Machine Learning with R

Machine Learning, Artificial Intelligence, Big Data... too many terms and too many books..

While SAS has been predominant tool for data science and analytics since 1980s and became mainstream tool in 2005; in 2022 SAS is facing the big competition from open source tools like Python and R. While Jim Goodnight - CEO of SAS have announced an IPO for 2024 ( https://www.sas.com/en_us/news/press-releases/2021/july/sas-charts-path-to-ipo-readiness.html ) and partnered with Microsoft on SAS Viya; a lot of student population and new age data scientists remain very loyal to R. The loyalty has been increasing steadily and one proof of the loyalty is Microsoft had to acquire RevolutionR in 2013 for a steep price.

While R is becoming a popular tool in data science and analytics, there are too many resources on R which can be confusing for a lot of users. So, what R book do we suggest for learning machine learning and data science? 

We highly suggest Machine Learning in R by Kumar Rahul and U. Dinesh Kumar. This book includes all the datasets and the R codes you need for learning the Machine Learning concepts with a practical approach. 

Some of the chapters on machine learning methods such as XGBoost are really helpful to the readers. Prof. Dinesh Kumar teaches at IIM Bangalore and writes with ease about the machine learning concepts. Another reason to read the book. U Dinesh Kumar’s research interests include Business Analytics and Big Data, Artificial Intelligence, Machine Learning, Deep Learning Algorithms, Stochastic models (Reinforcement Learning Algorithms), Reliability, Optimization, Six Sigma and Performance Based Logistics. He has published several research articles in reputed academic journals such as European Journal of Operational Research, Annals of Operations Research, International Journal of Production Economics, The Journal of Operational Research Society, Computers and Operations Research, IEEE Transactions on Reliability, International Journal of Reliability, Quality and Safety Engineering and so on.

He has published more than thirty case studies on Business Analytics and Machine Learning Algorithms based on Indian and multinational organizations such as Aavin Milk Dairy, Akshaya Patra Foundation, Apollo Hospitals, Bigbasket, Bollywood, Flipkart.com, Hewlett and Packard, iD Fresh Food, Larsen & Toubro, Manipal Hospitals, Mission Hospital, Hindustan Aeronautics Limited, Indian Premier League, Shubham Housing Development Finance Company and VMWare at the Harvard Business Publishing’s case portal.

Would you recommend this book to your friends and family? 

We asked this question to 1000 R users. 73% have mentioned that this is a great book for anyone who is trying to learn R for the first time. While the NPS in the US is lower (around 47%), some parts such as California with the age bracket of 20-30, have mentioned that this book brings amazing clarity of thinking when working on R and Data Science. The NPS in Asia (India, Pakistan, Bangladesh, Nepal) is around 52% while the real surprize is Japan where 87% of the R users absolutely liked the book. 

Another surprize is overall NPS by the female population was 89% as compared to the NPS by male population which was around 57%. 

So, a great book if you are planning to study R and data science for 2023 (unless you are planning to attend lectures by Professor U. Dinesh Kumar at IIM Bangalore). 

Apart from this book, the books which we highly recommend is byData Analytics with R by Bharti Motwani and R for Everyone: Advanced Analytics and Graphics by Jared P. Lander