class notes
teacher：李佳蓉 老師
記錄機器學習課堂上的筆記，但部份數學模型過於複雜的地方，並不完全清楚
史上最完整機器學習自學攻略！我不相信有人看完這份不會把它加進我的最愛
- intro.pdf
- Lecture 1.pdf
- Class_01.ipynb
- 作業1.pdf
- 作業一.ipynb
- Introduction
- Python
- zip()
- map()
- enumerate()
- eval()
- Overview
- Lecture 2 Data.pdf
- Class_02.ipynb
- Attributes
- Symmetric vs. Skewed Data
- Basic Statistical Descriptions
- Machine Learning Process
- Data Pre-processing
- fit and transform
- Pandas map()、apply() and applymap()
- Lecture 3 Regression 拷貝_完整.pdf
- Class_03.ipynb
- 作業2.pdf
- Class_04.ipynb
- 作業3.pdf
- Class_05.ipynb
- 作業4_answer.jpg
- Correlation coefficient
- Linear Regression
- Least square method ＆ Gradient descent
- Coefficient of Determination
- Pratices Linear Regression
- Overfitting v.s. Underfitting
- Pratices Polynomial Regression
- Normal equation
- Pratices Multiple Regression
- Regularization
