# R Ultimate: Learn R for Data Science and Machine Learning

### R Ultimate: Learn R for Data Science and Machine Learning | Udemy Coupon ED

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

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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)

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.

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