Machine learning is one of the most transformative fields in technology today. From powering recommendation systems to enabling self-driving cars, machine learning is at the core of modern artificial intelligence. However, mastering its vast concepts, equations and algorithms can be overwhelming especially for beginners and busy professionals.
That’s where the Stanford CS229 Machine Learning Cheatsheets by Afshine Amidi and Shervine Amidi come in. These concise, beautifully designed notes summarize key topics from Stanford’s world-renowned CS229 course, taught by Professor Andrew Ng. Available in multiple languages and freely accessible online, these cheatsheets have become a go-to study companion for students, researchers and data scientists worldwide.
What Is the Stanford CS229 Cheatsheet Repository?
The Stanford CS229 Machine Learning Cheatsheet repository is an open-source project hosted on GitHub. It organizes the entire Stanford CS229 syllabus into easy-to-understand visual summaries covering everything from supervised and unsupervised learning to deep learning and statistical refreshers.
The project’s goal is to make complex ML topics accessible to everyone, regardless of background. The repository includes:
- Supervised Learning Cheatsheet
- Unsupervised Learning Cheatsheet
- Deep Learning Cheatsheet
- Tips & Tricks Cheatsheet
- Probabilities and Statistics Refresher
- Algebra and Calculus Refresher
- Super VIP Cheatsheet — an all-in-one ultimate summary
All these are also available in 11+ languages including English, French, Spanish, Arabic, Chinese, Portuguese and Vietnamese making it one of the most inclusive learning resources in the ML community.
Why Stanford’s CS229 Course Is Legendary ?
Stanford University’s CS229: Machine Learning is one of the most popular and influential ML courses in the world. Created by Andrew Ng, co-founder of Coursera and a leading AI researcher, CS229 has shaped how millions of learners understand and apply machine learning.
The course covers:
- Linear and logistic regression
- Support vector machines (SVMs)
- Neural networks
- Clustering algorithms
- Dimensionality reduction
- Anomaly detection
- Reinforcement learning
The cheatsheets by Afshine and Shervine Amidi compress this wealth of knowledge into concise visually engaging notes making it easier to grasp the essence of each topic.
Key Highlights of the Cheatsheets
1. Supervised Learning
This section focuses on algorithms that learn from labeled data. It includes:
- Linear Regression
- Logistic Regression
- Support Vector Machines
- Decision Trees
- Random Forests
- Gradient Boosting
Each concept is illustrated with formulas, practical insights and when to use which model.
2. Unsupervised Learning
For datasets without predefined labels, this cheatsheet explains techniques such as:
- K-Means Clustering
- Gaussian Mixture Models
- Principal Component Analysis (PCA)
- Independent Component Analysis (ICA)
It highlights how unsupervised learning helps discover hidden structures and patterns in data.
3. Deep Learning
This section introduces neural networks and deep learning architectures including:
- Perceptrons and Multilayer Neural Networks
- Activation Functions (ReLU, Sigmoid, Tanh)
- Backpropagation
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
It also includes best practices for training, optimization and avoiding overfitting.
4. Tips and Tricks
This is a must-read for practitioners. It covers:
- Hyperparameter tuning strategies
- Regularization techniques (L1, L2, Dropout)
- Gradient descent variants (SGD, Adam, RMSProp)
- Model evaluation metrics (Accuracy, Precision, Recall, F1-score)
These practical insights are especially useful for improving model performance in real-world applications.
5. Refreshers: Math Foundations
Machine learning is built on a foundation of linear algebra, calculus, probability and statistics.
The repository includes two special refreshers:
- Algebra and Calculus Refresher: Matrix operations, derivatives and gradients.
- Probabilities and Statistics Refresher: Distributions, expectations, variance and Bayes’ theorem.
These sections ensure learners can understand the math behind the algorithms not just the code.
The Super VIP Cheatsheet
For those who prefer everything in one place, the Super VIP Cheatsheet combines all the sections into a single comprehensive document. It’s ideal for exam preparation, quick revision or interviews. You can print it, keep it on your tablet or refer to it anytime you need a fast recap of ML theory.
Multilingual Access and Global Reach
One standout feature of the project is its multilingual support. Thanks to contributions from the open-source community. This inclusivity has made it a global learning hub from students in France and India to engineers in Silicon Valley.
How to Access the Cheatsheets ?
You can explore the project in two ways:
- GitHub Repository
Visit the official GitHub page
Clone or download the PDF cheatsheets and use them offline. - Official Website
Visit the website
Browse all the materials interactively from your desktop or mobile device.
Why These Cheatsheets Are Perfect for You ?
If you are:
- Preparing for a data science or ML interview
- Taking an online machine learning course
- Reviewing concepts before exams
- Looking for structured revision notes
Then these cheatsheets will become your best friend. They condense hundreds of pages of ML theory into a few easy-to-read summaries, perfect for quick recall and understanding.
Conclusion
The Stanford CS229 Machine Learning Cheatsheets by Afshine and Shervine Amidi are a goldmine for anyone learning AI or data science. They simplify complex theories bridge the gap between math and intuition, and offer clear visuals for fast comprehension.
Whether you’re a student, researcher, or working professional, these cheatsheets will help you learn smarter, revise faster, and apply machine learning concepts with confidence.
Explore them today and take your machine learning journey to the next level.
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