Part 1. Fundamentals of unsupervised learning. Unsupervised learning in the machine learning ecosystem -- End-to-end machine learning project -- Part 2. Unsupervised learning using Scikit-learn. Dimensionality reduction -- Anomaly detection -- Clustering -- Group segmentation -- Part 3. Unsupervised learning using TensorFlow and Keras. Autoencoders -- Hands-on autoencoder -- Semisupervised learning -- Part 4. Deep unsupervised learning using TensorFlow and Keras. Recommender systems using restricted Boltzmann machines -- Feature detection using deep belief networks -- Generative adversarial networks -- Time series clustering -- Conclusion

Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. Unsupervised learning 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 provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow using Keras. With the hands-on examples and code provided, you 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