fast.ai 新课程:面向程序员的机器学习导论

fast.ai 在9月26号刚刚推出了一门机器学习新课程:面向程序员的机器学习导论(Introduction to Machine Learning for Coders),目标明确,面向编程者,注重实战,直接从随机森林讲起,这个之前Kaggle数据竞赛的热门机器学习方法,看起来很不错。

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.

课程授课者是 Jeremy Howard,Kaggle前冠军选手和专家,fast.ai的发起者之一,关于Jeremy Howard, 以下是来自雷锋网《Enlitic创始人Jeremy Howard专访:我眼中的深度学习与数据科学》中的介绍:

他是Enlitic、FastMail、Optimal Decisions Group三家科技公司的创始人兼CEO,是大数据竞赛平台Kaggle的前主席和首席科学家,是美国奇点大学(Singularity University)最年轻的教职工,是在2014达沃斯论坛上发表主题演讲的全球青年领袖,他在 TED 上的演讲《The wonderful and terrifying implications of computers that can learn》收获了近200万的点击…

这门机器学习课程包含12节课,比较偏重随机森林,第一节就从从随机森林讲起,后续几个章节相关,另外涉及性能评估、模型验证和融合、特征提取、梯度下降、逻辑回归以及NLP相关的内容:

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

关于这门面向程序员的机器学习课程,fast.ai官方有个详细介绍的文章,感兴趣的同学可以参考:http://www.fast.ai/2018/09/26/ml-launch/

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  • 原文链接https://kuaibao.qq.com/s/20180928G1VYO800?refer=cp_1026
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