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 have studied association rules. We also discussed the Apriori algorithm, which is used for mining frequent itemsets to derive various association rules. We also learned about frequent pattern growth (FP-growth), which is similar to Apriori and about the frequent itemset generation technique, which is similar to the Apriori algorithm. Finally, we saw how FP-growth tends to have an edge over Apriori, as it is faster and more efficient, using an example. 

In the next chapter, we will study probabilistic graphical models. We will learn in depth about the Bayesian rules and Bayesian networks.