Overview
- Exposes readers to running a large-scale model in a cloud environment
- Covers all major machine learning algorithms with theory along with case studies including the vast majority of algorithms used in industry
- Algorithm models are implemented both in Python and R
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
About this book
You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers.
You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence.
What You Will Learn
- Get an in-depth understanding of all the major machine learning and deep learning algorithms
- Fully appreciate the pitfalls to avoid while building models
- Implement machine learning algorithms in the cloud
- Follow a hands-on approach through case studies for each algorithm
- Gain the tricks of ensemble learning to build more accurate models
- Discover the basics of programming in R/Python and the Keras framework for deep learning
Who This Book Is For
Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.
Authors and Affiliations
About the author
V Kishore Ayyadevara currently leads retail analytics consulting in a start-up. He received his MBA from IIM Calcutta. Following that, he worked for American Express in risk management and in Amazon's supply chain analytics teams. He is passionate about leveraging data to make informed decisions - faster and more accurately. Kishore's interests include identifying business problems that can be solved using data, simplifying the complexity within data science and applying data science to achieve quantifiable business results.
Bibliographic Information
Book Title: Pro Machine Learning Algorithms
Book Subtitle: A Hands-On Approach to Implementing Algorithms in Python and R
Authors: V Kishore Ayyadevara
DOI: https://doi.org/10.1007/978-1-4842-3564-5
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: V Kishore Ayyadevara 2018
Softcover ISBN: 978-1-4842-3563-8Published: 01 July 2018
eBook ISBN: 978-1-4842-3564-5Published: 30 June 2018
Edition Number: 1
Number of Pages: XXI, 372
Number of Illustrations: 359 b/w illustrations
Topics: Artificial Intelligence, Python, Big Data, Open Source