Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
2 Hour(s) 9 Minute(s)
Language
English
Taught by
Elementary Learners
Rating
Course Overview
Learn Machine Learning with the help of R programming
Inspired by the field of Machine Learning? Then this course is for you!
This course is intended for both freshers and experienced hoping to make the bounce to Data Science.
R is a statistical programming language which provides tools to analyze data and for creating high-level graphics.
The topic of Machine Learning is getting exceptionally hot these days in light of the fact that these learning algorithms can be utilized as a part of a few fields from software engineering to venture managing an account. Students, at the end of this course, will be technically sound in the basics and the advanced concepts of Machine Learning.
Course Content
- 7 section(s)
- 38 lecture(s)
- Section 1 Module-1 Introduction to Course
- Section 2 Module-2 Introduction to validation and its Methods
- Section 3 Module-3 Classification
- Section 4 Module-4 Black Box Method-Neural network and SVM
- Section 5 Module-5 Tree Based Models
- Section 6 Module-6 Clustering
- Section 7 Module-7 Regression
What You’ll Learn
- Master Machine Learning, Regression modelling, knn algorithm, naive bayes algorithm, BPN(Back Propagation Network), SVM(Support Vector Machine), Decision Tree, Forecasting
Skills covered in this course
Reviews
-
EEshwar Udho
R experience needed like said in introduction. I just wanted an overview and quick example of each type of machine learning, and this gives just that. This course is a great starting point to explore machine learning. You will still need to do a lot on your own after this course to go in dept into the algorithms.
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SSherri Walker
Highly conceptual, so far.
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FFirst Last
This course does a pretty good job introducing and showing examples of various topics. Some examples don't work as given without minor tweaks. My biggest criticism is that there is no instruction on how to interpret the results of the models.
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TTina
Definitions and examples were simple to follow.
