Neural Networks with R : Build smart systems by implementing popular deep learning models in R

Balaji Venkateswaran

ISBN 10: 1788397878 / ISBN 13: 9781788397872

Edité par Packt Publishing, 2017

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guideKey FeaturesGet started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.Book DescriptionAs the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously.On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem.What You Will LearnAcquaint yourself with important elements of Machine LearningUnderstand the feature selection and feature engineering processAssess performance and error trade-offs for Linear RegressionBuild a data model and understand how it works by using different types of algorithmLearn to tune the parameters of Support Vector machinesImplement clusters to a datasetExplore the concept of Natural Processing Language and Recommendation SystemsCreate a ML architecture from scratch.Who This Book Is ForThis book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.

N° de réf. du vendeur 9781788397872

Détails bibliographiques

• Titre: Neural Networks with R : Build smart systems by implementing popular deep learning models in R
• Éditeur: Packt Publishing
• Date de parution: 2017
• Langue: anglais
• ISBN à 10 chiffres: 1788397878
• ISBN à 13 chiffres: 9781788397872
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide

Key Features

• Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.
• Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.
• Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.

Book Description

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.

In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously.

On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem.

What You Will Learn

• Acquaint yourself with important elements of Machine Learning
• Understand the feature selection and feature engineering process
• Assess performance and error trade-offs for Linear Regression
• Build a data model and understand how it works by using different types of algorithm
• Learn to tune the parameters of Support Vector machines
• Implement clusters to a dataset
• Explore the concept of Natural Processing Language and Recommendation Systems
• Create a ML architecture from scratch.

Who This Book Is For

This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.

À propos des auteurs

Balaji Venkateswaran is an AI expert, data scientist, machine learning practitioner, and database architect. He has 17+ years of experience in investment banking payment processing, telecom billing, and project management. He has worked for major companies such as ADP, Goldman Sachs, MasterCard, and Wipro. Balaji is a trainer in data science, Hadoop, and Tableau. He holds a postgraduate degree PG in business analytics from Great Lakes Institute of Management, Chennai. Balaji has expertise relating to statistics, classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures. His main interests are neural networks and deep learning. Balaji holds various certifications in IBM SPSS, IBM Watson, IBM big data architect, cloud architect, CEH, Splunk, Salesforce, Agile CSM, and AWS. If you have any questions, don't hesitate to message him on LinkedIn (balvenkateswaran); he will be more than glad to help fellow data scientists.

Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Università degli Studi della Campania Luigi Vanvitelli, Italy. He has over 18 years' professional experience in programming (Python, R, and MATLAB), first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.