In this post, I’ll cover some of the greatest data science resources I’ve come across. Whether you’re looking for an introduction to machine learning / deep learning or are looking for a good newsletter to keep track of recent developments in AI – this post is for you.
The list below is by far not comprehensive. It does not include well-known influencers such as kdnuggets or resources such as kaggle. But I guess we all know that there’s no lack of blog posts along the lines of „Top 1000 data science resources“. Here, instead, you’ll find my personal, small, but fine selection. Enjoy!
Online Courses (MOOCs)
- Machine Learning by Andrew Ng @ Coursera
Great introduction to Machine Learning by one of its most gifted teachers. Basic concepts and their mathematical foundations are explained very clearly and in an easily accessible way.
- Practical Deep Learning for Coders by Jeremy Howard @ fast.ai
Great course that empowers people with some coding experience to quickly get started with state-of-the-art deep learning with the help of fast.ai’s own python library. Focuses on practical hands-on aspects and best practices in deep learning (particularly vision, NLP & collaborative filtering).
- Towards Data Science
Varied contributed blog posts on data science topics. For many „How to do X (with Y)“ questions, there is an answer on Towards Data Science.
In its own description, Distill is „an academic journal in the area of Machine Learning. The distinguishing trait of a Distill article is outstanding communication and a dedication to human understanding.“ It’s not an abundant resource, but if you find an article about your topic of interest there, you can be almost sure it’s worth your time. If the word „academic“ in the journal description puts you off, take a look at The Gradient instead.
- Import AI by Jack Clarck
Weekly (Monday) newsletter about „artificial intelligence, read by more than ten thousand experts“. AI developments, both on the technical as well as the policy side, are covered in remarkable depth and comprehensiveness.
- The Data Science Roundup by Tristan Handy
Weekly (Sunday) newsletter with „the internet’s most useful data science articles“. Topics include data science tools, culture, trends, and challenges.
- 80,000 hours
This resource is not so much about data science as it is about the question which career to pursue. „You have 80,000 hours in your career.“ If you are considering embarking on a career in data science or a data scientist looking for a new field that might benefit from your expertise, this might be the resource you’ve been looking for.
- Interpretable Machine Learning
Great, accessible summary of approaches to interpreting model predictions.
- Papers with Code
Great resource with a large collection of papers with code on MANY different machine learning tasks (including SOTA results). Whether you’re looking for code, research on a specific machine learning task or want to know which data sets are popular to benchmark a model’s performance, this is the first resource to turn to.
- OpenAI Spinning Up
Remarkable educational resource by OpenAI, „which makes it easier to learn about deep reinforcement learning.“ To be honest, I didn’t look at it in detail yet, but it’ll be the first resource I’ll turn to in order to learn more about certain RL algorithms.
If you’re up to even more
Lots of (partly free) courses covering various data science related topics. If you’re looking for courses to get started with a data science tooling, you might want to have a look there.
Apart from competing on ML/Data Science competitions, obviously, kaggle is a great place to browse other people’s approaches to data exploration, machine learning etc. Besides that, they offer a few short courses in the form of jupyter notebooks which, if they match your needs, are worth taking.