[Pluralsight] How to Think About Machine Learning Algorithms
How to Think About Machine Learning Algorithms

How to Think About Machine Learning Algorithms

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Course Description

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. 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 th

e end of this course, you'll have the skills and knowledge required to recognize an opportunity for a machine learning application and seize it.

What you will learn

Hi everyone, my name is Swetha Kolalapudi, and I'd like to welcome you to my course, How to Think About Machine Learning Algorithms. I am the co-founder of a start-up called Loonycorn. Machine learning is all the rage these days, but too many folks get intimidated by its reputation. Contrary to popular perception, you don't actually need to be a math genius to successfully apply machine learning. Machine learning techniques can be learned from first principles by anyone who has the will to learn. This course is all about learning machine learning from first principles. There's no jargon, no abstruse math, just simple direct explanations and techniques that you can use directly. By the time you are done, you will know how to set up trading stocks, recommending movies to friends, or sensing the sentiment about your favorite candidates, as cookie-cutter ML problems that you can write, code, or solve. Some of the major topics that we will cover include, classifying data into pre-defined categories, predicting relationships between variables with regression, recommending products to a user, and clustering large data sets into meaningful groups. By the end of this course, you will be able to recognize opportunities where you can use machine learning and solve problems using standard techniques such as support vector machines, or linear regression. Before beginning this course, you should be familiar with Python at a very basic level. I hope you'll join me on this journey to learn how to think about machine learning algorithms at Pluralsight.

Curriculum

Section 1: Course Overview

Section 2: Introducing Machine Learning

Section 3: Classifying Data into Predefined Categories

Section 4: Solving Classification Problems

Section 5: Predicting Relationships between Variables with Regression

Section 6: Solving Regression Problems

Section 7: Recommending Relevant Products to a User

Section 8: Clustering Large Data Sets into Meaningful Groups

Section 9: Wrapping up and Next Steps