Course Code: mlpython
Duration: 28 hours
Prerequisites:

瞭解 Python 程式設計語言。建議基本熟悉統計學和線性代數。

Overview:

本課程的目的是提供在實踐中應用機器學習方法的一般熟練程度。通過使用 Python 程式設計語言及其各種庫, 並基於大量的實際示例, 本課程教授如何使用機器學習最重要的構建塊, 如何做出資料建模決策, 解釋輸出並驗證結果

我們的目標是讓您能夠自信地理解和使用機器學習工具箱中最基本的工具, 並避免資料科學應用的常見陷阱。

Course Outline:

應用材料簡介 Machine Learning

  • 統計學習 vs. 機器學習
  • 迭代與評估
  • 偏差-方差權衡

監督學習和無監督學習

  • Machine Learning Languages, 類型與範例
  • 監督學習 vs Unsupervised Learning

監督學習

  • 決策樹
  • Random Forests
  • 模型評估

機器學習與 Python

  • 函式庫選擇
  • 附加工具

回歸

  • 線性回歸
  • 泛化與非線性
  • 練習

分類

  • 貝葉斯複習
  • 朴素貝葉斯
  • 邏輯回歸
  • K-近鄰算法
  • 練習

交叉驗證和重採樣

  • 交叉驗證方法
  • Bootstrap
  • 練習

無監督學習

  • K-均值聚類
  • 範例
  • 無監督學習的挑戰與超越K-均值

神經網路

  • 層與節點
  • Python 神經網路函式庫
  • 使用scikit-learn
  • 使用PyBrain
  • Deep Learning
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