Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Summary


In this chapter, we studied ensemble learning and its different methods, namely bagging, boosting, and stacking. We even saw what is bootstrapping which is the root for ensemble learning methods such as bagging and boosting. We also learned about decision trees and its approach of divide and rule with example of people applying for loan. Then we covered tree splitting and the parameters to split a decision tree, moving on to the random forest algorithm. We worked on a case study of breast cancer using the concepts covered. We also discovered the difference between bagging and boosting and gradient boosting. We also discussed on parameters of gradient boosting to use it our example of breast cancer.

In the next chapter, we will learn about training neural networks.