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Applied Supervised Learning with R

Use machine learning libraries of R to build models that solve business problems and predict future trends

Author: Karthik Ramasubraman..., Published on 31-May-2019, Language: English


Learn the ropes of supervised machine learning with R by studying popular real-world use-cases, and understand how it drives object detection in driver less cars, customer churn, and loan default prediction. 

Key Features

  • Study supervised learning algorithms by using real-world datasets
  • Fine tune optimal parameters with hyperparameter optimization
  • Select the best algorithm using the model evaluation framework

Book Description

R provides excellent visualization features that are essential for exploring data before using it in automated learning.

Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. To prevent you from overfitting your model, a dedicated section will even demonstrate how you can add various regularization terms.

By the end of this book, you will have the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.

What you will learn

  • Develop analytical thinking to precisely identify a business problem
  • Wrangle data with dplyr, tidyr, and reshape2
  • Visualize data with ggplot2
  • Validate your supervised machine learning model using k-fold
  • Optimize hyperparameters with grid and random search, and Bayesian optimization
  • Deploy your model on Amazon Web Services (AWS) Lambda with plumber
  • Improve your model’s performance with feature selection and dimensionality reduction

Who this book is for

This book is specially designed for novice and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book.

Karthik Ramasubramanian

Karthik Ramasubramanian completed his M.Sc. in Theoretical Computer Science at PSG College of Technology, India, where he pioneered the application of machine learning, data mining, and fuzzy logic in his research work on computer and network security. He has over seven years' experience of leading data science and business analytics in retail, Fast-Moving Consumer Goods, e-commerce, information technology, and the hospitality industry for multinational companies and unicorn start-ups. He is a researcher and a problem solver with diverse experience of the data science life cycle, starting from data problem discovery to creating data science proof of concepts and products for various industry use cases. In his leadership roles, Karthik has been instrumental in solving many ROI-driven business problems via data science solutions. He has mentored and trained hundreds of professionals and students globally in data science through various online platforms and university engagement programs. He has also developed intelligent chatbots based on deep learning models that understand human-like interactions, customer segmentation models, recommendation systems, and many natural language processing models. He is an author of the book Machine Learning Using R, published by Apress, a publishing house of Springer Business+Science Media. The book was a big success with more than 50,000 online downloads and hardcover sales. The book was subsequently published as a second edition with extended chapters on Deep Learning and Time Series Modeling.