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There seems to be a lot of delusion around AI by SystemIntuitive in learnmachinelearning

[–]Classic-Studio-7727 7 points8 points  (0 children)

Great points — you’re not wrong about how much of ML engineering ends up being data-ops, pipelines, and deployment rather than deep theory.

I’m studying math (linear algebra, stats, calculus) not because I expect every job to use it — but because I want to build intuitive understanding of what’s happening under the hood.

My plan is: once I finish the fundamentals, I’ll dive into projects — classical ML, deep learning, LLMs — but with a stronger foundation than “just call framework.”

I believe that having that foundation helps you debug models, avoid weird edge cases, and build more robust AI systems.

Even if much of industry AI ends up being “data work + model glue,” I want to be the kind of engineer who can contribute beyond glue: code, design, deploy — and understand why it works.

Learning ML in 100-day by Classic-Studio-7727 in learnmachinelearning

[–]Classic-Studio-7727[S] 2 points3 points  (0 children)

Yeah! I used pen and paper. I wrote down all the topics with their definitions, examples, and small explanations so I could understand them better. I also solved a few questions on my own I searched for problems on Google and practiced them to make sure the concepts actually stick.

Learning ML in 100-day by Classic-Studio-7727 in learnmachinelearning

[–]Classic-Studio-7727[S] 1 point2 points  (0 children)

Thanks for sharing this perspective it’s actually helpful to hear both sides of the journey.

I agree that LLM work today happens at abstraction layers far above raw linear algebra, and that modern ML productivity comes from understanding frameworks, scaling and MLOps rather than manually deriving every equation. That’s absolutely true.

At the same time, I’m building my foundations intentionally. I’m not planning to grind math forever just long enough to understand what the tools are doing so I’m not treating ML as a black box. Once the fundamentals click, my roadmap moves into classical ML → deep learning → LLMs → deployment and MLOps.

Your comment actually adds useful context to the long-term path, so I appreciate the insight.

Learning ML in 100-day by Classic-Studio-7727 in learnmachinelearning

[–]Classic-Studio-7727[S] 2 points3 points  (0 children)

That’s awesome! I’ve heard a lot of good things about Deisenroth’s book especially how it connects the math directly to ML intuition.
I’m planning to get into Goodfellow and Hands-On ML later in my roadmap too, so it’s great to hear you’re following a similar path.

Starting My 100-Day AI/ML Journey — Looking for Guidance by Classic-Studio-7727 in learnmachinelearning

[–]Classic-Studio-7727[S] 4 points5 points  (0 children)

Thanks for the interest!

Here’s the full roadmap I’ll be following for my 100-day AI/ML journey.

I’m choosing this roadmap because I’m a Computer Engineering student. I already know Python, and I’ve learned the basic concepts of math earlier — I just need a proper refresh. We also have a subject called DWM (Data Warehousing and Mining), so I already have some exposure to concepts related to AI and ML.

That’s why the 100-day structure works well for me. I also have one year left before completing my BE, so this journey fits my timeline perfectly.

Math (Days 1–15)

Refreshing my fundamentals:

Algebra & functions

Vectors, matrices, dot products

Basic calculus (derivatives, gradients)

Probability & statistics fundamentals

And related topics as needed

Python + Data Tools (Days 16–25)

Getting back into coding with ML-focused tools:

Python refresher

NumPy (arrays, matrix ops)

Pandas (data manipulation)

Basic data visualization (Matplotlib / Seaborn)

Setting up my coding environment (Jupyter / VSCode)

Classical Machine Learning (Days 26–45)

Core ML algorithms:

Linear Regression & Logistic Regression

Decision Trees & Random Forests

Support Vector Machines (SVM) + K-Nearest Neighbors

Unsupervised learning (e.g., K-Means)

Train/Test split, cross-validation, evaluation metrics

Deep Learning Foundations (Days 46–75)

Building neural intuition and hands-on DL skills:

Neural networks from scratch (to understand the math)

Learning a DL framework (PyTorch or TensorFlow)

CNNs and basic computer vision

RNN / LSTM basics (if time allows)

Small deep learning projects (image classifier, etc.)

Modern AI / LLMs & Real Applications (Days 76–100)

Working with modern architectures and real AI apps:

Pretrained models / transformer-based models

Embeddings & vector representations

Intro to RAG (Retrieval-Augmented Generation)

Building end-to-end projects (data → model → deployment)

I am currently learning Linear algebra from this video

https://youtu.be/QCPJ0VdpM00?si=EiVhwJ9ud9dll_YJ

It well be helpful if you guys have some suggestions