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

Chapter 2. Evaluating Kernel Learning

In machine learning, pattern finding is an area that is being explored to the hilt. There are many methods and algorithms that can drive this kind of work and analysis. However, in this chapter, we will try to focus on how kernels are making a significant difference to the whole machine learning outlook. The application of kernel learning doesn't have any boundaries: starting from a simple regression problem to a computer vision classification, it has made its presence felt everywhere. Support vector machine (SVM) is one of those algorithms that happens to make use of kernel learning.

In this chapter, we will be focusing on the following concepts:

  • Concepts of vectors, linear separability, and hyperplanes
  • SVM
  • Kernel tricks
  • Gaussian process
  • Parameter optimization