Prateek Joshi et al. - Python: Real World Machine Learning

, Python Machine Learning Cookbook , teaches you about the algorithms that we use to build recommendation engines. We will learn how to apply these algorithms to collaborative filtering and movie recommendations.

, Advanced Machine Learning with Python , explains how to apply several semi-supervised learning techniques including CPLE, self learning, and S3VM.

, Large Scale Machine Learning with Python , covers interesting deep learning techniques together with an online method for neural networks. Although TensorFlow is only in its infancy, the framework provides elegant machine learning solutions.

Module 1: While we believe that the world is moving forward with better versions coming out, a lot of developers still enjoy using Python 2.x. A lot of operating systems have Python 2.x built into them. This course is focused on machine learning in Python as opposed to Python itself. It also helps in maintaining compatibility with libraries that havent been ported to Python 3.x. Hence the code in the book is oriented towards Python 2.x. In that spirit, we have tried to keep all the code as agnostic as possible to the Python versions.

Module 2: The entirety of this courses content leverages openly available data and code,including open source Python libraries and frameworks. While each chapters example code is accompanied by a README file documenting all the libraries required to run the code provided in that chapters accompanying scripts, the content of these files is collated here for your convenience. It is recommended that some libraries required for earlier chapters be available when working with code from any later chapter. These requirements are identified using bold text. Particularly, it is important to set up the first chapters required libraries for any content later in the book.

Module 3: The execution of the code examples provided in this book requires an installation of Python 2.7 or higher versions on macOS, Linux, or Microsoft Windows.

The examples throughout the book will make frequent use of Pythons essential libraries, such as SciPy, NumPy, Scikit-learn, and StatsModels, and to a minor extent, matplotlib and pandas, for scientific and statistical computing. We will also make use of an out-of-core cloud computing application called H2O. This book is highly dependent on Jupyter and its Notebooks powered by the Python kernel. We will use its most recent version, 4.1, for this book. The first chapter will provide you with all the step-by-step instructions and some useful tips to set up your Python environment, these core libraries, and all the necessary tools.