Practical Machine Learning Cookbook
Supervised and unsupervised machine learning simplified
Published on 8. July 2025
570 pages
978-1-78528-653-7 (ISBN)
Description
Building Machine Learning applications with R
Key Features
Implement a wide range of algorithms and techniques for tackling complex data
Improve predictions and recommendations to have better levels of accuracy
Optimize performance of your machine-learning systems
Book DescriptionMachine learning has become the new black. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you'll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one. What you will learn
[*] Get equipped with a deeper understanding of how to apply machine-learning techniques
[*] Implement each of the advanced machine-learning techniques
[*] Solve real-life problems that are encountered in order to make your applications produce improved results
[*] Gain hands-on experience in problem solving for your machine-learning systems
[*] Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model's performance, and improving the model's performance
Who this book is forThis book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected.
Key Features
Implement a wide range of algorithms and techniques for tackling complex data
Improve predictions and recommendations to have better levels of accuracy
Optimize performance of your machine-learning systems
Book DescriptionMachine learning has become the new black. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you'll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one. What you will learn
[*] Get equipped with a deeper understanding of how to apply machine-learning techniques
[*] Implement each of the advanced machine-learning techniques
[*] Solve real-life problems that are encountered in order to make your applications produce improved results
[*] Gain hands-on experience in problem solving for your machine-learning systems
[*] Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model's performance, and improving the model's performance
Who this book is forThis book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected.
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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
16,89 MB
ISBN-13
978-1-78528-653-7 (9781785286537)
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
Atul Tripathi
Practical Machine Learning Cookbook
Book
03/2017
Packt Publishing
€76.20
Shipment within 3-4 weeks
Person
Tripathi Atul :
Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.
Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.
Content
Table of Contents
Introduction
Classification
Clustering
Model Selection and Regularization
Non-linearity
Supervised Learning
Unsupervised Learning
Reinforcement learning
Structured prediction
Neural Networks
Deep Learning
Case Study 1
Case Study 2
Case Study 3
Introduction
Classification
Clustering
Model Selection and Regularization
Non-linearity
Supervised Learning
Unsupervised Learning
Reinforcement learning
Structured prediction
Neural Networks
Deep Learning
Case Study 1
Case Study 2
Case Study 3
