Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data 1st Edition ISBN: 9781492035640
Original price was: $79.99.$49.99Current price is: $49.99.
Product Details
- Condition: New
- Publisher: O’Reilly Media
- Language: English
- Paperback: 359 pages
- ISBN: 9781492035640
- Item Weight: 1.2 pounds
- Dimensions: 7 x 0.7 x 9.1 inches
Description
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
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| Weight | 1.2 lbs |
|---|---|
| Dimensions | 17.78 × 1.778 × 23.114 in |
Raymond Ellington –
A very practical book for understanding clustering, dimensionality reduction, and other unsupervised techniques. The examples in Python make the concepts easier to apply in real projects.
Harold Winthrop –
Clear explanations and well-structured chapters help build a strong foundation in unsupervised machine learning. A useful resource for learners moving beyond basic ML.
Phillip Norwood –
The hands-on approach makes complex algorithms much easier to grasp. Real-world examples add a lot of value to the learning process.
Terrence Blackwell –
A solid guide to working with unlabeled data. The practical coding sections are especially helpful for improving applied ML skills.
Douglas Fairchild –
The book provides a good mix of theory and implementation. It’s a reliable reference for anyone exploring advanced data analysis methods.
Leonard Kingsley –
Well-written and informative. The step-by-step explanations help in understanding clustering and feature extraction techniques effectively.
Stanley Whitcomb –
A comprehensive introduction to unsupervised learning tools in Python. Helpful for both students and working professionals.
Roger Pemberton –
The projects and exercises make learning engaging and practical. A valuable book for building real-world machine learning solutions.