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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 About the Author Avishek has a Master’s degree in Data Analytics & 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 & 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 & Data Science. He has shared his knowledge & experience through this book, which can help any Software Engineer to kick start in this area. He also writes blogs, and the same can be found at https://medium.com/@avisheknag17 Your Blog links: https://medium.com/@avisheknag17 Your LinkedIn Profile https://www.linkedin.com/in/avishek-nag-957a0015/