A very helpful 5-pages Data Science Cheatsheet - to assist with quick recalling, exam reviews, interview preparation, and anything in-between.
It covers 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.
Topics included:
• 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
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Full credit goes to the compiler Aaron Wang (https://www.linkedin.com/in/axw/)
Github repository (source) => https://lnkd.in/eJKjmBN from here, you can download PDF.
Inspired by Maverick's Data Science Cheatsheet => Link is here.
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