Hands-On Unsupervised Learning Using Python How to Build Applied Machine Learning Solutions from Unlabeled Data

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover.

Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.

• Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning
• Set up and manage machine learning projects end-to-end
• Build an anomaly detection system to catch credit card fraud
• Clusters users into distinct and homogeneous groups
• Perform semisupervised learning
• Develop movie recommender systems using restricted Boltzmann machines
• Generate synthetic images using generative adversarial networks