Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R
Language: English
Published by Packt Publishing, 2017
- Softcover
- Used
Seller: Goodwill of Silicon Valley, SAN JOSE, CA, U.S.A.Goodwill of Silicon Valley
AbeBooks seller since June 28, 2024
Condition: Used - Good
£ 6.08
Quantity: 1 available
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Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.
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- Title
- Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R
- Author
- Pratap Dangeti
- Publisher
- Packt Publishing
- Publication year
- 2017
- Condition
- good
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1788295757
- ISBN 13
- 9781788295758
Build Machine Learning models with a sound statistical understanding.
Key Features:
- Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.
- Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.
- Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.
Book Description:
Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement.
This book will teach you all it takes to perform the complex statistical computations that are required for machine learning. You will gain information on the statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and familiarize yourself with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more.
By the end of the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.
What You Will Learn:
- Understand the statistical and machine learning fundamentals necessary to
- build models
- Understand the major differences and parallels between the statistical way and the machine learning way to solve problems
- Learn how to prepare data and feed models by using the appropriate machine learning algorithms from the more-than-adequate R and Python packages
- Analyze the results and tune the model appropriately to your own predictive goals
- Understand the concepts of the statistics required for machine learning
- Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models
- Learn reinforcement learning and its application in the field of artificial intelligence domain
Who this book is for:
This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
Goodwill of Silicon Valley
SAN JOSE, CA, U.S.A.
AbeBooks seller since June 28, 2024
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Goodwill of Silicon Valley’s mission is to support our employees, our customers, and people with challenging barriers to employment; to raise their standard of living and improve their lives through our services and social enterprise.
Founded in Santa Clara County, California, USA in 1928, Goodwill of Silicon Valley is dedicated to improving employment opportunities, increasing standards of living, providing economic independence, and restoring our clients’ sense of self-worth. We do this through workforce creation, vocational training, and environmental stewardship. With 18 retail stores, an online store, an extensive reuse/recycling operation, and our Contract Services division, we help people overcome barriers to employment, build sustainable livelihoods, and transform their lives and communities.
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