Hands-On Unsupervised Learning Using Python (Paperback)

Ankur A. Patel



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



作者Ankur Patel向您展示如何使用兩個簡單且可用於生產的Python框架Scikit-learn和TensorFlow使用Keras進行無監督學習。通過代碼和實際示例,數據科學家將能夠在數據中識別出難以找到的模式,獲得更深入的業務洞察,檢測異常,執行自動特徵工程和選擇,以及生成合成數據集。您只需要具備編程和一些機器學習經驗即可開始。

- 比較監督學習、無監督學習和強化學習的優點和缺點
- 從頭到尾建立和管理機器學習項目
- 構建檢測信用卡詐騙的異常檢測系統
- 將用戶分為不同且同質的群體
- 執行半監督學習
- 使用受限玻爾茲曼機開發電影推薦系統
- 使用生成對抗網絡生成合成圖像