awesome-machine-learning

I changed the readme in my repo to say you have to send me a personal email if you want me to merge. https://github.com/josephmisiti/awesome-machine-learning It's helped a lot. Agents haven't figured out how to do that yet, or sendgrid, sns, etc are doing the hard work for me.

*Learn Machine Learning with these amazing GitHub repositories! * 1⃣ [ML for Beginners](https://github.com/microsoft/ML-For-Beginners) by Microsoft 2⃣ [100 Days of ML Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code) by Avik Jain 3⃣ [ML From Scratch](https://github.com/eriklindernoren/ML-From-Scratch) by Erik Linder-Noren 4⃣ [Hands-On ML](https://github.com/ageron/handson-ml2) by Ageron 5⃣ [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) by Joseph Misiti Save & Share for quick access! #MachineLearning #GitHub #AI #LearnML

One of the few times I wish we had dms here. :) I do not have many groups to offer, but for educational resources, here you go... Dig around on GitHub for Awesome Lists. There is so much out there: - https://github.com/sindresorhus/awesome#learn - https://github.com/mike-north/awesome-learn-to-code - https://github.com/lauragift21/awesome-learning-resources - https://github.com/afontcu/awesome-learning - https://github.com/yrgo/awesome-educational-games - https://github.com/notpresident35/awesome-learn-gamedev - https://github.com/RahulBirCodes/awesome-steam - https://github.com/Ghassen-Chaabouni/machine_learning_games - https://github.com/microsoft/Web-Dev-For-Beginners - https://github.com/microsoft/ML-For-Beginners Also check out this list Pitt puts out every year: - https://github.com/SimplifyJobs/Summer2024-Internships and make sure

If you're content with the current orthodox view / approach(es) then just browse around Github and find one of those "Awesome X" lists like "Awesome Machine Learning"[1], "Awesome Deep Learning"[2], "Awesome Artificial Intelligence"[3] and so on, and go to town. If you want to go deeper, including taking a step back in time and retracing the path(s) taken, to explore whether or not you might want to choose a different fork... well, that's doable, but it's a slog. I should probably write up a reading list for this approach and put it up on Github. It leads to some weird places though... like right before jumping over to HN and noticing this post, I'd been spending the last hour or so trying to track down two obscure Russian books on neural nets from back in the 1980's / 1990's... where print copies do not appear to be available in the US (

As a college student (MSc Computer Science) I often encounter the situation that I am sitting in lectures that suck. The content is very interesting, but it is presented in a very bad or boring way. Most professors are not really interested in providing the best education possible (they want to do research) and therefore their performance and material (and recommended books, papers etc.) are moderate at best [0]. As a solution I head out to YouTube [1, 2], Goodreads, HN threads [3], GitHub pages [4,5] and MOOCs, Medium posts, Google [6] etc. to find alternative material to learn from. While there are some indications of quality/learnability (comment sentiment, ratings) it is still hard to judge which material is best. I want to find the material that best suits my learning needs so I can learn more efficient and effective (and thus more).

perhaps an alternative would be to start a "models" section here? https://github.com/josephmisiti/awesome-machine-learning

Wiki didn't catch on for whatever reason, but github "Awesome ____" lists did: https://github.com/sindresorhus/awesome You can find one for machine learning: https://github.com/josephmisiti/awesome-machine-learning

https://unsupervisedmethods.com/over-150-of-the-best-machine... https://github.com/ChristosChristofidis/awesome-deep-learnin... https://github.com/josephmisiti/awesome-machine-learning

https://github.com/josephmisiti/awesome-machine-learning This is a really good list of resources on Machine learning and has a section dedicated to NLP/Text mining.

Coursera Machine Learning is one of the best places to start, but there are countless resources. There's a huge, wonderful open list of links at Github[1]. I definitely recommend you take a look at it There are also another great resources online, like those I list below: 1.) In-depth introduction to machine learning in 15 hours of expert videos[2] 2.) Deep Learning Tutorial (@ ufldl.Stanford.edu/tutorial/, can't post the link because I'm out of mana, I mean, not enough reputation yet) [1]: https://github.com/josephmisiti/awesome-machine-learning [2]: https://www.dataschool.io/15-hours-of-expert-machine-learnin...