Pragmatic Machine Learning with Python: Learn How to Deploy Machine Learning Models in Production

  • 3h 48m
  • Avishek Nag
  • BPB Publications
  • 2020

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

Key Features

  • A balanced combination of underlying mathematical theories and 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 and relevant visualization techniques specific to any topic


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.

About the Author

Avishek has a Master’s degree in Data Analytics and Machine Learning from BITS (Pilani) and a Bachelor’s degree in Computer Science from West Bengal University of Technology (WBUT). He has more than 14 years of experience in different renowned companies like VMware, Cognizant, Cisco, Mobile Iron, etc. He started his career as a Java developer and later moved to the core area of Machine Learning around five years back. He has practical experience in the design and development of Machine Learning systems, starting from inception to production in multiple organizations. Strong foundations in Mathematics/Statistics and a solid experience in product development had helped him to excel quickly in the world of ML and Data Science. He has shared his knowledge and experience through this book, which can help any Software Engineer to kick start in this area.

In this Book

  • Introduction to Machine Learning and Mathematical Preliminaries
  • Classification
  • Regression
  • Clustering
  • Deep Learning
  • Miscellaneous Unsupervised Learning
  • Text Mining
  • Machine Learning Models in Production
  • Case Studies and Storytelling