AI and ML Roadmap

🧠 AI/ML Foundation Roadmap

📚 Math Fundamentals

Linear Algebra

Cheatsheet:
- Vectors: magnitude/direction
- Matrices: transformations
- Eigenvalues/vectors: 📉 scaling factors
- Matrix operations: ⊕ ⊗

Calculus

Key Concepts:
- Derivatives: rate of change 📈
- Partial derivatives: multivariable functions
- Gradient: ∇ (steepest ascent)

💻 Programming Core

Python Essentials

Must-know:
- NumPy: np.array([1,2,3])
- Pandas: df.groupby()
- Matplotlib: plt.plot()

🤖 Machine Learning Core

Supervised Learning

Regression

Algorithms:
- Linear Regression: y = mx + b 📐
- Decision Trees: split features 🌳
- Regularization: L1/L2

Neural Networks

Components:
- Layers: Input → Hidden → Output
- Activation: ReLU, Sigmoid
- Backpropagation: chain rule ↔

🚀 Advanced ML

Natural Language Processing

Concepts:
- Tokenization: split text 🔤
- Embeddings: word2vec, GloVe
- Transformers: 🤖 (BERT, GPT)

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