Machine Learning Algorithms

A reference guide to popular algorithms for data science and machine learning

Publisher:Packt Publishing Limited

Paid access

|Aug 2017

E-Book €32.99Institutions €32.99

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide

Key Features

Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.
Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.
Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.

Book Description

In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.

In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.

On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem

What you will learn

Acquaint yourself with the important elements of machine learning
Understand the feature selection and feature engineering processes
Assess performance and error trade-offs for linear regression
Build a data model and understand how it
Learn to tune the parameters of SVMs
Implement clusters in a dataset
Explore the concept of Natural Processing Language and Recommendation Systems
Create a machine learning architecture from scratch

Who this book is for

This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.

Gentle Introduction To Machine Learning
Important Elements In A Machine Learning
Feature Selection & Feature Engineering
Linear Regression
Logistic Regression
Na
Support Vector Machines
Decision Trees And Random Forests
K-Means
Heirarchical Clustering
Introduction To Recommedation Systems
Introduction To Natural Language Processing
Topic Modelling and Sentiment Analysis in NLP
Brief Introduction To Deep Learning And Tensorflow
Creating a Machine Learning Architecture

https://github.com/packtpublishing/machine-learning-algorithms

PDF ISBN: 978-1-78588-451-1

Publisher: Packt Publishing Limited

Publication date: 2017

Language: English

Pages: 360

Related subjects:

Computer sciences,

Algorithms

Machine Learning Algorithms

Key Features

Book Description

What you will learn

Who this book is for

Table of Contents

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