A helpful 5-page data science cheatsheet to assist with exam reviews, interview prep, and anything in-between. It covers over a semester of introductory machine learning, and is based on MIT's Machine Learning courses 6.867 and 15.072. The reader should have at least a basic understanding of statistics and linear algebra, though beginners may find this resource helpful as well.
Inspired by Maverick's Data Science Cheatsheet (hence the 2.0 in the name), located here.
Topics covered:
- Linear and Logistic Regression
- Decision Trees and Random Forest
- SVM
- K-Nearest Neighbors
- Clustering
- Boosting
- Dimension Reduction (PCA, LDA, Factor Analysis)
- Natural Language Processing
- Neural Networks
- Recommender Systems
- Reinforcement Learning
- Anomaly Detection
- Time Series
- A/B Testing
This cheatsheet will be occasionally updated with new/improved info, so consider a follow or star to stay up to date.
Future additions (ideas welcome):
Time SeriesAdded!Statistics and ProbabilityAdded!- Data Imputation
- Generative Adversarial Networks
Here are screenshots of a couple pages - the link to the full cheatsheet is above!
I planned for this resource to cover mainly algorithms, models, and concepts, as these rarely change and are common throughout industries. Technical languages and data structures often vary by job function, and refreshing these skills may make more sense on keyboard than on paper.
Feel free to share this resource in classes, review sessions, or to anyone who might find it helpful :)
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
Images are used for educational purposes, created by me, or borrowed from my colleagues here
Feel free to suggest comments, updates, and potential improvements!
Author - Aaron Wang