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

by Giuseppe Bonaccorso

Overview

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.

Product Details

ISBN-13:	9781789345483
Publisher:	Packt Publishing
Publication date:	08/30/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	522
File size:	83 MB
Note:	This product may take a few minutes to download.

About the Author

Giuseppe Bonaccorso is an experienced team leader/manager in AI, machine/deep learning solution design, management, and delivery. He got his MScEng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, cryptocurrencies, and NLP.

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