Description Examine the latest technological advancements in building a scalable machine-Learning model With big data Using R.
He has designed, developed and spearheaded many A/B experiment frameworks for improvi.
He has mentored and trained hundreds of professionals and students around the world through various online platforms and university engagement programs in data science.
In his leadership roles, he has been instrumental in solving many ROI-driven business problems through data science solutions.
A researcher and problem solver With a diverse set of experience in the data science life cycle, starting from a data problem discovery to creating data science Po Cs and products for various industry use cases.
About the author Karthik Ramasubramanian has over seven years\' experience leading data science and business analytics in retail, FMCG, e-commerce, information technology and hospitality for multi-national companies and unicorn startups.
What You\'ll Learn Understand Machine Learning algorithms Using RMaster the process of building machine-Learning models Cover the theoretical foundations of machine-Learning algorithms See industry focused real-world use Cases Tackle Time Series modeling in RApply deep Learning Using Keras and Tensor Flow in RWho This Book is For Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-Learning approaches/algorithms in practice Using R.
New chapters in this edition cover Time Series models and deep learning.
As in the first edition, the authors have kept the fine balance of theory and application of Machine Learning through various real-world use-Cases which gives you a comprehensive collection of topics in Machine learning.
You will see how to use R programming With Tensor Flow, thus avoiding the effort of Learning Python if you are only comfortable With R.
This second edition shows you how to work With a machine-Learning algorithm and use it to build a ML model from raw data.
Description Examine the latest technological advancements in building a scalable machine-Learning model With big data Using R
