Classification Using Tree Based Models
Course info
Course info
Description
Machine Learning can sound very complicated, but anyone with a will to learn can successfully apply it, if they approach it from first principles. This course, Classification Using Tree Based Models, covers a specific class of Machine Learning problems - classification problems and how to solve these problems using Tree based models. First, you'll learn about building and visualizing decision trees as well as recognizing the serious problem of overfitting and its causes. Next, you'll learn about using ensemble learning to overcome overfitting. Finally, you'll explore 2 specific ensemble learning techniques - Random Forests and Gradient boosted trees By the end of this course, you'll be able to recognize opportunities where you can use Tree based models to solve classification problems and measure how well your solution is doing.
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.
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Section Introduction Transcripts
Course Overview
Hi everyone. My name is Swetha Kolalapudi, and welcome to my course, Classification Using Tree Based Models. I am the co-founder of a startup called Loonycorn. Machine learning can sound very complicated, but anyone with a will to learn can successfully apply it if they approach it from first principles. This course covers a specific class of machine-learning problems, which are classification problems, and how to solve these problems using tree-based models. Tree-based models are very intuitive to understand because they have a very clear visual representation. By the time you are done, you will know how to use a variety of tree-based algorithm to solve classification problems. Some of the major topics that we will cover include building and visualizing decision trees, recognizing the serious problem of overfitting, and its causes, using ensemble learning to overcome overfitting, and more specific ensemble learning techniques, random forest and gradient boosted trees. By the end of this course, you will be able to recognize opportunities where you can use tree-based models to solve classification problems, and you will be able to measure how well your solution is doing. Before beginning this course, you should be familiar with Python at a very basic level. I hope you will join me on this journey to learn classification using tree-based models at Pluralsight.