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.

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