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

Key concepts


We will cover a few key concepts before moving on to the body of the chapter:

  • In the case of discrete distribution, a probability mass function is used to find out the probability, p(X= x), where X is a discrete random variable and x is a real value number.
  • In the case of continuous distribution, probability density function is used to find out the probability p(X <= x). In this scenario, a probability curve is plotted and the area under the curve (integration) helps us with the probability.
  • Conditional probability is to understand this, a cricket match can be the perfect example. Suppose there is a game scheduled between India and Australia and we are trying to pass on our belief of India triumphing. Do you think that the probability will be impacted by the team selected by India? Will the probability of India winning the match be impacted if Virat Kohli and Rohit Sharma are part of the team? So, p(India winning|Rohit and Virat are playing) denotes the probability of India...