• Post category:StudyBullet-16
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R Basics, Data Science, Statistical Machine Learning models, Deep Learning, Shiny and much more (All R code included)

What you will learn

learn all aspects of R from Basics, over Data Science, to Machine Learning and Deep Learning

learn R basics (data types, structures, variables, …)

learn R programming (writing loops, functions, …)

data im- and export

basic data manipulation (piping, filtering, aggregation of results, data reshaping, set operations, joining datasets)

data visualisation (different packages are learned, e.g. ggplot, plotly, leaflet, dygraphs)

advanced data manipulation (outlier detection, missing data handling, regular expressions)

regression models (create and apply regression models)

model evaluation (What is underfitting and overfitting? Why is data splitted into training and testing? What are resampling techniques?)

regularization (What is regularization? How can you apply it?)

classification models (understand different algorithms and learn how to apply logistic regression, decision trees, random forests, support vector machines)

association rules (learn the apriori model)

clustering (kmeans, hierarchical clustering, DBscan)

dimensionality reduction (factor analysis, principal component analysis)

Reinforcement Learning (upper confidence bound)

Deep Learning (deep learning for multi-target regression, binary and multi-label classification)

Deep Learning (learn image classification with convolutional neural networks)

Deep Learning (learn about Semantic Segmentation)

Deep Learning (Recurrent Neural Networks, LSTMs)

More on Deep Learning, e.g. Autoencoders, pretrained models, …

R/Shiny for web application development and deployment

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, …

You will also learn to develop web applications and how to deploy them with R/Shiny.


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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.

English
language

Content

Course Introduction

Course Overview
R and RStudio (Overview and Installation)
How to get the code
RStudio Introduction / Project Setup
File Formats
Rmarkdown Lab
Package Handling

Data Types and -structures

Basic Data Types 101
Basic Data Types Lab
Matrices and Arrays Lab
Lists
Factors
Dataframes
Strings Lab
Datetime

R Programming

Operators
Loops 101
Loops Lab
Functions 101
Functions Lab (Intro)
Functions Lab (Coding)

Data Im- and Export

Data Import Lab
Data Export Lab
Web Scraping Intro
Web Scraping Lab

Basic Data Manipulation

Piping 101
Filtering 101
Filtering Lab
Filtering Exercise
Filtering Solution
Data Aggregation 101
Data Aggregation Lab
Data Aggregation Exercise
Data Aggregation Solution
Data Reshaping 101
Data Reshaping Lab
Data Reshaping Exercise
Data Reshaping Solution
Set Operations 101
Set Operations Lab
Joining Datasets 101
Joining Datasets Lab

Data Visualisation

Visualisation Overview
ggplot 101
ggplot Lab
plotly Lab (Intro)
plotly Lab
leaflet Lab (Intro)
leaflet Lab
dygraphs Lab (Intro)
dygraphs Lab

Advanced Data Manipulation

Outlier Detection 101
Outlier Detection Lab (Intro)
Outlier Detection Lab
Outlier Detection Exercise
Outlier Detection Solution
Missing Data Handling 101
Missing Data Handling Lab (Intro)
Missing Data Handling Lab (1/1)
Regular Expressions 101
Regular Expressions Lab

Machine Learning: Introduction

AI 101
Machine Learning 101
Models

Machine Learning: Regression

Regression Types 101
Univariate Regression 101
Univariate Regression Interactive
Univariate Regression Lab
Univariate Regression Exercise
Univariate Regression Solution
Polynomial Regression 101
Polynomial Regression Lab
Multivariate Regression 101
Multivariate Regression Lab
Multivariate Regression Exercise
Multivariate Regression Solution

Machine Learning: Model Preparation and Evaluation

Underfitting / Overfitting 101
Train / Validation / Test Split 101
Train / Validation / Test Split Interactive
Train / Validation / Test Split Lab
Resampling Techniques 101
Resampling Techniques Lab

Machine Learning: Regularization

Regularization 101
Regularization Lab

Machine Learning: Classification Basics

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

Machine Learning: Classification with Decision Trees

Decision Trees 101
Decision Trees Lab (Intro)
Decision Trees Lab (Coding)
Decision Trees Exercise
Decision Trees Solution

Machine Learning: Classification with Random Forests

Random Forests 101
Random Forests Interactive
Random Forest Lab (Intro)
Random Forest Lab (Coding 1/2)
Random Forest Lab (Coding 2/2)

Machine Learning: Classification with Logistic Regression

Logistic Regression 101
Logistic Regression Lab (Intro)
Logistic Regression Lab (Coding 1/2)
Logistic Regression Lab (Coding 2/2)
Logistic Regression Exercise
Logistic Regression Solution

Machine Learning: Classification with Support Vector Machines

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

Machine Learning: Classification with Ensemble Models

Ensemble Models 101

Machine Learning: Association Rules

Association Rules 101
Apriori 101
Apriori Lab (Intro)
Apriori Lab (Coding 1/2)
Apriori Lab (Coding 2/2)
Apriori Exercise
Apriori Solution

Machine Learning: Clustering

Clustering Overview
kmeans 101
kmeans Lab
kmeans Exercise
kmeans Solution
Hierarchical Clustering 101
Hierarchical Clustering Interactive
Hierarchical Clustering Lab
Dbscan 101
Dbscan Lab

Machine Learning: Dimensionality Reduction

PCA 101
PCA Lab
PCA Exercise
PCA Solution
t-SNE 101
t-SNE Lab (Sphere)
t-SNE Lab (Mnist)
Factor Analysis 101
Factor Analysis Lab (Intro)
Factor Analysis Lab (Coding 1/2)
Factor Analysis Lab (Coding 2/2)
Factor Analysis Exercise

Machine Learning: Reinforcement Learning

Reinforcement Learning 101
Upper Confidence Bound 101
Upper Confidence Bound Interactive
Upper Confidence Bound Lab (Intro)
Upper Confidence Bound Lab (Coding 1/2)
Upper Confidence Bound Lab (Coding 2/2)

Deep Learning: Introduction

Deep Learning General Overview
Deep Learning Modeling 101
Performance
From Perceptron to Neural Networks
Layer Types
Activation Functions
Loss Function
Optimizer
Deep Learning Frameworks
Python and Keras Installation

Deep Learning: Regression

Multi-Target Regression Lab (Intro)
Multi-Target Regression Lab (Coding 1/2)
Multi-Target Regression Lab (Coding 2/2)

Deep Learning: Classification

Binary Classification Lab (Intro)
Binary Classification Lab (Coding 1/2)
Binary Classification Lab (Coding 2/2)
Multi-Label Classification Lab (Intro)
Multi-Label Classification Lab (Coding 1/3)
Multi-Label Classification Lab (Coding 2/3)
Multi-Label Classification Lab (Coding 3/3)

Deep Learning: Convolutional Neural Networks

Convolutional Neural Networks 101
Convolutional Neural Networks Interactive
Convolutional Neural Networks Lab (Intro)
Convolutional Neural Networks Lab (1/1)
Convolutional Neural Networks Exercise
Convolutional Neural Networks Solution
Semantic Segmentation 101
Semantic Segmentation Lab (Intro)
Semantic Segmentation Lab (1/1)

Deep Learning: Autoencoders

Autoencoders 101
Autoencoders Lab (Intro)
Autoencoders Lab (Coding)

Deep Learning: Transfer Learning and Pretrained Networks

Transfer Learning and Pretrained Models 101
Transfer Learning and Pretrained Models Lab (Intro)
Transfer Learning and Pretrained Models Lab (1/1)

Deep Learning: Recurrent Neural Networks

Recurrent Neural Networks 101
LSTM: Univariate, Multistep Timeseries Prediction (Intro)
LSTM: Univariate, Multistep Timeseries Prediction Lab (1/1)
LSTM: Multivariate, Multistep Timeseries Prediction (Intro)
LSTM: Multivariate, Multistep Timeseries Prediction Lab (1/1)

Bonus

Congratulations and thank you