1.   機器學習簡介
        •   Python主要使用的領域
        •   機器學習演算法
        •   機器學習的種類
        •   機器學習建模的整體流程
2.   Numpy
        •   Numpy基礎
        •   Numpy陣列操作
        •   Axis軸的概念
        •   Numpy內建函數建構ndarray
        •   Numpy常使用方法
        •   python內建函數 vs numpy函數
        •   Broadcasting
        •   Mask
        •   Fancy Index
        •   Numpy實戰練習
3.   Pandas
        •   Series物件介紹
        •   DataFrame物件介紹
        •   Index物件介紹
        •   各物件資料選取
        •   DataFrame進階操作
        •   階層式索引
        •   資料集合併
        •   聚合計算
        •   抽樣
        •   Pandas實戰練習
4.   Feature Engineering Theory
        •   Selection
        •   Cleaning
        •   Enrichment
        •   Coding
5.   Feature Engineering Implement
        •   Cleaning
        •   Coding
        •   特徵工程實戰練習
6.   初探Machine Learning Algorithm
        •   KNN Algorithm
        •   Distance Definition
        •   Scikit-Learn
        •   Create KNN Model
        •   KNN的挑戰
        •   Grid Search
        •   KNN總結

7.   Cross Validation
        •   Holdout CV
        •   Leave-one-out CV
        •   K-fold CV
8.   Association Rule
        •   傳說的開始 - 啤酒與尿布的故事
        •   購物籃分析
        •   Algorithm Definition
        •   Apriori
        •   FP-Growth
9.   Sequential Pattern
        •   何謂Sequential Pattern
        •   Algorithm Definition
        •   AprioriAll
        •   其它著名的演算法
        •   混合領域的Sequential Pattern
10.   Cluster
        •   何謂Cluster
        •   何謂Similarity
        •   Hierarchical Methods
        •   Hierarchical Implement
        •   K-Means
        •   K-Means Implement
        •   Cluster Evaluate
        •   Cluster Evaluate Implement
        •   Best K
11.   Classification
        •   何謂Classification
        •   Naive Bayes Classification
        •   Basic Probability Theory
        •   Bayes Classification推導
        •   Bayes Classification範例
        •   SKLearn Model Selection
        •   Training Classification Model
        •   Classification Evaluate
12.   Regression
        •   何謂Regression
        •   Least Squares Method
        •   Regression Implement
        •   Regression Evaluate