The math you actually need to know for data science.

#datascience #machinelearning #math

Timing Breakdown
0:00 - Intro & Overview
1:53 - Statistics & Probability
4:14 - Linear Algebra
6:10 - Mathematical Optimization
8:28 - Calculus
11:05 - Specific data Science tasks & math you need to know for them
13:33 - Final Thoughts

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Coursera Specialization - Python for Everybody: https://cutt.ly/6bhlTne

🍓🍍JUICY LINKS🍍🍓
Definition of statistics: https://www.merriam-webster.com/dictionary/statistics
Definition of probability: https://en.wikipedia.org/wiki/Probability
Statistics terms for data science summary: https://towardsdatascience.com/probability-and-statistics-for-data-science-part-1-3eed6051c40d
Probability terms for data science summary:
https://www.analyticsvidhya.com/blog/2017/02/basic-probability-data-science-with-examples/
Definition of Linear Algebra:
https://www.merriam-webster.com/dictionary/linear%20algebra
Basic Linear algebra term definitions: https://towardsdatascience.com/basics-of-linear-algebra-for-data-science-9e93ada24e5c#
Mathematical Optimization Definition: https://en.wikipedia.org/wiki/Mathematical_optimization
Optimizations in machine learning: https://towardsdatascience.com/demystifying-optimizations-for-machine-learning-c6c6405d3eea
https://serokell.io/blog/ml-optimization
Calculus for Data Science: https://towardsdatascience.com/calculus-in-data-science-and-its-uses-3f3e1b5e5b35
Math in deep learning: https://towardsdatascience.com/introduction-to-math-behind-neural-networks-e8b60dbbdeba
Experimental Design: https://www.scribbr.com/methodology/experimental-design/
Another take on the math you need for data science: https://medium.com/s/story/essential-math-for-data-science-why-and-how-e88271367fbd
Anotha one: https://www.kdnuggets.com/2018/09/essential-math-data-science.html

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