McGraw Hill Education Introduces Two New Books

McGraw Hill Education recently launched two books, namely, ‘Applied Machine Learning’ and ‘Data Analytics Using R’. ‘Applied Machine Learning’ a self-study guide for machine learning projects authored by M. Gopal, an Ex-Professor of IIT Delhi. He is a recognized researcher in the area of Machine Learning. ‘Data Analytics Using R’ is a comprehensive...

McGraw Hill Education recently launched two books, namely, ‘Applied Machine Learning’ and ‘Data Analytics Using R’. ‘Applied Machine Learning’ a self-study guide for machine learning projects authored by M. Gopal, an Ex-Professor of IIT Delhi.

He is a recognized researcher in the area of Machine Learning. ‘Data Analytics Using R’ is a comprehensive guide for IT professionals authored by Seema Acharya, a Senior Lead Principal with the Education, Training and Assessment department of Infosys Limited.

She is an established author with several books in her kitty. Her approach is to keep the reader first, taking the learners through the concept of data analysis and providing practical experience in extracting deep and useful insights from data.

With the inclusion of new technologies such as AI, Robotics, Machine Learning (ML), Internet of Things (IoT) in the new curriculum prepared by AICTE, artificial intelligence is becoming an integral part of education on campuses. The books like Applied Machine Learning provide the right tools for enhancing students’ knowledge as per the changing industry needs.

Applied Machine Learning, covers all the fundamentals and theoretical concepts and presents a wide range of techniques (algorithms) applicable to challenges in our day-to-day lives.

The book recognizes that most of the ideas behind machine learning are simple and straightforward. It provides a platform for hands-on experience through self-study machine learning projects.

This is a comprehensive textbook on machine learning for undergraduates in computer science and all engineering degree programs. While the book serves as a useful initial exposure tool to postgraduates and research scholars before they go for highly theoretical depth in the specific areas of their research, it builds the foundation of machine learning for engineers, scientists, business managers and other practitioners.

Key Features:

  • Covers a broad array of algorithms
  • Datasets demonstrating real-life challenges like Breast Cancer Diagnosis, Optical Recognition of Handwritten Digits, Bank Telemarketing and Forecasting Stock Market Index Changes
  • Concepts and techniques presented in a non-rigorous mathematical setting
  • Nearly 200 problem exercises

Data Analytics Using R is a comprehensive and useful companion for IT professionals to data analysts and decision makers responsible for driving strategic initiatives, and management graduates and business analysts, engaged in self-study.

Key Features:

  • Exhaustive coverage includes installation of R and its package, getting accustomed to R interface and R commands, working with data from disparate data sources (.csv, JSON, XML, RDBMS etc.), getting conversant with classification, clustering, association rule mining, regression, text mining etc.
  • 12 Case studies namely Insurance Fraud Detection, Customer Insights Analysis, Sales Forecasting, Credit Card Spending by Customer Groups and Helping Retailers Predict In-store Customer Traffic
  • Pedagogy
    • 300+ chapter-end and check your progress questions for self-assessment
    • 200 Multiple-choice questions
    • 10+ hands-on practical exercises
    • Exhaustive illustrations
Source: www.pcquest.com