Machine Learning Algorithms - Second Edition: Popular algorithms for data science and machine learning, 2nd Edition

by Giuseppe Bonaccorso

Overview

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

Explore statistics and complex mathematics for data-intensive applications

Discover new developments in EM algorithm, PCA, and bayesian regression

Study patterns and make predictions across various datasets

Book Description

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight.

This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture.

By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.

What you will learn

Study feature selection and the feature engineering process

Assess performance and error trade-offs for linear regression

Build a data model and understand how it works by using different types of algorithm

Learn to tune the parameters of Support Vector Machines (SVM)

Explore the concept of natural language processing (NLP) and recommendation systems

Create a machine learning architecture from scratch

Who this book is for

Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book.

Product Details

ISBN-13:	9781789347999
Publisher:	Packt Publishing
Publication date:	08/30/2018
Edition description:	2nd ed.
Pages:	522
Product dimensions:	7.50(w) x 9.25(h) x 1.05(d)

About the Author

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.

Table of Contents

Table of Contents

1. A Gentle Introduction to Machine Learning
2. Important Elements in Machine Learning
3. Feature Selection and Feature Engineering
4. Regression Algorithms
5. Linear Classification Algorithms
6. Naive Bayes and Discriminant Analysis
7. Support Vector Machines
8. Decision Trees and Ensemble Learning
9. Clustering Fundamentals
10. Advanced Clustering
11. Hierarchical Clustering
12. Introducing Recommendation Systems
13. Introducing Natural Language Processing
14. Topic Modeling and Sentiment Analysis in NLP
15. Introducing Neural Networks
16. Advanced Deep Learning Models
17. Creating a Machine Learning Architecture