Python无监督学习实战(影印版)(英文版)

许多行业专家都认为无监督学习是人工智能的下 一个前沿领域，可能是通用人工智能的关键。一方 面，由于世界上大多数数据是无标签的，无法应用传 统的有监督学习；另一方面，无监督学习可以应用于 未标签的数据集，以发现数据中深藏的有意义模式， 这些模式对于人类来说几乎不可能被发现。 作者Ankur A.Patel为你展示了如何使用两个简 单且可用于生产的Python框架实践无监督学习： Scikit-Learn和使用Keras的TensorFlow。通过代码 和实践示例，数据科学家可以识别数据中难以找到的 模式并获得更深入的业务洞察，发现数据异常，执行 自动特征工程和模型选择，以及生成合成数据集。你 只需要一些Python编程和机器学习经验就可以开始阅 读这本书了。 ·比较不同机器学习方法的优缺点：监督学习、 无监督学习和强化学习 ·建立和管理端到端的机器学习项目 ·建立一个异常检测系统以查出信用卡欺诈行为 ·将用户分组为不同的同质组 ·执行半监督学习 ·使用受限玻尔兹曼机开发电影推荐系统 ·使用生成对抗网络来生成合成图像

reface Part Ⅰ Fundamentals of Unsupervised Learning 1. Unsupervised Learning in the Machine Learning Ecosystem Basic Machine Learning Terminology Rules-Based vs. Machine Learning Supervised vs. Unsupervised The Strengths and Weaknesses of Supervised Learning The Strengths and Weaknesses of Unsupervised Learning Using Unsupervised Learning to Improve Machine Learning Solutions A Closer Look at Supervised Algorithms Linear Methods Neighborhood-Based Methods Tree-Based Methods Support Vector Machines Neural Networks A Closer Look at Unsupervised Algorithms Dimensionality Reduction Clustering Feature Extraction Unsupervised Deep Learning Sequential Data Problems Using Unsupervised Learning Reinforcement Learning Using Unsupervised Learning Semisupervised Learning Successful Applications of Unsupervised Learning Anomaly Detection 2. End-to-End Machine Learning Project Environment Setup Version Control: Git Clone the Hands-On Unsupervised Learning Git Repository Scientific Libraries: Anaconda Distribution of Python Neural Networks: TensorFlow and Keras Gradient Boosting, Version One: XGBoost Gradient Boosting, Version Two: LightGBM Clustering Algorithms Interactive Computing Environment: Jupyter Notebook Overview of the Data Data Preparation Data Acquisition Data Exploration Generate Feature Matrix and Labels Array Feature Engineering and Feature Selection Data Visualization Model Preparation Split into Training and Test Sets Select Cost Function Create k-Fold Cross-Validation Sets Machine Learning Models (Part I) Model #1: Logistic Regression Evaluation Metrics Confusion Matrix