u/inkeep

I’ve built a library of short concept notes and labs to revise things quickly before interviews, check if this helps -  https://www.bitelrn.com/library https://www.bitelrn.com/labs

Hey, thanks for checking it. Yes I used Claude to build it but the app took many months to build. There are complex backend components + agent workflows that had to be fine tuned and tested for it to reach this stage  

Thanks mods. 

I’ve built - https://www.bitelrn.com/

It helps you learn AI/ML concepts without the long videos. It uses different micro-learning approaches like spaced repetition, flash cards, quiz, code examples as well as a lab feature where you can interact with the concepts and visualise the output. 

Made for beginners who are starting in the field, as well as professionals who need a recap.

I used Claude to code and created a agentic pipeline to generate the content which uses Gemini model + LLm as a judge to validate. Found it extremely challenging to generate reliable statistical charts and formulas from LLMs, learnt to use ai harness for better results. 

Here is some open content (no sign-up needed) - https://www.bitelrn.com/library | https://www.bitelrn.com/labs

Would truly appreciate any feedback/ inputs. I am interested to know if others face the same issue - of not being able to learn from long form video courses and keep googling the concepts when you need them. 

Our justice system is a joke. Pray to god that you go through life without needing them. All you can hope is karma gets these people, because shame is too out of reach for these beasts

https://www.bitelrn.com/

Helps you learn AI/ML concepts without the long videos. It Includes different micro-learning approaches like spaced repetition, flash cards, quiz, code examples as well as a lab feature where you can interact with the concepts and visualise the output. It is for beginners who are starting in the field, as well as professionals who need a recap.

Here is some open content to start with - https://www.bitelrn.com/library | https://www.bitelrn.com/labs

https://www.bitelrn.com/ - a learning gym for AI and machine learning core concepts. Includes different learning approaches like spaced repetition, flash cards, quiz, code examples as well as a lab feature where you can interact with the concepts and visualise the output. It is for beginners who are starting in the field, as well as professionals who need a recap.

Yes, there is budget and team must do certifications, just hoping to make the most of this

there is a film called Joyland. It is exceptionally made, heart breaking and life reaffirming at the same time.

I used geopandas library for a project where we had to build models for a CPG product targeting. We would get outlet coordinates across US, the geopandas helped create polygons around it and we overlayed census data on top to get demographics of people who would likely visit the stores, then mapped those to the products sold at the store, built a xgb model and apply it for any new/ un tapped outlet.

None of the learning platforms plan this part properly, it is just endless hours of video lectures. I have been facing the same problem, how do you retain all this knowledge? Especially if you are not actively using it in a project.

Making your own notes can be the closest way to capturing what you have learnt but like you rightly said, these can run into long pages and when you need to revisit that topic, you have to spend time going through your notes (assuming the notes are self sufficient).

one suggestion - try to space out the learning, include revision days so that it doesn't get overwhelming at the end.

I have also built a learning app for exactly this - https://www.bitelrn.com/ It is like a personal knowledge keeper for AI/ML concepts and a place to revise with flashcards, code examples, simulators etc. check if you find it helpful

exactly, feels like the hidden language of data!

keep the budget for certifications, not learning. there are ample free resources today, explore deeplearning.ai for advanced courses. You can find some good courses on Udemy in your budget but read the reviews properly before signing up. Lastly, if you need some clearly laid our written content and short ml concept notes with code examples, you can try https://www.bitelrn.com/ (disclaimer, I have built it).

It can be quite overwhelming. Everyone is trying to push ai courses now. But try and focus on learning the core. Build up from basic stats, learn probability, distribution, vectors, matrices, machine learning (regression, tree based algorithms) then move to neural nets and deep learning.  Three recourses to get a grasp of the math -  1. Stat-quest 2. 3blue1brown 3. Khan academy

I’m in the same boat, being pushed into an MLE role, tried to fight it since I too prefer core DS. But the job market argument is real, there are way more DS than experienced MLEs and the pay is better as well. 

I feel the same way about long video courses, could never finish them and ended up wasting a lot of money on subscriptions. 

There is a google course on ML which is nice -  https://developers.google.com/machine-learning/crash-course

You could also just use NotebookLM on any course material you come across (even long video lectures) and convert it into short text summary or infographics. 

I have also built a micro learning app for ML, just simple practical knowledge with Python examples and quiz - Here is a sample topic  https://www.bitelrn.com/library/principal-component-analysis and this is the app -  https://www.bitelrn.com if you find it interesting. 

Hey, I am a data science practitioner and I built something to solve a personal problem with learning. 

I grew tired of long courses on AI/ ML subjects. It took too long and I couldn’t retain the concepts when I needed to apply them.

I wanted smartly structured notes, some place to practice code and a personal retention tool.

So I built BiteLrn -  https://www.bitelrn.com

It has statistics and machine learning concepts broken into small nodes arranged as a mind map rather than a linear syllabus, each with code examples and a practice section — about 150 topics so far.

The part I care most about: once you complete a topic it starts decaying on a forgetting-curve schedule, and the map visibly goes cold. When it does, you get a review slice — flash cards and a short quiz — targeted at what you've actually lost.

Open library, free and no sign-up:  https://www.bitelrn.com/library

Here’s a recent blog I posted on PCA -  https://www.bitelrn.com/blog/what-pca-is-actually-doing-to-your-data

Stanford and MIT video lectures on YouTube are free. Most of the deeplearning.ai courses are free. 

If you want short notes/ quick guide to ml concepts with code examples, you can try - BiteLrn.com (disclaimer: I’ve built it) 

Depends on what is your purpose for learning, if it is to become an expert with foundational knowledge, I think you have it right, start with the theory. Understand the workings of the algorithm deeply and why do you need to use one vs. the other. This will form a solid base for you to go into the math of it, this part might be a bit dry but it will give you a picture of the inner workings, master linear algebra, calculus, probability theory etc. then jump into the code. the libraries will do most of the heavy lifting and you wont ever need to go into the details again. but you will know what you are doing.

However, if you want to quickly get unskilled, best approach would be to do some short course, focus on the python libraries and then jump into case studies. You fill find enough resources on Kaggle to practice.

Thanks for the inputs, we are heavy on databricks and having it in UC would help. Reference vs multiple copies seems like the right approach. Will try it out

Agreed, do you know any way to have centrally managed skills made available to developers? 

Hey, this was written by me, no AI assistance used

This looks great! All the best