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Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning

Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning

Machine Learning Algorithms: A reference guide to popular algorithms for data
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Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning Paperback - 2017

by Bonaccorso, Giuseppe

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Packt Publishing, 2017-07-24. paperback. New. 7.50x0.75x9.25. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning
  • Author Bonaccorso, Giuseppe
  • Binding Paperback
  • Condition New
  • Pages 360
  • Volumes 1
  • Language ENG
  • Publisher Packt Publishing
  • Publication date 2017-07-24
  • Bookseller's Inventory # DADAX1785889621
  • ISBN 9781785889622 / 1785889621
  • Weight 1.36 lbs (0.62 kg)
  • Dimensions 9.25 x 7.5 x 0.75 in (23.50 x 19.05 x 1.91 cm)
  • Size 7.50x0.75x9.25
  • Category Computers - Languages / Programming
  • Quantity available 1

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From the publisher

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:

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. The algorithms that are covered in this book are linear regression, logistic regression, SVM, nave Bayes, k-means, random forest, TensorFlow and feature engineering.


In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.


On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem



What You Will Learn:

- Acquaint yourself with the important elements of machine learning

- Understand the feature selection and feature engineering processes

- Assess performance and error trade-offs for linear regression

- Build a data model and understand how it

- Learn to tune the parameters of SVMs

- Implement clusters in a dataset

- Explore the concept of Natural Processing Language and Recommendation Systems

- Create a machine learning 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.

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