R Ultimate: Learn R for Data Science and Machine Learning

R Basics, Data Science, Statistical Machine Learning models, Deep Learning with Keras, much more (All R code included)
What you'll 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, ...

Requirements
no prior knowledge required - just be passionate to gain new skills

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. Don't 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