The aim of this blog post is to gather different resources on Machine Learning. I will update this post or create a serie of posts in the future to add/update the resources. N.B. : The way I choose resources is based only on what I found useful and relevant.

Last update : 07/03/2022

Machine Learning resources

Gatherings of resources (blog posts, tweets, articles)

• Here a blog post of Sam Finlayson who gathered a lot of resources and published them online.
• A similar type of blog post on Medium from Robbie Allen.
• You will find other learning resources on this Twitter thread.
• An article by Pierre Sadrach which gathers deep learning resources.
• An article on DataScience interviews which gathers a lot of concepts and resources linked to these concepts.
• On the website thalesians.com you can find recommendations for books, videos and datasets in ML (a focus on Finance).

Resources by topic

Machine Learning and Deep Learning

• All courses on Coursera given by deeplearning.ai and Andrew Ng concerning Machine Learning and Deep Learning.
• Courses on Stanford Online, especially Statistical Learning given by Robert Tibshirani and Trevor Hastie.
• Lectures of Kilian Weinberger at Cornell University on Youtube
• Deep learning courses by MIT

Reinforcement learning

• The lectures given at UCL by David Silver (DeepMind) on Reinforcement Learning available on Youtube.

Causal inference

• Resources on Qingyuan Zhao’s website on Causal Inference.

Mixing of different fields

• Learning resources provided by DeepMind (DL & RL)
• Kaggle courses and competitions in order to put into practice theories (ML, DL, NLP,…)

Vocabulary for ML, AI

• For French readers : un lexique utile pour le jargon du ML
• ML and Speech Recognition Glossary

Interviews

• An article on 10 concepts to know for Data Science Interviews
• Machine Learning Interviews Book by Chip Huyen. Her blog contains interesting articles also.
• An article by Leon Chlon gathering a lot of concepts and useful resources for Data Science interviews.
• A reddit’s post which provides advice on DS interviews.

Exposure to news, influencers

• A non-exhaustive list of some Twitter influencers on AI.
• For French readers, a lot of short and interesting articles on DataScientest.
• Toward Data Science which contains a lot of interesting articles published by the DS community.

Cheatsheets

• Different cheatsheets by Shervine Amidi
• My personal cheatsheets on tree-based methods and PCA

Datasets

• Datasets from paperswithcode.
• Datasets for NLP by Hugging Face.

Libraries

• Open-Source Machine Learning libraries : Scikit-learn, Keras.io, TensorFlow, PyTorch, CNTK (Microsoft), Berkeley AI Research library Caffe.

If you are interested in adding resources on this post, please feel free to send me a message.