Machine Learning using Python

This book is written to provide a strong foundation in machine learning using Python libraries by providing real-life case studies and examples. It covers topics such as foundations of machine learning, introduction to Python, descriptive analytics and predictive analytics. Advanced machine learning concepts such as decision tree learning, random forest, boosting, recommended systems, and text analytics are covered. The book takes a balanced approach between theoretical understanding and practical applications. All the topics include real-world examples and provide step-by-step approach on how to explore, build, evaluate, and optimize machine learning models.

Authors

Professor U Dinesh Kumar

Professor U Dinesh Kumar, is a professor of Decision Sciences and Chair of Excellence at the Indian Institute of Management Bangalore. Dr Dinesh Kumar has over 25 years of teaching and research experience. Prior to joining IIM Bangalore, Dr Dinesh Kumar has worked at several reputed Institutes across the world including Stevens Institute of Technology, USA; University of Exeter, UK; University of Toronto, Canada; Federal Institute of Technology (ETH), Zurich, Switzerland; Queensland University of Technology, Australia; Australian National University, Australia and the Indian Institute of Management Calcutta.

Recognised as one of the Top 10 Most Prominent Analytic Academicians in India, his main research and teaching interests are Business Analytics and Artificial Intelligence, and he has published 38 case studies at the Harvard Business Publishing on data science and machine learning, Nine of his case studies are best-sellers at the Harvard Business Publishing case portal. He has authored more than 70 research articles, and two books. His books, Business Analytics – The Science of Data-Driven Decision-Making and Machine Learning using Python are Amazon India best-sellers.

Dr Dinesh Kumar has conducted training program on Analytics for several companies such as Accenture, Aditya Birla Group, Ashok Leyland, Asian Paints, Bank of America, Blue Ocean Market Intelligence, Cisco, Fidelity, Hindustan Aeronautics Limited, Honeywell, Infosys, ITC Info Tech, Madya Pradesh Agency for Promotion of Information Technology (MAPIT), National Academy for Defence Production, Ocwen financial Services, SONY and so on. Dr Dinesh Kumar conducts corporate training programme in Analytics and trained more than 1000 professionals in the field of analytics.

Manaranjan Pradhan

Manaranjan Pradhan, an IIM Bangalore alumnus, has 20-plus years of industry experience working on Big Data & Machine Learning. He has worked with TCS, HP, and iGATE and worked on large scale project implementations for customers like Motorola, Home Depot, CKWB Bank and P&G in the roles of solution and technical architect. He is a freelancer who provides consulting and training on Big data and Data Science including Machine Learning. He has been teaching Big Data and Machine Learning for more than seven years and has trained more than 1,000 people from several large MNCs including EMC, CISCO, TESCO, HP, YODLEE, Goldman Sachs, Software AG, Amadeus, Cognizant, Cap Gemini and Accenture. He has co-authored the best-selling book Machine Learning using Python.

He has published a case on Harvard Business Publishing: A: Customer Analytics at Big Basket – Product Recommendations B. Improving Lead Generation at Eureka Forbes Using Machine Learning Algorithms.

Excellent book for data scientist new to Python. The book provides code examples on a variety of topics starting from probability distribution and many machine learning algorithms. The examples are well choosen and embibes the core concepts of data science and reflects years of teaching experience of both Prof Dinesh and Manaranjan. I highly recommend it as a reference book to look up for codes snippets while working on ML projects.

Great coverage of basic statistics, hypothesis testing, Regression techniques. one of the few books which covers these topics in python, since these were traditionally done in other tools such as SPSS,SAS or R. Has many solved case studies which makes it easy to understand and apply the ML techniques. Recommend this book for beginners and intermediate data scientists

Know More