Free Download Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data by Ankur A. Patel
English | April 16, 2019 | ISBN: 1492035645 | True PDF | 359 pages | 8.6 MB
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.
Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learningSet up and manage a machine learning project end to end everything from data acquisition to building a model and implementing a solution in productionUse dimensionality reduction algorithms to uncover the most relevant information in data and build an anomaly detection system to catch credit card fraudApply clustering algorithms to segment users such as loan borrowers into distinct and homogeneous groupsUse autoencoders to perform automatic feature engineering and selectionCombine supervised and unsupervised learning algorithms to develop semi supervised solutionsBuild movie recommender systems using restricted Boltzmann machinesGenerate synthetic images using deep belief networks and generative adversarial networksPerform clustering on time series data such as electrocardiogramsExplore the successes of unsupervised learning to date and its promising future
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