An easytofollow, stepbystep guide for getting to grips with the realworld application of machine learning algorithms
Key Features

Explore statistics and complex mathematics for dataintensive 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, semisupervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore realworld examples based on the most diffused libraries, such as scikitlearn, 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 tradeoffs 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.
Table of Contents

A Gentle Introduction to Machine Learning

Important Elements in Machine Learning

Feature Selection and Feature Engineering

Regression Algorithms

Linear Classification Algorithms

Naive Bayes and Discriminant Analysis

Support Vector Machines

Decision Trees and Ensemble Learning

Clustering Fundamentals

Advanced Clustering

Hierarchical Clustering

Introducing Recommendation Systems

Introducing Natural Language Processing

Topic Modeling and Sentiment Analysis in NLP

Introducing Neural Networks

Advanced Deep Learning Models

Creating a Machine Learning Architecture