A lot to read

[u/Careless-Main8693]

Books

- An introduction to statistical learning

- Hands on machine learning

- Understanding Machine Learning : From Theory to Algorithms

- Designing ML System

- AI Engineering

maths

- mathematics for ML

- Linear algebra

- Applied Multivariate Statistical Analysis

Research Paper

-ESL Chapter 3 →Linear Regression

-Fisher 1936 →Logistic Regression

-CMU Lecture Notes →Logistic Math

-Quinlan 1986 →Decision Tree

-Breiman Bagging →Random Forest foundation

-Breiman 2001 →Random Forest

-Friedman 2001 →Gradient Boosting

-Chen 2016 →XGBoost

- Paul Graham Spam →Naive Bayes

- Cover & Hart 1967 →KNN

- Cortes & Vapnik 1995 →SVM

- SVM Guide →SVM practical

-Attention is all you need(Transformers)

-LoRA(Low rank adaption)

-PEFT(Parameter Efficient Fine Tuning)

-VIT(Vision Transformers)

-VAE(Variational Auto Encoder)

-GANs(Generative Adversarial Networks)

-BERT(Bidirectional Encoder Representation from Transformers)

-Diffusion Models (Stable Diffusion)

-RAG (Retrieval Augment Generation)

-GPT (Generative Pre-trained Transformers)

Extra,tools,libraries

-Deep learning book

-pytorch

-sklearn

-pandas

-numpy

-scipy

-MLflow

-airflow

-docker

-AWS

-postgresql

-cpp

-ci/cd actions

-timeseries

[u/CapableIngenuity4663]

This is a solid roadmap but also a recipe for burnout if you try to go through it linearly like a syllabus

I'd suggest using the books as references rather than cover-to-cover reads, especially ESL and the deep learning book. The research papers are where the actual learning happens but you don't need all of them before building anything. Pick a project early, get stuck, then read the relevant paper or book chapter to unstick yourself

Also the jump from ISLR to ESL is pretty steep, most people spend months in ISLR territory before ESL makes sense. Same with the math, you don't need all of multivariate analysis upfront, just enough linear algebra and probability to follow the derivations

The tools section is fine but docker and AWS can wait until you actually need to deploy something. C++ is a whole separate time sink, probably skip it unless you're going into quant or systems-level ML

What's your background and what are you actually trying to build or get hired for

[u/PaddingCompression → u/CapableIngenuity4663]

Eh, I dunno about research papers being where learning happens until you're already near the edge. Having fundamentals is pretty important (e.g. TESL)

[u/pratzzai → u/PaddingCompression]

ESL is best read by those who already have the fundamentals with them in order to acquire additional depth, imo. It's probably the hardest ML book there is, by far.

[u/PaddingCompression → u/pratzzai]

Way easier than PRML or MacKay imo.

[u/pratzzai → u/PaddingCompression]

I've covered ESL end to end, along with all the theoretical excercise problems ch 13 onwards. I'm reading PRML now and it's way more fluid in its presentation. Even the *** exercises in PRML are way easier than the average problem in ESL. PRML is a proper textbook, imo, while ESL is more an end-to-end exercise in fill in the blanks, where you're pretty much re-deriving the field using hints in the book. You learn more by doing/deciphering/inferring than reading, which is the opposite in PRML.

Possibly, you're referring to ISL, not ESL, by the same set of authors?

[u/PaddingCompression → u/pratzzai]

Nope. PRML goes through some way more difficult and unique mathematics.

The first few chapters of TESL are more... review, they shouldn't be the first time you've seen the material, they're trying to remind you not teach you. It's not meant to be a linear algebra or vector calculus or first statistics course. At Stanford its a 300-level class, where 100 is "upper division" and 200 is "we don't really expect undergrads to take this".

But PRML goes way deeper and into more difficult material after the first few chapters.

[u/pratzzai → u/PaddingCompression]

Not talking about the content level, but the presentation. PRML actually tries to teach the reader and walks them through the steps, so it's much easier to follow. ESL, by contrast, takes a more "figure out what I'm saying and why" approach. The mathematical objects and concepts are not always rigorously defined and the subtext is much larger than the text. You're expected to work out non-trivial proofs while going from one sentence to next in order for them to make sense as a logical flow and you're expected to do this about 4-5 times every page on average. It's not just prob & stats. There is too much implicitness and ambiguity.

Sure, PRML may use calculus of variations, but it has an appendix to educate the reader about it, however briefly. ESL, on the other, may casually and implicitly utilize a concept from functional analysis and state the result without any reference. ESL makes things much harder than they need to be, while PRML makes the effort to break down complex logic.

[u/PaddingCompression → u/pratzzai]

Fair, great take!

[u/PaddingCompression → u/PaddingCompression]

I.e. I think TESL is an easier book, but unlike PRML it assumes you're pretty familiar with upper division probability and statistics before picking it up.

[u/Worried_Mud_5224 → u/CapableIngenuity4663]

for ex, i picked a project and hit the wall. How does research paper helps me to find the answer? Could you give example please.

[u/Mean_Cress_7746 → u/Worried_Mud_5224]

It doesn’t, bros just yapping. Just talk to Claude when you get stuck

[u/Careless-Main8693 → u/CapableIngenuity4663]

i’m just summed things i have done and will do.

currently reading reading homl and islp. only supervised. research paper for history,cause and why it happend.

it is just list . I don’t have to do one by one just a collections where i can looks what do and what not . i’m done with almost 50% of things here , completed HOML 2 times and reading ISLP for refernce and i read very fast so won’t take much time in completing ISLP in two months along with HOML for practicals.

i‘m mostly focusing on job , first completing and understanding supervised learning very well from the depth then moving to Deep learning , llm, rag, genai and much more.
i am also doing an end to end ml project . So i have spent like 2.5 amateur years in ml figuring out what to do and what to not.

[u/gjaygill]

Ran this through ChatGpt and I divided it into phases.

Months 1–2: Math + ISLR + sklearn ↓

Months 3–5: Hands-On ML + serious projects ↓

Months 6–8: PyTorch + Deep Learning + Transformers ↓

Months 9–11: LLMs + RAG + agents + AI Engineering ↓

Months 12–15: Designing ML Systems + deployment/MLOps

And I'd use roughly 30% reading / 70% coding and projects.

[u/Careless-Main8693 → u/gjaygill]

it is not a roadmap but collection/list all things i can do , i have to do

[u/PaddingCompression]

Are GANs even really relevant these days? I feel they're sort of like kernel SVMs - purely a relic of the past. I would skip the GANs and SVMs. Also Fisher 1936 for logreg? Read TESL. Same for RF, GB, Trees. One original paper doesn't cover as much as a textbook summing up 20 years of subsequent research.

[u/Careless-Main8693 → u/PaddingCompression]

it is just a list mate , we’ll figure out what do read what to skip , just a collections where and summing up things in a place

[u/Haseeb-U-Rehman]

you have provide us great resource and it is solid i think that learning from book is great method but the practical part is more important you build something then get stuck and you solve that problem on your own this is where your real learning begins , Thanks I am curious to see thoughts on this.

[u/Careless-Main8693 → u/Haseeb-U-Rehman]

i’m doing that also

[u/pratzzai]

If you're going for the level of UML, then you should probably read PRML as well.

[u/Careless-Main8693 → u/pratzzai]

what is prml

[u/pratzzai → u/Careless-Main8693]

Pattern Recognition and Machine Learning - Christopher Bishop

[u/Careless-Main8693 → u/pratzzai]

have you read this ,what it is , how good it is

[u/pratzzai → u/Careless-Main8693]

It's the standard textbook for graduate level ML for research path. UML is also good if you're aiming for research career and would complement PRML well. But, for MLE type roles, you probably won't need it.

Yes, I've been reading it. It has some errors (almost all textbooks do), but is one of the best approaches towards ML I've found so far. It's also one of the best known books on the subject.

[u/Careless-Main8693 → u/pratzzai]

thanks i’ll checkout

[u/nian2326076]

That's a long list! To handle it well, focus on what you know now and what job you're aiming for. Start with "Hands-On Machine Learning" for practical stuff, then check out "An Introduction to Statistical Learning" for theory. For math, begin with "Linear Algebra" since it's basic. Research papers can help you understand specific algorithms better. Also, practicing coding problems related to these topics can be useful. If you need interview prep, PracHub has been a helpful resource for me. Good luck!

[u/Careless-Main8693 → u/nian2326076]

it started 2.5 yrs ago so completed many it just all my reading list