瞭解 Python 程式設計語言。建議基本熟悉統計學和線性代數。
本課程的目的是提供在實踐中應用Machine Learning方法的基本熟練程度。通過使用Python編程語言及其各種庫，並基於大量實際示例，本課程教授如何使用Machine Learning最重要的構建塊，如何制定數據建模決策，解釋算法的輸出和驗證結果。
我們的目標是讓您自信地理解和使用Machine Learning工具箱中最基本的工具，並避免Data Science應用程序的常見缺陷。
應用材料簡介 Machine Learning
- 統計學習與機器學習
- 反覆運算和評估
- 偏差-方差權衡
使用 Python 進行機器學習
- 庫的選擇
- 附加工具
回歸
- 線性回歸
- 泛化和非線性
- 習題
分類
- 貝葉斯複習
- 樸素貝葉斯
- 邏輯回歸
- K-最近鄰
- 習題
交叉驗證和重採樣
- 交叉驗證方法
- Bootstrap
- 習題
無監督學習
- K-means 聚類
- 例子
- 無監督學習和超越 K 均值的挑戰
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