Pragmatic Machine Learning with Python

An easy-to-understand guide to learn practical Machine Learning techniques with Mathematical foundations

Key Features

● A balanced combination of underlying mathematical theories & practical examples with Python code
● Coverage of latest topics like multi-label classification, Text Mining, Doc2Vec, Word2Vec, XMeans clustering, unsupervised outlier detection, techniques to deploy ML models in production-grade systems with PMML, etc
● Coverage of sufficient & relevant visualization techniques specific to any topic

Description

This book will be ideal for working professionals who want to learn Machine Learning from scratch. The first chapter will be an introductory chapter to make readers comfortable with the idea of Machine Learning and the required mathematical theories. There will be a balanced combination of underlying mathematical theories corresponding to any Machine Learning topic and its implementation using Python. Most of the implementations will be based on ‘scikit-learn,’ but other Python libraries like ‘Gensim’ or ‘PyTorch’ will also be used for some topics like text analytics or deep learning. The book will be divided into chapters based on primary Machine Learning topics like Classification, Regression, Clustering, Deep Learning, Text Mining, etc. The book will also explain different techniques of putting Machine Learning models into production-grade systems using Big Data or Non-Big Data flavors and standards for exporting models.

What will you learn

● Get familiar with practical concepts of Machine Learning from ground zero
● Learn how to deploy Machine Learning models in production
● Understand how to do “Data Science Storytelling”
● Explore the latest topics in the current industry about Machine Learning

Who this book is for

This book would be ideal for experienced Software Professionals who are trying to get into the field of Machine Learning. Anyone who wishes to Learn Machine Learning concepts and models in the production lifecycle.

Table of Contents

1. Introduction to Machine Learning & Mathematical preliminaries
2. Classification
3. Regression
4. Clustering
5. Deep Learning & Neural Networks
6. Miscellaneous Unsupervised Learning
7. Text Mining
8. Machine Learning models in production
9. Case Studies & Data Science Storytelling