If you’re familiar with R and want to master in machine learning, take up this course today!

Machine learning is an emerging field and there is a need of people who can analyze machine learning techniques to get some useful information from the ever increasing data. There are many big companies such as Google, Facebook, Amazon, Apple, IBM, and many more who heavily invest in machine learning research and applications. R and Machine Learning is one of the best combinations that is at the high demand in this data-driven world.

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

Your learning journey into the world of machine learning begins with exploring the various algorithms of supervised learning such as classification and regression. You will then learn how to use support vector machines, decision trees, and ensemble methods with real-world examples. Next, you will see how to implement neural networks and Bayesian learning in R.

After getting hands-on experience in supervised learning, you will then uncover the algorithms of unsupervised learning. The course teach you various techniques such as k-means clustering, principal component analysis, and recommendation algorithm. More than just knowing the outcome, you’ll understand how these algorithms work and what they do.

By the end of this course, you should be to confidently take your machine leaning skills at work!

Key Features

• Become master in implementing the top supervised and unsupervised learning algorithms
• An all-in-one mini project
• More than 25 assessments for you to gain mastery over the concepts you learned

Who this course is for

The course is aimed at data science professionals and aspiring data scientists who want to excel in the field of machine learning. Anyone who is familiar with R and want to become a machine-learning practitioner, a better problem solver, or maybe even consider a career in machine learning research can take up this course.