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Applied Supervised Learning with R

You're reading from Applied Supervised Learning with R

Product type Book

Published in May 2019

Publisher

ISBN-13 9781838556334

Pages 502 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Authors (2):

Karthik Ramasubramanian

Jojo Moolayil

View More author details

Table of Contents (12) Chapters

Applied Supervised Learning with R

Preface

1. R for Advanced Analytics

2. Exploratory Analysis of Data

3. Introduction to Supervised Learning

4. Regression

5. Classification

6. Feature Selection and Dimensionality Reduction

7. Model Improvements

8. Model Deployment

9. Capstone Project - Based on Research Papers

Appendix

What is an API?

Before delving into the specifics of model deployment, we need to study an important software engineering topic that simplifies the entire process of model deployment, that is, an Application Program Interface, commonly referred to as an API. An API is a set of clearly defined methods for communication between various software components. Software development has been made significantly easier with the advent of APIs. If a developer, say, wanted to develop an iPhone app that would add some filters to an image, they need not write the entire code to capture the image from the phone's camera, save it to the library, and then apply their app-specific filters to it. Instead, they can use the phone camera API, which provides an easy way to communicate with the camera and only focus on writing code that would add filters to an image. In a nutshell, an API is the means for heterogeneous software components to communicate with each other.

In a large software product, there would be...

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Authors (2)

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.

See other products by Karthik Ramasubramanian

Jojo Moolayil

Jojo Moolayil is a data scientist, living in Bengaluru—the silicon valley of India. With over 4 years of industrial experience in Decision Science and IoT, he has worked with industry leaders on high impact and critical projects across multiple verticals. He is currently associated with GE, the pioneer and leader in data science for Industrial IoT. Jojo was born and raised in Pune, India and graduated from University of Pune with a major in information technology engineering. With a vision to solve problems at scale, Jojo found solace in decision science and learnt to solve a variety of problems across multiple industry verticals early in his career. He started his career with Mu Sigma Inc., the world's largest pure play analytics provider where he worked with the leaders of many fortune 50 clients. With the passion to solve increasingly complex problems, Jojo touch based with Internet of Things and found deep interest in the very promising area of consumer and industrial IoT. One of the early enthusiasts to venture into IoT analytics, Jojo converged his learnings from decision science to bring the problem solving frameworks and his learnings from data and decision science to IoT. To cement his foundations in industrial IoT and scale the impact of the problem solving experiments, he joined a fast growing IoT Analytics startup called Flutura based in Bangalore and headquartered in the valley. Flutura focuses exclusively on Industrial IoT and specializes in analytics for M2M data. It is with Flutura, where Jojo reinforced his problem solving skills for M2M and Industrial IoT while working for the world's leading manufacturing giant and lighting solutions providers. His quest for solving problems at scale brought the 'product' dimension in him naturally and soon he also ventured into developing data science products and platforms. After a short stint with Flutura, Jojo moved on to work with the leaders of Industrial IoT, that is, G.E. in Bangalore, where he focused on solving decision science problems for Industrial IoT use cases. As a part of his role in GE, Jojo also focuses on developing data science and decision science products and platforms for Industrial IoT.

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