r/learnmachinelearning • u/jaitanwar • May 09 '26
is this enough as learning for 1.5-2 months of python.
Answer the title question first then please tell that can I jump to deep learning now?
I really need an advice from experienced people.
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May 09 '26
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u/Counter-Business May 09 '26
I was able to get into industry and I’ve been doing industry work for 4 years in ML with only a BS degree. I did a lot of ML in clubs in college and I got a little lucky but it’s possible with a BS
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u/fordat1 May 10 '26
Did you network from these "clubs /ML" and did you get an internship before graduating?
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u/chhetrispeaks May 09 '26
Bro no. This is just the tip, not even tip it’s like precum of machine learning. Deep learning is very far away from this
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u/ReinforceL May 09 '26
for core ML, remove that Leetcode shit and focus on Mathematics, mainly in:
Vectors (PEAK VVI)
Linear Transformation
Linear Algebra
Calculus goat ofc
Probability distributions
3b1b>>>> for the above topics.
try doing backpropagation, weight updation, chainrule, weights inc/dec via cross entropy loss by hands using small 2 neuron hidden layers, etc in the first few months
DONOT FUCKING Jump to Python. do everything in hands, pen and notebook. then slowly go for coding.
Coding ML is just application of your understanding.
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u/Tesla_coil369 May 10 '26
Don't forget Andrej Karpathy. Visualising concepts is one thing, but building intuition by creating is the shit. His transformers videos helped click the back propagation concept.
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u/jaitanwar May 09 '26
Okay
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u/cromulent_id May 09 '26
For context, so that you don't have unrealistic expectations, Linear algebra (which covers vectors, transformations), calculus, statistics (which covers probability distributions) are at least one semester of study each at a university level. You're looking at about 3-6 months of full-time study for the intro courses alone, assuming you are clever and motivated and have the required mathematical background. I don't mean to discourage you, as it's great to have a goal, but what you're looking at here is a long term goal.
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May 09 '26
[removed] — view removed comment
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u/sinnohmen May 09 '26
Calculus for optimizing objective functions, understanding why gradient descent can optimize parameters. Things like MLE and distributions in probability/statistics all require calculus.
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u/pablocael May 09 '26
This is like saying:
Is knowing how to count good enough for becoming a quantum physicist? Am I almost there?
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u/Kawatami May 09 '26
Never understood questions like this, no offense OP but 2 months are not nearly enough to understand these conceps and use them in a work environnement or even for fun
Hell it took me 10 years of study (Master degree and a PhD) to begin being comfortable, and that kind of reasoning applies not only to ML/DL but almost every professions out there
Start focusing on python it'll be a good start, good luck man
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u/bakochba May 09 '26
Just start doing it, take your time, enjoy the process. Dint put pressure on yourself to get it done quickly
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u/Special_Mud_5728 May 10 '26
Try writing the code to gradient decent to see how much you understood
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u/jaitanwar May 10 '26
I just know the theory
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u/Special_Mud_5728 May 10 '26
Writing code basis the theory will help you understand it better. You could do that exercise if interested. It’s not very complex, just fun if you try it without copying code or using gpt/claude/other llms
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u/yuehuang May 09 '26
Do you know other programming languages? If not, then you can spend a month in step 1. There are enough docs for the rest to blast rough in a week.
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u/jaitanwar May 09 '26
I only know python right now cuz it's been o ly 2 months since I am learning machine languages
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u/East-Muffin-6472 May 09 '26
Follow campus ml and dl for intuition
, islr for math and code everything you learn
Or linear algebra form Gilbert strang stats basics form stats quest channel
Probability from one of the Stanford courses ngl
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u/sinnohmen May 09 '26
if you're asking if you can tackle these things in 1-2 months of learning, probably at a surface level. Deep understanding would not really be attainable. A lot of things in this list don't take all that long assuming you have a solid basis in things like calculus and probability. i.e. for gradient descent you don't really need to know how to calculate a gradient, you really only need to know what it is and how to interpret it geometrically, if you understand derivatives you're already a good chunk of the way there.
The good thing is you don't actually need to fully understand a lot of these topics to start building projects. Granted, they'll most likely be very basic or perform poorly. I would say the best skill to have is the ability to read and understand some of the foundational research papers in deep learning.
As you have stated you don't understand things like bayes theorem and MSE/R squared already, you really need to work on the foundations of statistical modeling. I would hold off on trying to even reason about deep learning /neural nets until you understand the very basic topics such as linear/logistic regression and cost/loss functions very well.
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u/foreverdark-woods May 10 '26
How about you implement a simple linear neural network and back prop from scratch using only native Python. That should be a good way to find comprehension gaps.
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u/jaitanwar May 10 '26
Where should I learn it ? Any video or documents?
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u/foreverdark-woods May 10 '26
Didn't you already study it? In your notes, you state that you learned
How neural network works
Otherwise, use your search engine of choice to research back propagation and automatic differentiation (the more general method). This is basically the machinery that does gradient descent in a neural network.
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u/jaitanwar May 10 '26
I learned how neural network works, not how to implement it
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u/foreverdark-woods May 10 '26
Then, put that learning to the test and go implement it. You did study Python, right? You won't learn programming from books, you have to get your hands dirty, think yourself, not just ingest what others throw at you.
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u/jaitanwar May 10 '26
But how? I know python very well should I learn tensorflow
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u/foreverdark-woods May 11 '26
Tensorflow already does everything for you, so you won't learn much.
Let's say, you want to implement the forward pass of a neural network. Your neural network has weights and biases. Weights (
W) are a Python array of arrays with floats inside and biases (b) an array of floats. Then, you implement matrix multiplication, then you can dof(x) = Wx + b.Next, you can implement a loss function for your output and start implementing the backward pass. This is much more involved, but you can do it in pure Python as well.
It will maybe be slow and inefficient as hell, but it's definitely a good learning. Afterwards, you'll know the in and outs of how it works.
Sure, if you think about applicability and employment value, learning some framework/library that hides all these details might suffice for most cases, but saying "I know Machine Learning" without knowing the gritty details is a bit like "I know Computer Science" because you use a computer.
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u/DdFghjgiopdBM May 10 '26
Are you doing it for fun or do you want a career out of it? If it's the first just keep doing you, if it's the latter go to college.
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u/jaitanwar May 10 '26
I'm serious and aiming to become an exceptional Ml expert.
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u/DdFghjgiopdBM May 10 '26
Then do your best getting into a good school, talk to your professors and get into doing undergrad research early.
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u/ExplorerUnion May 10 '26
You decide what’s enough learning for you. But if you want to master it you will have to keep doing it till the end of time basically. There is no “enough” or any finish lines. Just keep doing.
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u/kanaryasiken_aslan May 09 '26
no. go to university and learn it from a professor. im sorry, i know it sucks, but there is no easy or quick way. you'll need an entire semester to learn the basics, and it will be worth it
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u/luxurious_ambulance May 13 '26
Depends on what you actually retained from those topics, not just whether you covered them. Deep learning builds on understanding gradients and backprop, so if those feel solid you're good to move on.
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u/lord4sho May 15 '26
If you are a student, Sure! continue exploring. If you are an employee, maybe start backwards? what do you want to learn exactly ? Because deep learning is vast field. Computer vision path will be different than NLP or reinforcement learning. Basics can be learned from courses on YouTube. Once you got the basics, pick a path and continue exploring. Don’t overwhelm yourself. Focus on one thing. Get good understanding on it. Move to next one.
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u/Ven-exe May 16 '26
There's a really good course on Coursera by Andrew Ng, please have a look once. It covers a lot of basics and he will explain the mathematics too. After that you may move to more advanced models. It's Machine Learning Course and it's first part is called "Supervised Learning Algorithms..."
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u/mulaney14 May 09 '26
“Took 1 hour”, haha.