Machine Learning with Python

Most AI engineers never fully understood the maths behind what they build! 🤯🧮 This is an open, unconventional textbook covering maths, CS, and AI from the ground up, written for curious practitioners who want to deeply understand the field, not just survive an interview. 📘✨ Over 7 years of AI/ML experience distilled into intuition-first, no hand-waving explanations that connect the concepts in a way that actually sticks. 🧠🔗 What it covers: - Vectors, linear algebra, calculus, and optimization 📐📉 - Classical machine learning and deep learning 🤖 - Transformer architectures and LLMs 🦄 - Efficient architectures, quantization, and distillation ⚡️ - CUDA, GPU programming, and SIMD 🚀 - AI inference and deployment 🌐 Ships with an MCP server so Claude Code, Cursor, and any MCP-compatible agent can use the compendium as a live knowledge base during development. You only need elementary maths and basic Python to start. 🐍🏗 Repo: https://github.com/HenryNdubuaku/maths-cs-ai-compendium 🔗

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Here are the 25 ML feature engineering techniques

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