Data Science Cheatsheet 2.0
A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between. This resource is not meant to be a comprehensive deep dive into any specific model, but rather a quick refresher on a few of the most fundamental machine learning algorithms. The reader should have at least a basic understanding of statistics and linear algebra, though beginners may find this cheatsheet 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/star to stay up to date.
Future additions (ideas welcome):
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Time SeriesAdded! - …
