fast.ai 新課程:面向程式設計師的機器學習導論(Introduction to Machine Learning for Coders)
fast.ai 在9月26號剛剛推出了一門機器學習新課程:面向程式員的機器學習導論(Introduction to Machine Learning for Coders),目標明確,面向程式設計師,注重實戰,直接從隨機森林講起,這個之前Kaggle資料競賽的熱門機器學習方法,看起來很不錯。
課程主頁: ofollow,noindex" target="_blank">http://course.fast.ai/ml
Welcome to Introduction to Machine Learning for Coders! taught by Jeremy Howard (Kaggle’s #1 competitor 2 years running, and founder of Enlitic). Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.
課程授課者是 Jeremy Howard,Kaggle前冠軍選手和專家,fast.ai的發起者之一,關於Jeremy Howard, 以下是來自雷鋒網《 Enlitic創始人Jeremy Howard專訪:我眼中的深度學習與資料科學 》中的介紹:
他是Enlitic、FastMail、Optimal Decisions Group三家科技公司的創始人兼CEO,是大資料競賽平臺Kaggle的前主席和首席科學家,是美國奇點大學(Singularity University)最年輕的教職工,是在2014達沃斯論壇上發表主題演講的全球青年領袖,他在 TED 上的演講《The wonderful and terrifying implications of computers that can learn》收穫了近200萬的點選…
這門機器學習課程包含12節課,比較偏重隨機森林,第一節就從從隨機森林講起,後續幾個章節相關,另外涉及效能評估、模型驗證和融合、特徵提取、梯度下降、邏輯迴歸以及NLP相關的內容:
Lesson 1 – Introduction to Random Forests
Lesson 2 – Random Forest Deep Dive
Lesson 3 – Performance, Validation and Model Interpretation
Lesson 4 – Feature Importance, Tree Interpreter
Lesson 5 – Extrapolation and RF from Scratch
Lesson 6 – Data Products
Lesson 7 – Introduction to Random Forests
Lesson 8 – Gradient Descent and Logistic Regression
Lesson 9 – Regularization, Learning Rates and NLP
Lesson 10 – More NLP, and Columnar Data
Lesson 11 – Embeddings
Lesson 12 – Complete Rossmann, Ethical Issues
關於這門面向程式設計師的機器學習課程,fast.ai官方有個詳細介紹的文章,感興趣的同學可以參考: http://www.fast.ai/2018/09/26/ml-launch/
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