Python Machine Learning
This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.
What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning. My mission was to not treat algorithms as a black box, provide the necessary math intuition in the most accessible way, and provide code examples to put the learned material into action.
Knowledge is gained by learning, the key is our enthusiasm, but the true mastery of skills can only be achieved by practice.
The focus of this book will help you to understand machine learning concepts and algorithms. We will implement algorithms from scratch in Python and NumPy to complement our learning experience, go over many examples using scikit-learn for our own convenience, and optimize our code via Theano and Keras for neural network training on GPUs.
Paperback: 454 pages, ebook
Packt Publishing Ltd. (September 24th, 2015)
- The book’s GitHub repository with code examples, table of contents, and additional information
- Python Machine Learning at Amazon.com, PacktPub, Google Books, Safari Books, Apple iBooks, O’Reilly …
- Literature References & Further Reading Resources
- The Foreword by Dr. Randal Olson
- PDF and LaTeX Equation Reference
- Reviews & Feedback
Sebastian Raschka’s new book, Python Machine Learning, has just been released. I got a chance to read a review copy and it’s just as I expected - really great! It’s well organized, super easy to follow, and it not only offers a good foundation for smart, non-experts, practitioners will get some ideas and learn new tricks here as well.
– Lon Riesberg at Data Elixir
Superb job! Thus far, for me it seems to have hit the right balance of theory and practice…math and code!
– Brian Thomas
I’ve read (virtually) every Machine Learning title based around scikit-learn and this is hands-down the best one out there.
– Jason Wolosonovich
If you need help to decide whether this book is for you, check out some of the “longer” reviews linked below. (If you wrote a review, please let me know, and I’d be happy to add it to the list).
Sebastian Raschka created an amazing machine learning tutorial which combines theory with practice. The book explains machine learning from a theoretical perspective and has tons of coded examples to show how you would actually use the machine learning technique. It can be read by a beginner or advanced programmer.
- William P. Ross, 7 Must Read Python Books
- Python Machine Learning Review by Patrick Hill at the Chartered Institute for IT
- Book Review: Python Machine Learning by Sebastian Raschka by Alex Turner at WhatPixel
- German ISBN-13: 978-3958454224
- Japanese ISBN-13: 978-4844380603
- Italian ISBN-13: 978-8850333974
- Chinese ISBN-13: 978-9864341405
- Korean ISBN-13: 979-1187497035
Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python
Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we’ll continue where we left off in “Python Machine Learning” and implement deep learning algorithms in TensorFlow.
Manuscripts / Early Access Drafts
The Perceptron [Code Notebook]
Optimizing Cost Functions with Gradient Descent
Logistic Regression and Softmax Regression
From Softmax Regression to Multi-layer Perceptrons
Cross Validation and Performance Metrics
Regularization in Neural Networks
Learning Rates and Weight Initialization
Convolutional Neural Networks
Recurrent Neural Networks
General Adverserial Neural Networks
Deep Generative Models
Appendix C: Linear Algebra Essentials
Appendix E: Python Setup
Model Zoo: A collection of standalone TensorFlow models in Jupyter Notebooks
About the book
Machine learning has become a central part of our life — as consumers, customers, and hopefully as researchers and practitioners! I appreciate all the nice feedback that you sent me about “Python Machine Learning,” and I am so happy to hear that you found it so useful as a learning guide, helping you with your business applications and research projects. I have received many emails since its release. Also, in these very emails, you were asking me about a possible prequel or sequel.
Initially, I was inclined to write more about the “math” parts, which can be a real hurdle for almost everyone without (or even with) a math major in college. Initially, I thought that writing a book about “machine learning math” was a cool thing to do. Now, I have ~15 chapters worth of notes about pre-calculus, calculus, linear algebra, statistics, and probability theory. However, I eventually came to a conclusion that there were too many other math books out there, already! Most of them are far better and more comprehensive and accurate than my potential ~500-page introduction to the topics that I had in store. After all, I think that the real motivation for learning and understanding a subject comes from being excited about it in the first place; if you are passionate about machine learning and you stumble upon the chain rule in calculus, you wouldn’t have any problems to find a trusted resource via your favorite search engine these days.
So, instead of writing that “prequel,” let me write about something that’s built upon the concepts that I introduced in the later chapters of “Python Machine Learning” – algorithms for deep learning. After we coded a multi-layer perceptron (a certain kind of feedforward artificial neural network) from scratch, we took a brief look at some Python libraries for implementing deep learning algorithms, and I introduced convolutional and recurrent neural networks on a conceptual level.
In this book, I want to continue where I left off and want to implement deep neural networks and algorithms for deep learning algorithms from scratch, using Python, NumPy, and SciPy throughout this educational journey. In addition to the vanilla Python science-stack, we will implement these algorithms in TensorFlow, Google’s open source and cutting-edge deep learning library for implementing and applying deep learning to real-world problems efficiently.
Paperback: est. 2018
Python: Deeper Insights into Machine Learning
A 3-in-1 collection of the three books:
- Python Machine Learning (Sebastian Raschka, Sep 2015)
- Designing Machine Learning Systems with Python (David Julian, Apr 2016)
- Advanced Machine Learning with Python (John Hearty, Jul 2016)
Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.
The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems.
The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras. After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems.
At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.
Packt Publishing (August, 2016)
Heat Maps in R: How-To
We are living in the information age where huge amounts of data are readily available to everyone. In my book, I provide a practical hands-on approach of how to create heat maps using the free and probably most popular Statistical Software Package: R. Don’t worry, I already did the hard work for you and provide all the code you’ll need to create great heat maps from your data. Detailed information on each approach make this book a valuable experience for beginners as well as experienced users of R.
My honest opinion: This book is a couple of years old by now and many new packages have been been developed in R since then. Although this book contains a little bit more than “just” heat maps, maybe one of my blog articles is already sufficient to get you started.
Paperback: 72 pages, ebook
Packt Publishing (June, 2013)