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How I actually learned to code (no CS degree): 1. CS50 – for fundamentals and thinking like a computer 2. YouTube (selectively, not binge-watching) – freeCodeCamp – Abdul Bari (DSA intuition) – MI
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I wasted a lot of time doing ML projects without understanding the math underneath. If I had to start again, this is the ONLY order I’d follow: 1. Linear Algebra – vectors, matrices, eigenvalues – wh
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5 ways to actually understand ML : 1. Use AI as a tutor, not a crutch It's tempting to just ask Claude or ChatGPT to explain everything. Use it to check your understanding instead. Explain a concept
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which one are you lowkey obsessed with?
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1. NumPy Arrays, vectorized operations, broadcasting - ML libraries are built on top of this. You'll use it constantly. 2. Pandas Loading, cleaning, and manipulating datasets. Real-world data is mess
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4 tools that actually help you manage real ML workflows: W&B helps tracks your experiments, metrics, and hyperparameters so you can actually compare what’s working https://wandb.ai/ MLflow helps man
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1. Most of ML is data, not models. Cleaning, labeling, and understanding data takes far more time than training fancy models. 2. Math matters more than frameworks. Libraries like PyTorch or TensorFlo
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4 ML papers to read this weekend instead of touching grass. Attention Is All You Need Yes, everyone talks about this one, but that’s because it completely changed the direction of AI. This paper intr
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Links: jbstatistics: https://youtube.com/@jbstatistics?si=7M-29J9w4LXOzmNz ritvikmath: https://youtube.com/@ritvikmath?si=bA7iqqp2OueB6N5K Brandon Foltz: https://youtube.com/@brandonfoltz?si=G_WafP
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Explain a concept simply. Find the gaps in your understanding. Refine the explanation. If you can clearly explain these concepts, you probably understand machine learning beyond just using libraries.
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Small setup changes, but these extensions make iteration way less painful in ML. These are the VS Code tools I’ve been using lately.
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I think machine learning is truly beautiful when you appreciate the math behind it. These books teach you what’s really going on under the hood and not just how things work, but why they work. And hon
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About @workiniterations

@workiniterations is a Instagram micro influencer. With 17.5K followers and 12 posts, workiniterations's account shows a follower-to-following ratio of 205.9:1. This profile demonstrates strong community engagement within its niche. View all content anonymously on Pictame - no login required.

workiniterations's engagement rate sits at 39.46%. That's 1478% above what you'd typically see for accounts with 10K-100K followers (average 2.5%) — a sign of a genuinely connected audience.

Out of the last 12 posts, 75% are videos and 25% are photos. Notably, videos tend to get 13.9x more engagement than photos. Content shows up about every 10 days. Captions lean detailed, averaging 662 characters.

39.46% engagement rate — 1478% above average for 10K-100K follower accounts.
75% videos, 25% photos. Videos get 13.9x more engagement here.
Posts about every 10 days.
205.9:1 follower-to-following ratio.

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workiniterations
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@workiniterations

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#machinelearning#learncoding#programming#computerscience#mlstudent#STEM#math#MachineLearning

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Making machine learning intuitive 20 | Physics and math background Interested in scientific ML ML roadmap I use 👇

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