Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning
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

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.

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. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously.

On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem.

What you will learn

Acquaint yourself with important elements of Machine Learning
Understand the feature selection and 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
Implement clusters to a dataset
Explore the concept of Natural Processing Language and Recommendation Systems
Create a ML architecture from scratch.

Table of Contents

A Gentle Introduction to Machine Learning
Important Elements in Machine Learning
Feature Selection and Feature Engineering
Linear Regression
Logistic Regression
Naive Bayes
Support Vector Machines
Decision Trees and Ensemble Learning
Clustering Fundamentals
Hierarchical Clustering
Introduction to Recommendation Systems
Introduction to Natural Language Processing
Topic Modeling and Sentiment Analysis in NLP
A Brief Introduction to Deep Learning and TensorFlow
Creating a Machine Learning Architecture