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Hands-on unsupervised learning using python: how to build applied machine learning...

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Описание

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 learning

Set up and manage a machine learning project end to end everything from data
acquisition to building a model and implementing a solution in production

Use dimensionality reduction algorithms to uncover the most relevant
information in data and build an anomaly detection system to catch credit card
fraud

Apply clustering algorithms to segment users such as loan borrowers into
distinct and homogeneous groups

Use autoencoders to perform automatic feature engineering and selection

Combine supervised and unsupervised learning algorithms to develop semi
supervised solutions

Build movie recommender systems using restricted Boltzmann machines

Generate synthetic images using deep belief networks and generative
adversarial networks

Perform clustering on time series data such as electrocardiograms

Explore the successes of unsupervised learning to date and its promising
future

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