Mastering Machine Learning with Python in Six Steps, 2nd Edition

eBook Details:

• Paperback: 457 pages
• Publisher: WOW! eBook; 2nd edition (October 15, 2019)
• Language: English
• ISBN-10: 1484249461
• ISBN-13: 978-1484249468

eBook Description:

Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python, 2nd Edition: Covers Reinforcement Learning and Convolutional Neural Networks

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.

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

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 Mastering Machine Learning with Python in Six Steps, 2nd Edition book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.