applied-ml VS build-your-own-x

Also, have any of you stumbled across any cool GitHub repos like this one: https://github.com/eugeneyan/applied-ml ?

The second repo I LOVE is Eugene Yan’s Applied ML repository. This is a brilliant idea to create and actually something I was planning on sort of casually doing in my non-existent free time… Anyhow, it is a curated list of technical posts from top engineering teams (Netflix, Amazon, Pinterest, Linkedin, etc.) detailing how they built out different types of AI/ML systems (e.g. forecasting, recommenders, search and ranking, etc.). Ofc, it focuses on AI/ML, but something similar could be made for the traditional or BI-oriented analytics stack, as well as the streaming world, super high value for practitioners! Btw-one of my favorite things at BCG used to be looking at our IT architecture team’s reference architecture diagrams… the best way to understand technologies is to look at how a ton of stuff is architected… and its fun!

Applied ML: A collection of papers, articles, and blogs on ML in production by different companies (Netflix, Uber, Facebook, LinkedIn, etc)

Eugene Yan's applied-ml has tons of case studies.

https://github.com/eugeneyan/applied-ml You may find some of his links interesting. I would avoid anything that refers to scaling up a platform as these are more backend engr focus. The more relevant posts to you are probably on the scale of blog posts that are product oriented like the ones I listed in section 4 (e.g. we wanted to solve X for our users and this is how we scoped and defined it). The technical aspects should come backseat to the business aspects. There's def a lot of companies/blog posts that he missed, but the internet is huge.

Check the resources mentioned in applied-ml. It includes blog posts/papers from many companies describing how they built some ML product X.

Check out this repo. They collect hundreds of case studies, broken down by dozens of methodologies from large real-world companies such as AirBnB, Nvidia, Uber, Netflix etc.

https://github.com/danistefanovic/build-your-own-x

https://news.ycombinator.com/item?id=22299180

https://news.ycombinator.com/item?id=13660086

https://news.ycombinator.com/item?id=26039706

Other resources:

https://github.com/danistefanovic/build-your-own-x

https://github.com/AlgoryL/Projects-from-Scratch

https://github.com/tuvtran/project-based-learning

All suggestions are welcome,thanks in advance

Make sure that apart from learning you're using the knowledge to create something either your own idea or maybe something from https://github.com/danistefanovic/build-your-own-x (with your own twist if possible.). It helps a lot to be working on something separately and seeing the results of your new knowledge outside of a tutorial scenario.

28. Build your own X

Do some projects. Come up with your own ideas or pick something from a list like https://github.com/danistefanovic/build-your-own-x

ello! i'm 20f and humanities student (ir/poli sci) with interest in coding since high school, but just now i have the time to start learning it. i opted for learning pyhton first mostly because i'm interested in automation, data analysis, plus was skimming over the tutorials of build your own x and was surprised that you can do a lot of things with just pyhton.

As for exercises there are plenty of programming task websites out there, most of them are quite boring but you can use a fun one like https://adventofcode.com/ . However the best things to work on are things you actually like so do some small projects. Games (start with command line stuff like hang-man) are common, otherwise pick something from https://github.com/danistefanovic/build-your-own-x or whatever else ideas come to your mind.