This book covers machine learning, one of the hottest topics in more recent years. With computing power increasing exponentially and costs decreasing at the same time, there is no better time for machine learning. Machine learning tasks that usually require huge processing power are now possible on desktop machines. However, machine learning is not for the faint of heart – it requires a good foundation in statistics, as well as programming knowledge. This code-intensive book encourages readers to try out various examples of both topics which are designed to be compact, yet easy to follow and understand. Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how machine learning is useful to solving the problem at hand.

This book will get readers started in Python Machine Learning by covering the following fundamental topics: 

Introduction to Machine Learning 
Machine Learning algrothims 

• Regression
• Classifications
• Clustering
• Anomaly Detection 

Deploying Machine Learning Models as Web Services 
Introduction to Python Data Science 
Python Libraries for data science 

• Numpy
• Pandas
• Matplotib

Getting Started with Scikit-learn