Machine Learning cheatsheets form Stanford's CS 229:
1️⃣ Supervised Machine Learning
Covers:
- Notations & general concepts
- Linear regression
- Generalised linear models
- Gaussian discriminant analysis
- Tree based & ensemble methods
🔗 tinyurl.com/CS229-Supervis…
2️⃣ Unsupervised Machine Learning
Covers:
- PCA
- K-means clustering
- Hierarchical clustering
- Expectation maximization
- Clustering evaluation metrics
🔗 tinyurl.com/CS229Unsupervi…
3️⃣ Deep Learning
Covers:
- Neural networks
- Convolutional neural networks
- Recurrent neural networks
- Reinforcement learning & control
🔗 tinyurl.com/CS229DeepLearn…
4️⃣ ML Tips & Tricks
Covers:
- Model selection
- Model Evaluation
- Diagnostics (Bias/Variance)
🔗 tinyurl.com/CS229MLTipsTri…
5️⃣ Probability & Statistics
Covers:
- Random Variables
- Probability & combinatorics
- Conditional probability
- Parameter estimation
🔗 tinyurl.com/CS229ProbStats
6️⃣ Algebra & Calculus
Covers:
- General notations
- Matrix operations
- Matrix properties
- Matrix calculus
🔗 tinyurl.com/CS229LinalgCal…
If you interested in:
- Python 🐍
- ML/MLOps 🛠
- CV/NLP 🗣
- LLMs/AI Engineering ⚙️
Find me → @akshay_pachaar ✔️
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