See also:
Machine Learning Books 3Blue1Brown / 3B1B videos on Bilibili & Youtube Note: The following books are more introductory than professional, but usually enough for most engineers.
Linear Algebra # Sheldon Axler 《Linear Algebra Done Right / 线性代数应该这样学》(以线性变换为中心) [豆瓣 8.8,184 人],图灵社区出品,人民邮电出版社; supplementary videos (English subtitle) on Youtube and Bilibili; slides; Errata. 适合于上课后听不懂的二次自学。
麻省理工 Gilbert Strang “线性代数 MIT 18.06”（网易公开课有翻译） & 教材 《Introduction to Linear Algebra 线性代数》 [第五版 豆瓣 9.5，71 人] 清华大学出版社。 Videos (中英双字幕) on Bilibili & Youtube with Newly added content in 2020。 Strang 教授另有一本《Linear Algebra and Its Applications》，内容差不多。
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See also:
Machine Learning - Just-do-it (hands on) Basics Math Books Favoured # MLY (Andrew Ng), Machine Learning Yearning (EN full: ch1-13), 《CN: 机器学习秘籍》(CN Web online), (CN Github) and someone’s notes: part 1 (bak), part 2 (bak) and part 3.
MLAPP (Kevin Murphy), Machine Learning - A Probablistic Perspective, is more comprehensive, insightful and interesting, and contains more “real” examples/problems. However, the presents are kinda out of order, which can be difficult to follow for a first book.
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This is a detailed reproduction of ref.
Sunny Summary # 3 steps:
preprocessing.py preprocessing to extract: author, average sentence length, average word length, punctuation profile, sentiment scores, part-of-speech profiles/tags (only in code, not taken into the csv). TFIDF.py content-wise k-means clustering (on TFIDF scores) to get: 3 levels/degrees of clustering/classification results. knn.py knn search on the results of step 1 and 2 to get: recommendations (k=15 by default). Preprocessing # pip2 install nltk pip2 install twython # optional ?
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