A Collection of High-Quality & Free Data Science Resources

Time series

  1. Video: ritvikmath’s “Time Series Talk” playlist (available on YouTube)

The distinction between typical regression (interpolation) and time series prediction (extrapolation) is particularly illuminating

  1. Open book: Hyndman and Athanasopoulos “Forecasting: Principles and Practice”
  2. Video: Aileen Nielsen’s Scipy 2019 tutorial: Moden Time Series Analysis
  1. Video: Sean Law’s Modern Time Series Analysis with STUMPY

Feature selection

  1. Video: Caltech big data summer school lecture by David Thompson: Feature selection

Feature engineering

  1. Library documentation: Feature-engine — A Python library for feature engineering for machine learning

Neural network fundamentals

  1. Videos and articles: Deeplizard Machine learning & deep learning fundamentals

Computer science

  1. Video: Art of the problem’s “What is complexity theory” (The concept of polynomial time is well explained)
  2. Video: Sorting algorithms by Computerphile

Optimization

  1. Video: Genetic algorithms by Computerphile
  2. Articles: Optimization guide by neos

Recommendation system

  1. Video: PyConCA talk by Jill Cates: How to design and build a recommendation system pipeline in Python
  2. Video: Basic idea on “how recommender systems work” by Art of the Problem
  3. Video: Collaborative filtering by ritvkmath
  4. Video: eBay teck talk: Deep learning for recommender system
  5. Python libraries: Surprise | LightFM

Information criterion

  1. Video: Nathan Kutz lecture on Model selection: information criteria
  2. Video: ritvikmath time series model selection AIC&BIC
  3. Video: ANOVA vs. AICc by YouTube channel Methods in Experimental Ecology I

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Data & Machine Learning Scientist

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Shan Dou

Shan Dou

Data & Machine Learning Scientist

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