Applied Machine Learning in R


Learn machine learning and implement practical algorithms using R programming Free Download

  • Basic knolwedge of R is required to complete this course

Machine learning is here and it is changing the way businesses work! From Netflix recommendation engine to Google’s self-driving car, it’s all machine learning. Machine Learning explores the development and use of algorithms that can gain from data.  ML Algorithms provides the ability to learn with an accelerated pace as more and more data set is available for training. It is very similar to how human mind learn many things.

Our course on Applied Machine learning with R uses the powerful data manipulation language R for solving the ML problems. This unique course will help you get started on the journey of becoming an AI and machine learning developer.


In this course you will also learn about machine learning and deep learning and will see how R can be used as a tool for showing up the output and how you can use R for your ML projects also.

The course also covers the packages that implements machine learning with Tensorflow and H2O. Tensorflow is the python package that is implemented in R as well. The course also covers artificial neural networks. Here you get to learn how to create our own neural network and implement them in R.

Last but not the least, the sixth module is Decision Tree and Text mining. It is a well know pattern that is involved in data science, again a new concept of machine learning.

All the above modules will throw light on how machine learning implementation is easy and simple using R.  So what are you waiting for? Begin your epic journey to be an awesome ML programmer with this applied R course.


Who this course is for:
  • Anyone interetsed in building ML aplications in R will find this course very useful


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Author: Ho Quang Dai

I am Ho Quang Dai, from Vietnam – A country that loves peace. I share completely free courses from major academic websites around the world. Hope to bring free knowledge to everyone who can’t afford to buy

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