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課程簡介
-
數據預處理
- Data Cleaning
- 數據集成和轉換
- 數據縮減
- 離散化和概念層次結構生成
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統計推斷
- 概率分佈、隨機變數、中心極限定理
- 採樣
- 置信區間
- 統計推斷
- 假設檢驗
-
多元線性回歸
- 規範
- 子集選擇
- 估計
- 驗證
- 預測
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分類方法
- Logistic 回歸
- 線性判別分析
- K - 最近鄰
- 樸素貝葉斯
- 分類方法的比較
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Neural Networks
- 擬合神經網路
- 訓練神經網路問題
-
決策樹
- 回歸樹
- 分類樹
- 樹與線性模型
-
裝袋, Random Forests, Boosting
- 裝袋
- Random Forests
- 提高
-
支援向量機和柔性盤
- 最大邊距分類器
- 支援向量分類器
- 支援向量機
- 2 個及以上類 SVM
- 與logistic回歸的關係
-
主成分分析
-
聚類
- K-means 聚類
- K 中心點聚類
- 分層聚類
- 基於密度的聚類
-
模型評估和選擇
- 偏差、方差和模型複雜性
- 樣本內預測誤差
- 貝葉斯方法
- 交叉驗證
- Bootstrap 方法
28 時間:
客戶評論 (5)
I was benefit from the guidance and sharing life examples + answering all questions.
Marta Melloch - Amazon Development Center Poland Sp. z o.o.
Course - Data Mining and Analysis
I really enjoyed the all the best.
Halil polat - Amazon Development Center Poland Sp. z o.o.
Course - Data Mining and Analysis
The information given was interesting and the best part was towards the end when we were provided with Data from Durex and worked on Data we are familiar with and perform operations to get results.
Jessica Chaar
Course - Data Mining and Analysis
The hands-on exercise and the trainer capacity to explain complex topics in simple terms.
youssef chamoun
Course - Data Mining and Analysis
I like the exercises done.