R and Machine Learning Fundamentals

Course details

R is one of the most popular languages used for machine learning and arguably, the best entry point to the fascinating world of machine learning (ML). If you're interested to explore both the programming and machine learning world with R, then go for this course.

This course is a blend of text, videos, code examples, assessments, case studies, and a mini project which together makes your learning journey all the more exciting and truly rewarding. It is meticulously designed and developed in order to empower you with all the right and relevant information on R.

Lets take a look at this learning journey. The course starts with teaching you how to set up the R environment, which includes installing RStudio and R packages. You will learn the various data types, operators, and control structures. You will then understand the split-apply-combine paradigm. You will see how to build effective data visualization using the widely popular ggplot2 library. The course also demonstrates a case study on the very famous Iris dataset.

Moving ahead, you will be introduced to the various aspects of machine learningsupervised, unsupervised, reinforcement, and deep learning. Machine learning aims to uncover hidden patterns, unknown correlations, and find useful information from data. This course aims to make you proficient enough to write R programs to perform various ML tasks irrespective of your previous programming experience and skill level. You will go through the different types of machine learning and when it's to be used along with a case study. Finally, you will look at a full-fledged project that will teach you how to build machine learning models.

By the end of this course, you will have a good knowledge of R principles in both programming and machine learning which you can use as a springboard to further develop your expertise.

About the Author

Akash Tandon is a Data Engineer at RedCarpet (a Y-Combinator and Google Startup Launchpad startup) with his primary responsibilities including setup and maintenance of the organizations Machine Learning infrastructure. Hes also a data science competitions enthusiast and has worked on various competitions with notable results on various platforms, including Kaggle, HackerEarth and Analytics Vidhya. An avid open source software (OSS) enthusiast, he has worked thrice with the the organization, R project of Statistical computing, under the Google Summer of Code programs, both as a student and mentor.