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

Chapter 1. Quantifying Learning Algorithms

We have stepped into an era where we arebuildingsmart or intelligent machines. This smartness or intelligence is infused into the machine with the help of smart algorithms based on mathematics/statistics. These algorithms enable the system or machine to learn automatically without any human intervention. As an example of this, today we are surrounded by a number of mobile applications. One of the prime messaging apps of today in WhatsApp (currently owned by Facebook). Whenever we type a message into a textbox of WhatsApp, and we type, for example, I am..., we get a few word prompts popping up, such as ..going homeRahultraveling tonight, and so on. Can we guess what's happening here and why? Multiple questions come up:

  • What is it that the system is learning?
  • Where does it learn from?
  • How does it learn?

Let's answer all these questions in this chapter.

In this chapter, we will cover the following topics:

  • Statistical models
  • Learning curves
  • Curve fitting
  • Modeling cultures
  • Overfitting and regularization
  • Train, validation, and test
  • Cross-validation and model selection
  • Bootstrap method