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

Independent component analysis


Independent component analysis (ICA) is similar to PCA in terms of dimensionality reduction. However, it originated from the signal processing world wherein they had this problem that multiple signals were being transmitted from a number of sources, and there were a number of devices set up to capture it. However, the problem was that the captured signal by the device was not very clear as it happened to be a mix of a number of sources. They needed to have clear and independent reception for the different devices that gave birth to ICA. Heralt and Jutten came up with this in.

The difference between PCA and ICA is that PCA focuses upon finding uncorrelated factors, whereas ICA is all about deriving independent factors. Confused? Let me help you. Uncorrelated factors imply that there is no linear relationship between them, whereas independence means that two factors have got no bearing on each other. For example, scoring good marks in mathematics is independent...