補充教材:

• Machine Learning Algorithms: 課本作者自行維護的原始碼

• Machine Learning Crash Course: by Google.

• Useful Tips: update whenever needed.
• 台大資工系教師在 MOOC 上的機器學習課程：Artificial Intelligence - Learning & Theory, Machine Learning Foundations: Mathematics, and Machine Learning Foundations: Algorithms

• Data Preparation:
  1. Data Cleaning and Preprocessing for Beginners

• 非常棒的數學基礎的介紹：
  1. Why divide the sample variance by N-1?: 介紹 likelihood function, maximum likelihood estimator 等
  2. What are eigenvectors and eigenvalues?
  3. A geometric interpretation of the covariance matrix
  4. Transforming vectors using matrices and Linear Transformation

• The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python codes).

• Benchmarking State-of-the-Art Deep Learning Software Tools.

應用：

• 台灣無比精彩：智慧機械

資料集:

• UCI Machine Learning Repository, skilearn datasets, and a list of datasets.

• YouTube-8M Segments Dataset

• Big Mart Sales III: 賣場的銷售資料，包含一些 missing values，這是其他已經清理過的資料集不同的地方。