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Identify where to apply unsupervised machine learning and how to do it in Python. Find meaning in unlabeled data through Python-based unsupervised machine learning.About This Book
- Concrete examples that can jump-start the learner to solve a real problem (work-related, for example) right away.
- Help with implementation in Python -- libraries, best practices.
- An understanding of what kinds of problems can be solved by which unsupervised learning algorithm.
Audience should already know Python and wants to gain some hands-on experience in using Python for unsupervised machine learning. Our ideal audience would also have some experience with machine learning generally, as otherwise they may benefit from a more general course. Learning unsupervised machine learning will help our audience find structure in their data that they may not know exist.What You Will Learn
- Learn where to apply unsupervised learning, with practical examples.
- Learn which unsupervised learning algorithm to apply to what problem.
- Gain practical experience solving various unsupervised learning problems in Python.
Unsupervised learning is useful and practical in situations where labelled data is not available. Unsupervised Learning with Python shows you the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data.
The book begins by explaining how basic clustering works to find similar data points in a set. You will learn in detail various clustering methods, such as K-means, hierarchical clustering, and DBSCAN, and build algorithms from scratch using these methods. Then, you will learn about dimensionality reduction and its applications. You will also learn Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE) and Autoencoders in detail, and learn their implementations and their shortcomings. You will use sklearn to implement and analyse PCA on the Iris dataset. Then, you will use Keras to build autoencoder models for the CIFAR 10 dataset and visualize them using t-SNE. While studying the applications of unsupervised learning, you will explore mine trending topics in twitter or facebook, and build a news recommendation engine for readers.
You will complete the book with several interesting activities, such as performing a market basket analysis and finding relations between different merchandises, using hotspot discovery and KDE algorithm to analyze crime data in London for this effort, and using the Apriori algorithm to study transaction data.
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About the Author
Eugene Y. Chen is a machine learning enthusiast and a Python advocate. He has applied unsupervised machine learning techniques for work and for hobby. One of his previous work uses Kernel Density Estimation to combine predictions from different sources. The work was presented at KDD workshop for mining and learning from time series. He also contributes to Python-based open-source projects such as scikit-learn.
Xavier Holt is a startup co-founder, data scientist, and academic researcher. He has worked as a university tutor, teaching people how to program; and an NLP researcher, teaching machines how to talk. He believes machine learning should be both principled and practical.
Chris Kruger is a practicing data scientist and AI researcher. He has managed applied machine learning projects across multiple industries while mentoring junior team members on best practices. His primary focus is on pushing both business practicality as well as academic rigor in every project. Chris is currently developing research in the computer vision space.