Unlock the hidden layers of data with R
R is one of the leading technologies in the field of data science. Are you looking at gaining in-depth knowledge of machine learning and deep learning? If yes, then this Learning Path is for you. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios.
The Learning Path begins with covering some basic concepts of R to refresh your R knowledge before we deep dive into advanced techniques. You will start with setting up the environment and then perform data ETL in R. You will then learn important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Next, you will understand the basics of deep learning and artificial neural networks and move on to exploring topics such as ANNs, RNNs, and CNNs. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC and feature engineering.
By the end of the Learning Path, you will have a solid knowledge of all these algorithms and techniques and be able to implement it efficiently in your data science projects.
Prerequisites: Basic knowledge of R would be beneficial. A background in linear algebra and statistics is expected.
Resources: Code downloads and errata:
This path navigates across the following products (in sequential order):
Mastering R Programming (5h 12m)
R Machine Learning Solutions (8h 20m)
Deep Learning with R (4h 4m)