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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 667 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 9 381-o'rinni va Hindiston mintaqasida 31 693-o'rinni egallagan.

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“Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

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Postlar arxiv

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R Cheatsheet - Part 3

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Projects To Learn AI and LLM Engineering

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PCA Dimensionality Reduction Cheatsheet

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R Cheatsheet - Part 2

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The Curse of Dimensionality 🧩 Here’s something that trips up many beginners: More features ≠ always better. When your dataset has too many features (dimensions), weird things happen: ⛔️ Distances between points become meaningless. ⛔️ Models struggle to generalize. ⛔️Training time explodes. 👉 Solution: techniques like PCA, feature selection, or just collecting smarter data instead of more data. Remember: Adding noise isn’t adding information.

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R CHEATSHEET - Part 1

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Data Structure

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SQL for Data Science 📈.pdf2.25 KB

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Overfitting vs Underfitting 🎯 Why do ML models fail? Usually because of one of these two villains: Overfitting: The model memorizes training data but fails on new data. (Like a student who memorizes past exam questions but can’t handle a new one.) Underfitting: The model is too simple to capture patterns. (Like using a straight line to fit a curve.) The sweet spot? A model that generalizes well. Note: Regularization, cross-validation, and more data usually help fight these problems.

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AI vs ML vs Deep Learning 🤖 You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not. AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning. ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed. Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc. Think of it this way: AI = Science ML = A chapter in the science Deep Learning = A paragraph in that chapter.

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Mathematical Foundations For Deep Learning

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Neural Networks and Deep Learning by Michael Nielsen.pdf5.82 MB

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🔥 Building models is fun… but here’s the real test: is your model actually any good, or just pretending? 👀 Topic: Evals in Data Science Evaluations—or evals—are our model’s report card. They tell us: - For a spam filter: Do we catch all spam (recall) without misclassifying grandma’s emails as junk (precision)? - For price prediction: How close are our predictions on average (RMSE)? But evals aren’t just about numbers—they influence trust, fairness, and real-world usefulness of our models. Discussion prompts: - What’s your go-to evaluation metric and why? - Seen a model that looked great on paper but flopped in reality? - Should fairness & usability be considered first-class evaluation metrics alongside accuracy? Free book to dive deeper: - Fairness and Machine Learning — rigorous, practical guide to evaluating models for fairness: https://fairmlbook.org/ Drop your thoughts below ⬇️

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TOP ML Interview Problems

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Machine_Learning_For_Dummies_by_John_Paul_Mueller,_Luca_Massaron.pdf11.81 MB

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Jupyter Notebook Basics.pdf7.43 KB