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.
What You'll Learn
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
Who This Book Is For
Python developers or data engineers looking to expand their knowledge or career into machine learning area.
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.
Table of contents
- 1. Step 1 – Getting Started in Python
- 2. Step 2 – Introduction to Machine Learning
- 3. Step 3 – Fundamentals of Machine Learning
- 4. Step 4 – Model Diagnosis and Tuning
- 5. Step 5 – Text Mining and Recommender Systems
- 6. Step 6 – Deep and Reinforcement Learning
- 7. Conclusion
- Title: Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python
- Release date: July 2017
- Publisher(s): Apress
- ISBN: 9781484228661
You might also like
Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud
This is the eBook of the printed book and may not include any media, website access …
40 Algorithms Every Programmer Should Know
Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental …
Machine Learning Algorithms - Second Edition
An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms …
51+ hours of video instruction. Overview The professional programmer’s Deitel® video guide to Python development with …