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.
What you'll learn
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.
Table of contents
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. She was the first member of Flipkart’s elite Analytics team and was instrumental in scaling it to 100+ employees. Swetha has always had an entrepreneurial bent and a love for teaching. She now has the chance to do both as the co¬founder of Loonycorn, a content studio focused on providing high quality content for technical skill development. Loonycorn ... more