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R Programming Mastery

R Basics, Data Science, Statistical Machine Learning models, Deep Learning with Keras, much more (All R code included)

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4.7 (22 ratings)
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30-day money-back guarantee

This course includes

21 h 19 min of video content
Beginner Difficulty
Perpetual Access
Access on mobile and Tablet
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Learning Objectives

learn all aspects of R from Basics, over Data Science, to Machine Learning and Deep Learning
learn R programming (writing loops, functions, ...)
basic data manipulation (piping, filtering, aggregation of results, data reshaping, set operations, joining datasets)
advanced data manipulation (outlier detection, missing data handling, regular expressions)
model evaluation (What is underfitting and overfitting? Why is data splitted into training and testing? What are resampling techniques?)
classification models (understand different algorithms and learn how to apply logistic regression, decision trees, random forests, support vector machines)
clustering (kmeans, hierarchical clustering, DBscan)
Reinforcement Learning (upper confidence bound)
Deep Learning (learn image classification with convolutional neural networks)
Deep Learning (Recurrent Neural Networks, LSTMs)
learn R basics (data types, structures, variables, ...)
data im- and export
data visualisation (different packages are learned, e.g. ggplot, plotly, leaflet, dygraphs)
regression models (create and apply regression models)
regularization (What is regularization? How can you apply it?)
association rules (learn the apriori model)
dimensionality reduction (factor analysis, principal component analysis)
Deep Learning (deep learning for multi-target regression, binary and multi-label classification)
Deep Learning (learn about Semantic Segmentation)
More on Deep Learning, e.g. Autoencoders, pretrained models, ...

Table of Contents

189 lectures
21 h 19 min
Course Overview
05:17
R and RStudio (Overview and Installation)
09:31
How to get the code
02:58
RStudio Introduction / Project Setup
09:57
File Formats
08:58
Rmarkdown Lab
09:26
Package Handling
00:59
Basic Data Types 101
06:57
Basic Data Types Lab
15:02
Matrices and Arrays Lab
07:22
Lists
08:11
Factors
13:44
Dataframes
08:37
Strings Lab
24:05
Datetime
07:22
Operators
07:54
Loops 101
05:16
Loops Lab
09:16
Functions 101
04:16
Functions Lab (Intro)
01:27
Functions Lab (Coding)
18:57
Data Import Lab
09:24
Data Export Lab
04:36
Web Scraping Intro
01:06
Web Scraping Lab
07:01
Piping 101
02:35
Filtering 101
05:52
Filtering Lab
10:18
Data Aggregation 101
04:46
Data Aggregation Lab
04:53
Data Reshaping 101
03:20
Data Reshaping Lab
11:43
Set Operations 101
01:30
Set Operations Lab
02:21
Joining Datasets 101
07:32
Joining Datasets Lab
05:34
Visualisation Overview
02:54
ggplot 101
11:04
ggplot Lab
17:48
plotly Lab (Intro)
02:18
plotly Lab
11:21
leaflet Lab (Intro)
02:24
leaflet Lab
09:11
dygraphs Lab (Intro)
01:19
dygraphs Lab
10:22
Outlier Detection 101
11:16
Outlier Detection Lab (Intro)
01:20
Outlier Detection Lab
20:04
Missing Data Handling 101
06:08
Missing Data Handling Lab (Intro)
01:02
Missing Data Handling Lab (1/1)
20:04
Regular Expressions 101
04:25
Regular Expressions Lab
16:19
AI 101
05:06
Machine Learning 101
07:09
Models
05:33
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
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 101
05:57
Regularization Lab
17:37
Confusion Matrix 101
06:16
ROC Curve 101
07:11
ROC Curve Interactive
06:28
ROC Curve Lab Intro
01:54
ROC Curve Lab 1/3 (Data Prep, Modeling)
13:13
ROC Curve Lab 2/3 (Confusion Matrix and ROC)
05:56
ROC Curve Lab 3/3 (ROC, AUC, Cost Function)
12:07
Decision Trees 101
05:54
Decision Trees Lab (Intro)
01:31
Decision Trees Lab (Coding)
14:37
Decision Trees Exercise
021:47
Random Forests 101
02:55
Random Forests Interactive
03:41
Random Forest Lab (Intro)
01:52
Random Forest Lab (Coding 1/2)
11:39
Random Forest Lab (Coding 2/2)
08:58
Logistic Regression 101
07:33
Logistic Regression Lab (Intro)
00:59
Logistic Regression Lab (Coding 1/2)
08:52
Logistic Regression Lab (Coding 2/2)
06:59
Logistic Regression Exercise
01:15
Logistic Regression Solution
00:02
Support Vector Machines 101
05:17
Support Vector Machines Lab (Intro)
01:26
Support Vector Machines Lab (Coding 1/2)
08:24
Support Vector Machines Lab (Coding 2/2)
04:58
Support Vector Machines Exercise
02:16
Association Rules 101
05:50
Apriori 101
08:12
Apriori Lab (Intro)
01:56
Apriori Lab (Coding 1/2)
07:33
Apriori Lab (Coding 2/2)
10:52
Apriori Exercise
02:22
Apriori Solution
10:44
Clustering Overview
02:51
kmeans 101
07:23
kmeans Lab
15:36
kmeans Exercise
03:17
kmeans Solution
10:45
Hierarchical Clustering 101
08:04
Hierarchical Clustering Interactive
06:39
Hierarchical Clustering Lab
18:38
Dbscan 101
04:49
Dbscan Lab
13:53
PCA 101
08:41
PCA Lab
14:45
PCA Exercise
02:08
PCA Solution
09:19
t-SNE 101
05:47
t-SNE Lab (Sphere)
06:23
t-SNE Lab (Mnist)
06:45
Factor Analysis 101
09:27
Factor Analysis Lab (Intro)
01:37
Factor Analysis Lab (Coding 1/2)
08:02
Factor Analysis Lab (Coding 2/2)
08:19
Factor Analysis Exercise
01:46
Reinforcement Learning 101
07:41
Upper Confidence Bound 101
12:46
Upper Confidence Bound Interactive
07:14
Upper Confidence Bound Lab (Intro)
01:58
Upper Confidence Bound Lab (Coding 1/2)
14:22
Upper Confidence Bound Lab (Coding 2/2)
06:23
Deep Learning General Overview
03:41
Deep Learning Modeling 101
03:33
Performance
02:33
From Perceptron to Neural Networks
03:46
Layer Types
03:57
Activation Functions
04:14
Loss Function
03:33
Optimizer
06:16
Deep Learning Frameworks
02:23
Python and Keras Installation
07:00
Multi-Target Regression Lab (Intro)
01:31
Multi-Target Regression Lab (Coding 1/2)
11:33
Multi-Target Regression Lab (Coding 2/2)
09:21
Binary Classification Lab (Intro)
01:34
Binary Classification Lab (Coding 1/2)
11:44
Binary Classification Lab (Coding 2/2)
06:30
Multi-Label Classification Lab (Intro)
02:50
Multi-Label Classification Lab (Coding 1/3)
10:01
Multi-Label Classification Lab (Coding 2/3)
11:30
Multi-Label Classification Lab (Coding 3/3)
05:38
Convolutional Neural Networks 101
10:04
Convolutional Neural Networks Interactive
03:44
Convolutional Neural Networks Lab (Intro)
01:32
Convolutional Neural Networks Lab (1/1)
18:46
Convolutional Neural Networks Exercise
02:26
Convolutional Neural Networks Solution
00:02
Semantic Segmentation 101
07:42
Semantic Segmentation Lab (Intro)
03:01
Semantic Segmentation Lab (1/1)
11:00
Autoencoders 101
02:39
Autoencoders Lab (Intro)
01:42
Autoencoders Lab (Coding)
10:58
Transfer Learning and Pretrained Models 101
04:52
Transfer Learning and Pretrained Models Lab (Intro)
01:45
Transfer Learning and Pretrained Models Lab (1/1)
09:35
Recurrent Neural Networks 101
06:58
LSTM: Univariate, Multistep Timeseries Prediction (Intro)
01:45
LSTM: Univariate, Multistep Timeseries Prediction Lab (1/1)
13:13
LSTM: Multivariate, Multistep Timeseries Prediction (Intro)
01:38
LSTM: Multivariate, Multistep Timeseries Prediction Lab (1/1)
12:17

Description

You want to be able to perform your own data analyses with R? You want to learn how to get business-critical insights out of your data? Or you want to get a job in this amazing field? In all of these cases, you found the right course!

We will start with the very Basics of R, like data types and -structures, programming of loops and functions, data im- and export.

Then we will dive deeper into data analysis: we will learn how to manipulate data by filtering, aggregating results, reshaping data, set operations, and joining datasets. We will discover different visualisation techniques for presenting complex data. Furthermore find out to present interactive timeseries data, or interactive geospatial data.

Advanced data manipulation techniques are covered, e.g. outlier detection, missing data handling, and regular expressions.

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. Do not wait. See you in the course.

Who this course is for:

  • R beginners interested in learning R
  • data science practitioners who want to deepen their knowledge
  • developers who want to learn different aspects of Machine Learning

Featured Reviews

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Apr 05, 2021

Jake B.

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The instructor takes his time and adequately explains each step, which is helpful since I do not have a lot of experience with R.
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Aug 20, 2021

Jasper L.

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Really ultimate learn! very detailed knowledge, thanks!