My notes in Jupyter Notebooks for statistics, probability, and plotting applied with major python libraries as an introduction to machine learning.
Launch on Binder:
- Zedstatistics by Justin Zeltzer: basic statistics, very awesome
- Brandon Foltz: foundations of statistics and probability as an extra awesome prof
- jbstatistics by Jeremy Balka: Professor's introductory statistics course to non-statistics majors
- StatsQuest by Josh Starmer: Statistics and Machine Learning
- Kimberley Fessel: Plotting with Matplotlib and Seaborn
- Become a Probability & Statistics Master by Krista King
- Probability and Statistics for Business and Data Science by Jose Portilla
- Python for Data Science and Machine Learning Bootcamp by Jose Portilla
- Python for Time Series Analysis by Jose Portilla
- stats101 by Phil Mike Jones: Jupyter notes on statistical analysis and quantitative methods for social science researchers.
- Stats101 by piramolk: review and recap statistical concepts
- Cross Validated: Stackexchange platform for statistics
- Statistics Lectures: Foundations of statistics
- Lumen Learning: Intro to statistics with examples
- PennState Uni: Stat 414 lessons
- Bayesian Methods for Hackers: Probabilistic Programming & Bayesian Methods for Hackers
- Continuous Statistical Distributions by Scipy
- Scipy Lecture Notes: "One document to learn numerics, science, and data with Python"
- Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos, Monash University, Australia
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