Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python
- 出版商: Apress
- 出版日期: 2017-06-07
- 定價: USD $44.99
- 售價: $1,710
- 貴賓價: 9.8 折 $1,676
- 語言: 英文
- 頁數: 358
- 裝訂: Paperback
- ISBN: 1484228650
- ISBN-13: 9781484228654
立即出貨 (庫存 < 3)
貴賓價: $1,710A First Course in Machine Learning, 2/e (Hardcover)
貴賓價: $1,103Python Power!: The Comprehensive Guide
售價: $1,120Interactive Data Visualization for the Web (Paperback)
貴賓價: $1,496Swift Programming: The Big Nerd Ranch Guide (2nd Edition) (Big Nerd Ranch Guides)
貴賓價: $998Invent Your Own Computer Games with Python, 4/e (Paperback)
貴賓價: $855The Hitchhiker's Guide to Python: Best Practices for Development (Paperback)
貴賓價: $1,995Python Data Science Handbook: Essential Tools for Working with Data (Paperback)
貴賓價: $1,330R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Paperback)
貴賓價: $1,676Make Your Own Neural Network (Paperback)
貴賓價: $2,826Foundations of Machine Learning (Hardcover)
貴賓價: $1,881R in Action: Data Analysis and Graphics with R, 2/e (Paperback)
Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner.
This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages.
You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you’ll explore advanced text mining techniques, neural networks and deep 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.
- Examine the fundamentals of Python programming language
- Review machine Learning history and evolution
- Understand machine learning system development frameworks
- Implement supervised/unsupervised/reinforcement learning techniques with examples
- Explore fundamental to advanced text mining techniques
- Implement various deep learning frameworks
Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python.
Novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and basics of reinforcement learning.