Python Machine Learning Cookbook by Packt Publishing
##Instructions and Navigation This is the code repository for Python Machine Learning Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. The code files are organized according to the chapters in the book. These code samples will work on any machine running Linux, Mac OS X, or Windows. Even though they are written and tested on Python 2.7, you can easily run them on Python 3.x with minimal changes.
To run the code samples, you need to install scikit-learn, NumPy, SciPy, and matplotlib. For Chapter 6, you will need to install NLTK and gensim. To run the code in chapter 7, you need to install hmmlearn and python_speech_features. For chapter 8, you need to install Pandas and PyStruct. Chapter 8 also makes use of hmmlearn. For chapters 9 and 10, you need to install OpenCV. For chapter 11, you need to install NeuroLab.
##Description Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields like search engines, robotics, self-driving cars, and so on. During the course of this book, you will learn how to use Python to build a wide variety of machine learning applications to solve real-world problems. You will understand how to deal with different types of data like images, text, audio, and so on.
We will explore various techniques in supervised and unsupervised learning. We will learn machine learning algorithms like Support Vector Machines, Random Forests, Hidden Markov Models, Conditional Random Fields, Deep Neural Networks, and many more. We will discuss about visualization techniques that can be used to interact with your data. Using these algorithms, we will discuss how to build recommendation engines, perform predictive modeling, build speech recognizers, perform sentiment analysis on text data, develop face recognition systems, and so on.
You will understand what algorithms to use in a given context with the help of this exciting recipe-based guide. You will learn how to make informed decisions about the type of algorithms you need to use and learn how to implement those algorithms to get the best possible results. Stuck while making sense of images, text, speech, or some other form of data? This guide on applying machine learning techniques to each of these will come to your rescue! The code is well commented, so you will be able to get it up and running easily. The book contains all the relevant explanations of the algorithms that are used to build these applications.
There is a lot of debate going on between Python 2.x and Python 3.x. 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. It also helps in maintaining compatibility with Python libraries that haven't been ported to Python 3.x. Keeping that in mind, the code in this book is oriented towards Python 2.x. We have tried to keep all the code as agnostic as possible to the Python versions, so that Python 3.x users won't face too many issues. We are focused on utilizing the machine learning libraries in the best possible way in Python.
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