Machine Learning Algorithms - Second Edition

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

Machine Learning Algorithms - Second Edition

Giuseppe Bonaccorso
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4 customer reviews
An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms
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Book Details

ISBN 139781789347999
Paperback522 pages

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.

Table of Contents

Chapter 14: Topic Modeling and Sentiment Analysis in NLP
Chapter 15: Introducing Neural Networks

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

Authors

Table of Contents

Chapter 14: Topic Modeling and Sentiment Analysis in NLP
Chapter 15: Introducing Neural Networks

Book Details

ISBN 139781789347999
Paperback522 pages
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From 4 reviews

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