.

Preview 02:55
AI 101
05:06
Machine Learning 101
07:09
+ R Refresher
8 lectures 50:36
R and RStudio Installation
09:35
How to get the code
01:37
Rmarkdown Lab
09:26
Piping 101
02:35
Data Manipulation Lab
10:32
Data Reshaping 101
03:20
Data Reshaping Lab
11:43
Packages Preparation Lab
01:48
+ Regression
12 lectures 01:26:18
Regression Types 101
03:40
Univariate Regression 101
05:48
Univariate Regression Interactive
04:01
Univariate Regression Lab
12:10
Univariate Regression Exercise
02:20
Univariate Regression Solution
07:51
Polynomial Regression 101
02:12
Polynomial Regression Lab
13:59
Multivariate Regression 101
04:41
Multivariate Regression Lab
14:09
Multivariate Regression Exercise
02:15
Multivariate Regression Solution
13:12
Regression Quiz
5 questions
+ Model Preparation and Evaluation
6 lectures 57:49
Underfitting Overfitting 101
11:19
Train / Validation / Test Split 101
02:56
Train / Validation / Test Split Interactive
07:45
Train / Validation / Test Split Lab
12:51
Resampling Techniques 101
04:52
Resampling Techniques Lab
18:06
+ Regularization
2 lectures 23:34
Regularization 101
05:57
Regularization Lab
17:37
+ ----- Classification -----
2 lectures 01:41
Classification Introduction
00:04
How to get the code
01:37
+ Classification Basics
7 lectures 51:10
Confusion Matrix 101
06:15
ROC Curve 101
07:11
ROC Curve Lab Intro
01:54
ROC Curve Lab 1/3 (Data Prep, Modeling)
11:19
ROC Curve Lab 2/3 (Confusion Matrix and ROC)
05:56
ROC Curve Lab 3/3 (ROC, AUC, Cost Function)
12:07
+ Random Forests
6 lectures 31:34
Random Forests 101
02:55
Random Forests Interactive
03:41
Random Forest Lab (Coding 1/2)
11:39
Random Forest Lab (Coding 2/2)
08:58
Random Forest Exercise
02:29
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.

Who this course is for:
  • R beginners and professionals with interest in Machine Learning and/or Deep Learning