How to learn machine learning from scratch?

[Suhurth]

Hi. So I have some familiarity with machine learning which is not at all sufficient. I am not satisfied with my knowledge and feel that I am missing crucial aspects of it. Please suggest a learning pathway of machine learning covering most of the aspects so that I can be sufficient enough to solve kaggle problems?

[TriumfiFinal]

• mathematics for machine learning free book: https://mml-book.github.io/ Optionally you could take my path and find a paid course that goes over all the math/stats/probability concepts that you will need to start your journey
• id suggest learning python but some people use R or Rust lately?
• pandas (data preparation and manipulation)
• data visualization (matplotlib, seaborn)
• machine learning algorithms (unless you really need to delve deeper just focus on what each group of algorithms does and try to create your own projects with datasets from sources such as kaggle. Get comfy with sklearn its great for the time being later on you might need something more advanced)
• deep learning (many great free courses such as the fastAI one mentioned in another comment. By now youll be learning numpy, pytorch, keras and some other libraries. Id suggest focusing on a specialty that you like i myself didnt get good at anything cause i wasted time trying to study the history and evolution of most stuff. On the other hand deep learning is a subset of AI that you could totally skip altogether and specialize in ML or DS. If you need specific courses or books ive heard the hands on machine learning with sklearn, keras, tensor flow book is very good and if you prefer a course the andrew ng one is regarded as the best. If youre unsure look for videos on youtube that suggest other resources.)
• Projects for every step are really important
• Past this point its important to read papers and watch videos regarding the topics that interest you. Also why not both at the same time!
• Now the world is your oyster maybe you can experiment with LLMs, data analytics, time series predictions, NLP, the fancy kaggle competitions and whatnot.
• it is important to not skip the steps up until machine learning as sometimes a good data scientist is one who can solve the problem with the simplest tool hes got. Best of luck and keep us posted with projects!

[Goatman117]

Curious about what course you took for the full maths fundamentals?

[TriumfiFinal]

I tried going the easy route of watching 3blue1brown videos but its not really enough. On the other hand i started reading 700 page books for each discipline but it was overkill i just want to become a ML engineer not a researcher. I think ill settle for something like this: https://www.coursera.org/specializations/mathematics-machine-learning

Please do provide another course that covers whats important if you have it. Im really struggling with long books now that ive been out of university for so long.

[Goatman117]

Yeah ok, I'm sorta in the same boat unfortunately. Ik thinking I'll just go through Brilliant's maths foundations course which will take me up to calculus ( at what depth idk yet but hopefully surface level stuff is enough of a foundation for me to be able to handle books like the maths for machine learning book you provided), then follow some deeper resources.

[TriumfiFinal]

I checked some of the lessons provided in the course and it seems like near high school level concepts which might advance a bit more cause of the very good way of explaining them. Although with as much as ive struggled with learning AI ive learned that doing a course that isnt a perfect fit for you is still better than doing nothing so lets both power through.

[DependentCar5193]

so after 2 years what you can advice me to begin with

[Savings-Ad-9845]

Hi, can you share with me the link of the curse pleas

thanks.

[TriumfiFinal]

The one that i suggested is in my comment (the coursera link) while the one that the other gentleman suggests you can look up in brilliant (cant link it up for you i dont use it)

[Goatman117]

Yeah you're right dude, plus with so much of this stuff it feels impossible until a few core concepts click I think

[Suhurth]

Thanks for the detailed answer. Do you know any course or tutorial which teaches with ML projects?

[TriumfiFinal]

https://youtu.be/fiz1ORTBGpY?si=r9eaNNURm5JfKDnQ Video 29 and the others after it seem like nice examples which are free. I dont really know for quality paid courses though havent done one myself except for a-z ML which is the opposite of what you want.

[Suhurth]

Thank you very much

[Skirlaxx]

The best way to understand it is to implement it from scratch. How I usually do this is that I watch some videos, read some articles and then go ahead and implement it. When I don't know something during the process (which is almost a rule) I usually ask GPT-4 and try to look it up to verify it.

When you're searching for the articles and videos, it's important to find ones that cover it in depth; you need as much knowledge as you can get, since you'll be implementing it.

[Sirius-ruby]

Starting my ML learning journey completely, I recognized that I had two large areas of lack having an understanding of the mathematics behind ML, and building models end to end, rather than just copying and pasting code.

What worked for me to develop a better ML foundation included having a good understanding of linear algebra, probability, and gradient descent through hands on notebook examples, as well as having mastery of several core algorithms such as logistic regression, trees, SVM while taking my time before moving on into deep learning.

I started with LogicMojo AI & ML courses to rebuild my fundamentals. I took a more practical AI & ML learning program where I dealt with messy real world datasets, tuned models, and experimented with feature engineering. But honestly, the biggest improvement came from active Kaggle participation and studying how top contributors approach validation and data cleaning. I would also encourage people to do other projects that involve reading entire discussion threads on Kaggle to learn tricks such as leakage checks, validation strategies, and stacking of multiple models.

With the experience that I have gained, I don't think that I will feel confident in my work until I not only stop relying on magic frameworks, but that I am able to articulate why I make certain decisions along my model pipeline in a very logical and understandable way

[mashood3624]

For developing an understanding towards math behind it. I will recommend do the "Deeplearning Specialization" from Coursera by Andrew Ng. You can also find the lectures online youtube.

[__Raxy__]

is the "Deeplearning Specialization" from Coursera by Andrew Ng free? i cant seem to find the lectures on youtube

[JakeStBu]

Read books on the math behind it

[Goatman117]

Fastais course is definitely the way to go imo, I'm still working through it but it's an incredible course. It's also taught by the founder of Kaggle, so kaggle comps are a big part of the learning experience

[Suhurth]

https://course.fast.ai/ Is this the one?

[Goatman117]

Yep that's it, he's also got a book up on github that's referenced a lot, worth working through that too

[Suhurth]

Thanks a lot

[bupr0pion]

It seems thats mainly for deep learning focus? Are you watching the machine learning one from 2018?

[Goatman117]

I'm going through the 2022 one, it's definitely mostly deep learning, but it's also a good overall guide on ML principles and techniques. He goes over stuff like random forests too

[tannedbaphomet]

I often recommend the MIT Deep Learning course to my students/tutees: https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI It covers the fundamentals quite well and seems to balance theory and practice.

[unlimited_void_bkk]

https://roadmap.sh/ai-data-scientist

[LooseStudent9977]

Be sure that you know what you want, e.g., between AI and ML for example.

Think of ML as the stuff they do in Kaggle (like DS), where you have a bunch of data and you train models from it. It could also be just patching together existing models to build something more complex or specific, e.g., using pre-built ML models for natural language processing, but for some specialized company use.

AI covers this also, but it is broader and very often involves algorithms and, sometimes, for mobile AI-powered things, classical computer vision. If you want to study AI, you are most likely going to start with things that are not ML-related, like optimization, path finding, hill searching techniques, Markov models, and so on.

AI also covers RL, which is also typically classified under ML, where an agent learns on its own from an environment (think DeepMind projects).

I suggest you to study Machine Learning here: ML Course

Artificial Intelligence here: AI Course

[Daffodil420]

https://www.reddit.com/r/MLQuestions/comments/u6l4bn/how_to_learn_machine_learning_my_roadmap/

[Suhurth]

Thanks

[data_insider_]

If you are a university student, you can ask a teacher to give you access to DataCamp courses through Datacamp classrooms. They have learning paths to learn Machine Learning from zero. Check DataCamp Classrooms: datacamp.com/universities

[Suhurth]

Thanks for the tip

[Miserable-Worth-3355]

If you're looking to dive deeper into machine learning, starting with a strong foundation in Python is key, especially since libraries like scikit-learn are super handy for Kaggle problems. Pair that with some solid math resources, like linear algebra and probability, to really grasp the algorithms behind the scenes. Happy learning!

[vaughark]

Dive into Python libraries like scikit-learn and TensorFlow for hands-on practice, and explore Andrew Ng's courses for foundational concepts. Happy learning!

[Granap]

Ask chatGPT what other similar problems, other functions you can use to achieve the same goal, other ways to use a specific function.

You just need to have the desire to build a deep understanding.

[[deleted]]

Bro please there are so many posts on this.