How to Think About Machine Learning Algorithms

If you don't know the question, you probably won't get the answer right. This course is all about asking the right machine learning questions, modeling real-world situations as one of several well understood machine learning problems.
Course info
Rating
(271)
Level
Beginner
Updated
Sep 27, 2016
Duration
3h 8m
Table of contents
Course Overview
Introducing Machine Learning
Classifying Data into Predefined Categories
Solving Classification Problems
Predicting Relationships between Variables with Regression
Solving Regression Problems
Recommending Relevant Products to a User
Clustering Large Data Sets into Meaningful Groups
Wrapping up and Next Steps
Description
Course info
Rating
(271)
Level
Beginner
Updated
Sep 27, 2016
Duration
3h 8m
Description

Machine learning is behind some of the coolest technological innovations today, Contrary to popular perception, however, you don't need to be a math genius to successfully apply machine learning. As a data scientist facing any real-world problem, you first need to identify whether machine learning can provide an appropriate solution. In this course, How to Think About Machine Learning Algorithms, you'll learn how to identify those situations. First, you will learn how to determine which of the four basic approaches you'll take to solve the problem: classification, regression, clustering or recommendation. Next, you'll learn how to set up the problem statement, features, and labels. Finally you'll plug in a standard algorithm to solve the problem. At the end of this course, you'll have the skills and knowledge required to recognize an opportunity for a machine learning application and seize it.

About the author
About the author

Swetha loves playing with data and crunching numbers to get cool insights. She is an alumnus of top schools like IIT Madras and IIM Ahmedabad.

More from the author
Classification Using Tree Based Models
Beginner
1h 56m
Jan 6, 2017
More courses by Swetha Kolalapudi