共 5 条相关资源。
斯坦福吴恩达的机器学习旗舰课程新版本：用 Python 与 scikit-learn 教学，覆盖监督学习、聚类、异常检测、推荐系统与决策树，配套 Labs 与测验，是全球公认的 ML 第一课。Andrew Ng's updated Machine Learning Specialization: Python-based, covering supervised learning, clustering, anomaly detection and recommender systems with hands-on labs.
斯坦福研究生级机器学习课程：严格数学推导 + 精炼 notes，覆盖 GLM、SVM、学习理论、EM、强化学习，吴恩达主讲版本是经典中的经典，适合打深基础。Stanford's graduate-level ML course with rigorous math derivations and concise notes, covering GLMs, SVMs, learning theory, EM and RL.
周志华《机器学习》：国内 ML 教材的事实标准，覆盖十余种经典算法，语言凝练、体系完整；配合《南瓜书》的公式推导更佳，是考研与面试的高频出处。Zhou Zhihua's Machine Learning (the 'Watermelon Book'): the de-facto standard ML textbook in China, covering classic algorithms with rigor.
李航《统计学习方法（第二版）》：以统计视角严格推导感知机、SVM、EM、CRF 等经典模型，篇幅精炼、推导完整，与西瓜书并称国内 ML 双璧。Li Hang's Statistical Learning Methods: rigorous derivations of perceptron, SVM, EM and CRF from a statistical perspective; concise and complete.
谷歌官方机器学习速成课：中文界面、约 15 小时、40+ 可视化交互练习与 pandas 实操，节奏快、覆盖广，非常适合快速建立全局认知。Google's official ML Crash Course: Chinese-supported, ~15 hours, 40+ interactive visual exercises — a fast way to see the whole picture.