Machine learning algorithms: reference guide for popular algorithms for data science and machine learning
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book 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...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing
2017
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781785889622/?ar |
| Zusammenfassung: | Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book 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. 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. 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. In Detail 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... |
| Beschreibung: | Includes bibliographical references at the end of each chapters and index. - Online resource; title from title page (Safari, viewed August 17, 2017) |
| Umfang: | 1 Online-Ressource (1 volume) illustrations |
| ISBN: | 9781785884511 1785884514 9781523112210 1523112212 9781785889622 |
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| 520 | |a Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book 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. 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. 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. In Detail 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... | ||
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| spelling | Bonaccorso, Giuseppe VerfasserIn aut Machine learning algorithms reference guide for popular algorithms for data science and machine learning Giuseppe Bonaccorso Reference guide for popular algorithms for data science and machine learning Birmingham, UK Packt Publishing 2017 1 Online-Ressource (1 volume) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references at the end of each chapters and index. - Online resource; title from title page (Safari, viewed August 17, 2017) Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book 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. 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. 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. In Detail 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... Machine learning Computer algorithms Algorithms Machine Learning Apprentissage automatique Algorithmes algorithms COMPUTERS ; Programming ; Algorithms COMPUTERS ; Data Processing COMPUTERS ; Programming Languages ; Python 9781785889622 Erscheint auch als Druck-Ausgabe 9781785889622 |
| spellingShingle | Bonaccorso, Giuseppe Machine learning algorithms reference guide for popular algorithms for data science and machine learning Machine learning Computer algorithms Algorithms Machine Learning Apprentissage automatique Algorithmes algorithms COMPUTERS ; Programming ; Algorithms COMPUTERS ; Data Processing COMPUTERS ; Programming Languages ; Python |
| title | Machine learning algorithms reference guide for popular algorithms for data science and machine learning |
| title_alt | Reference guide for popular algorithms for data science and machine learning |
| title_auth | Machine learning algorithms reference guide for popular algorithms for data science and machine learning |
| title_exact_search | Machine learning algorithms reference guide for popular algorithms for data science and machine learning |
| title_full | Machine learning algorithms reference guide for popular algorithms for data science and machine learning Giuseppe Bonaccorso |
| title_fullStr | Machine learning algorithms reference guide for popular algorithms for data science and machine learning Giuseppe Bonaccorso |
| title_full_unstemmed | Machine learning algorithms reference guide for popular algorithms for data science and machine learning Giuseppe Bonaccorso |
| title_short | Machine learning algorithms |
| title_sort | machine learning algorithms reference guide for popular algorithms for data science and machine learning |
| title_sub | reference guide for popular algorithms for data science and machine learning |
| topic | Machine learning Computer algorithms Algorithms Machine Learning Apprentissage automatique Algorithmes algorithms COMPUTERS ; Programming ; Algorithms COMPUTERS ; Data Processing COMPUTERS ; Programming Languages ; Python |
| topic_facet | Machine learning Computer algorithms Algorithms Machine Learning Apprentissage automatique Algorithmes algorithms COMPUTERS ; Programming ; Algorithms COMPUTERS ; Data Processing COMPUTERS ; Programming Languages ; Python |
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