Machine Learning Using R

Karthik Ramasubramanian, works for one of the largest and fastest growing technology unicorn in India, Hike Messenger. In his 7 years of research and industry experience, he has worked on cross-industry data science problems in retail, e-commerce, and technology, developing and prototyping data driven solutions. In his previous role at Snapdeal, one of the largest e-commerce retailers in India, he was leading core statistical modelling initiatives for customer growth and pricing analytics. Prior to Snapdeal, he was part of central database team, managing the data warehouses for global business applications of Reckitt Benckiser (RB). He has a Masters in Theoretical Computer Science from PSG College of Technology, Anna University and certified big data professional. He is passionate about teaching and mentoring future data scientists through different online and public forums. He also enjoys writing poems in his spare time and is an avid traveler. Abhishek Singh, is based in Ireland as a Data Scientist in the Advanced Data Science team for Prudential Financial Inc. He has 5 years of professional and academic experience in the Data Science field. At Deloitte Advisory, he led Risk Analytics initiatives for top US banks in their regulatory risk, credit risk, and balance sheet modelling requirements. In his current role, he is working on scalable machine learning algorithms for Individual Life Insurance branch of Prudential. He was also a trainer at Deloitte Professional University and development initiatives for professionals in the areas of statistics, economics, financial risk and data science tools (SAS and R). Abhishek is a B.Tech. in Mathematics and Computing from Indian Institute of Technology, Guwahati and has an MBA from Indian Institute of Management, Bangalore. He speaks at public events on Data Science and is working with leading universities towards bringing data science skills to graduates. He also holds a Post Graduate Diploma in Cyber Law from NALSAR University. He enjoys cooking and photography during his free hours. 

Chapter 1: Introduction to Machine Learning and R.- Chapter 2: Data Preparation and Exploration.- Chapter 3: Sampling and Resampling Techniques.- Chapter 4: Visualization of Data.- Chapter 5: Feature Engineering.- Chapter 6: Machine Learning Models: Theory and Practice.- Chapter 7: Machine Learning Model Evaluation.-Chapter 8: Model Performance Improvement.- Chapter 9: Scalable Machine Learning and related technology.-