Overview
- Compares different machine learning framework implementations for each topic
- Covers Reinforcement Learning and Convolutional Neural Networks
- Explains best practices for model tuning for better model accuracy
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Table of contents (7 chapters)
Keywords
About this book
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.
You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data.
Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.
What You'll Learn
- Understand machine learning development and frameworks
- Assess model diagnosis and tuning in machine learning
- Examine text mining, natuarl language processing (NLP), and recommender systems
- Review reinforcement learning and CNN
Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Mastering Machine Learning with Python in Six Steps
Book Subtitle: A Practical Implementation Guide to Predictive Data Analytics Using Python
Authors: Manohar Swamynathan
DOI: https://doi.org/10.1007/978-1-4842-4947-5
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Manohar Swamynathan 2019
Softcover ISBN: 978-1-4842-4946-8Published: 02 October 2019
eBook ISBN: 978-1-4842-4947-5Published: 01 October 2019
Edition Number: 2
Number of Pages: XVII, 457
Number of Illustrations: 184 b/w illustrations, 1 illustrations in colour
Topics: Artificial Intelligence, Big Data, Open Source