This is the code repository for Hands-On Unsupervised Learning with Python, published by Packt.
Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.
This book covers the following exciting features:
If you feel this book is for you, get your copy today!
<img src="https://raw.githubusercontent.com/PacktPublishing/GitHub/master/GitHub.png" alt="https://www.packtpub.com/" border="5" />
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
X_train = faces['images']
X_train = (2.0 * X_train) - 1.0
width = X_train.shape[1]
height = X_train.shape[2]Following is what you need for this book: This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.
With the following software and hardware list you can run all code files present in the book (Chapter 1-09).
| Chapter | Software required | OS required |
|---|---|---|
| 1-9 | Python 3.5+, Jupyter Notebbok | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Building Machine Learning Systems with Python - Third Edition [Packt] [Amazon]
Machine Learning Algorithms - Second Edition [Packt] [Amazon]
Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.
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