Applied Supervised Learning with R
Use machine learning libraries of R to build models that solve business problems and predict future trends
Published on 23. September 2024
502 pages
978-1-83855-716-4 (ISBN)
Description
Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless 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 DescriptionR 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. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained 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 forThis book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.
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 DescriptionR 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. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained 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 forThis book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.
More details
Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
File size
23,21 MB
ISBN-13
978-1-83855-716-4 (9781838557164)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Karthik Ramasubramanian | Jojo Moolayil
Applied Supervised Learning with R
Use machine learning libraries of R to build models that solve business problems and predict future trends
Book
05/2019
Packt Publishing
€57.70
Shipment within 3-4 weeks
Persons
Ramasubramanian Karthik :
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.Moolayil Jojo :
Jojo Moolayil is an arti?cial intelligence, deep learning, machine learning, and decision science professional with over six years of industrial experience. He is the author of Learn Keras for Deep Neural Networks, published by Apress, and Smarter Decisions - The Intersection of IoT and Decision Science, published by Packt Publishing. He has worked with several industry leaders on high-impact, critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist in Canada. Apart from writing books on AI, decision science, and the internet of things, Jojo has been a technical reviewer for various books in the same ?elds published by Apress and Packt Publishing.
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.Moolayil Jojo :
Jojo Moolayil is an arti?cial intelligence, deep learning, machine learning, and decision science professional with over six years of industrial experience. He is the author of Learn Keras for Deep Neural Networks, published by Apress, and Smarter Decisions - The Intersection of IoT and Decision Science, published by Packt Publishing. He has worked with several industry leaders on high-impact, critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist in Canada. Apart from writing books on AI, decision science, and the internet of things, Jojo has been a technical reviewer for various books in the same ?elds published by Apress and Packt Publishing.
Content
Table of Contents
R for Advanced Analytics
Exploratory Analysis of Data
Introduction to Supervised Learning
Regression
Classification
Feature Selection and Dimensionality Reduction
Model Improvements
Model Deployment
Capstone Project - Based on Research Papers
R for Advanced Analytics
Exploratory Analysis of Data
Introduction to Supervised Learning
Regression
Classification
Feature Selection and Dimensionality Reduction
Model Improvements
Model Deployment
Capstone Project - Based on Research Papers
