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 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.
Hi everyone, my name is Swetha Kolalapudi and welcome to my course "How to Think About Machine Learning Algorithms." I am the cofounder of a startup 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 need to be a math genius to successfully apply machine learning. ML techniques can be learnt from first principles by anyone with the will to learn.
This course is all about learning ML from first principles. No jargon, no abstruse math just simple direct explanations and techniques you can use. By the time you are done, you’ll know how to set up trading stocks, recommending movies to friends or sensing sentiment about your favorite candidates as cookie cutter ML problems that you can write code to solve.
Some of the major topics that we will cover include:
1. Classifying data into predefined categories
2. Predicting relationships betweenvariables withregression
3. Recommending products toauser
4. Clustering large datasets into meaningful groups
By the end of this course you’ll 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 the 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.