Mathematics-for-Machine-Learning

This is the repository of ML Math foundation certification course I completed. Feel free to check the notebooks for more information

Tech stack: PyTorch, TensorFlow and numpy.

There are eight subjects covered in this repository organized into four subject areas.

Linear Algebra
- 1: Intro to Linear Algebra
- 2: Linear Algebra II: Matrix Operations
Calculus
- 3: Calculus I: Limits & Derivatives
- 4: Calculus II: Partial Derivatives & Integrals
Probability and Statistics
- 5: Probability & Information Theory
- 6: Intro to Statistics
Computer Science
- 7: Algorithms & Data Structures
- 8: Optimization

Name		Name	Last commit message	Last commit date
Latest commit History 23 Commits
1-intro-to-linear-algebra.ipynb		1-intro-to-linear-algebra.ipynb
2-linear-algebra-ii.ipynb		2-linear-algebra-ii.ipynb
3-calculus-i.ipynb		3-calculus-i.ipynb
4-calculus-ii.ipynb		4-calculus-ii.ipynb
4.1-regression-in-pytorch.ipynb		4.1-regression-in-pytorch.ipynb
4.2-single-point-regression-gradient.ipynb		4.2-single-point-regression-gradient.ipynb
4.3-batch-regression-gradient.ipynb		4.3-batch-regression-gradient.ipynb
4.4-gradient-descent-from-scratch.ipynb		4.4-gradient-descent-from-scratch.ipynb
4.5-SGD-from-scratch.ipynb		4.5-SGD-from-scratch.ipynb
4.6-learning-rate-scheduling.ipynb		4.6-learning-rate-scheduling.ipynb
5-probability.ipynb		5-probability.ipynb
6-statistics.ipynb		6-statistics.ipynb
7-algos-and-data-structures.ipynb		7-algos-and-data-structures.ipynb
8-optimization.ipynb		8-optimization.ipynb
README.md		README.md

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