24. Statistics | Maths for Machine Learning

Veröffentlicht am: 28 Juni 2026
auf dem Kanal: AI with KADER

Mathematics for Machine Learning
Master the mathematics behind Machine Learning, Deep Learning, and modern AI from first principles.
This complete 34-part Mathematics for Machine Learning course covers all the essential mathematical concepts used in AI, including algebra, functions, linear algebra, calculus, optimization, probability, statistics, information theory, vectors, matrices, eigenvalues, singular value decomposition (SVD), gradient descent, neural network mathematics, and the mathematical foundations of transformers and large language models.
Every lesson combines intuitive explanations, visual animations, mathematical derivations, and executable Python examples using NumPy and PyTorch.

Course Notebook
https://github.com/kader-xai/ml-cours...

Course Repository
https://github.com/kader-xai/ml-cours...

If you enjoy this course, these playlists are a great next step:
Machine Learning Series
   • Machine Learning Series
Scikit-Learn Series
   • SciKit Learn Series
Machine Learning from Scratch
   • Machine Learning from Scratch
Data Science with Python
   • Data Science with Python
AI Agents with LangGraph
   • AI Agents with LangGraph
XGBoost for CyberDefense
   • XGBoost for CyberDefense
Neural Network Optimization
   • Neural Network Optimization
Hugging Face Transformers
   • Hugging Face Transformers
PyTorch: Build Your Own GPT
   • Pytorch : Build your own GPT
TensorFlow from Scratch
   • Tensor Flow from scratch

Course Structure
FOUNDATIONS
1. Numbers & Algebra
2. Functions & Graphs
3. Trigonometry & Geometry
4. Set Theory & Logic
5. Sequences, Series & Limits
LINEAR ALGEBRA
6. Vectors & Vector Spaces
7. Matrices & Matrix Operations
8. Matrix Transformations
9. Dot Products & Projections
10. Matrix Factorization
11. Eigenvalues & Eigenvectors
12. Singular Value Decomposition (SVD)
13. Principal Component Analysis (PCA)
CALCULUS & OPTIMIZATION
14. Limits & Continuity
15. Derivatives
16. Partial Derivatives
17. Gradients & Jacobians
18. Chain Rule & Backpropagation
19. Hessians & Second-Order Optimization
20. Gradient Descent
PROBABILITY & STATISTICS
21. Probability
22. Probability Distributions
23. Bayes' Theorem
24. Statistics
25. Information Theory
26. Maximum Likelihood Estimation (MLE)
MATHEMATICS OF MACHINE LEARNING
27. Linear Regression Mathematics
28. Logistic Regression Mathematics
29. Neural Network Mathematics
30. Optimization & Regularization
MODERN AI MATHEMATICS
31. Attention Mathematics
32. Transformer Mathematics
33. Embeddings & Vector Spaces
34. Mathematics Behind Large Language Models
Subscribe for more Machine Learning, Deep Learning, AI Engineering, LLM Engineering, PyTorch, CUDA, Mathematics, and Data Science courses.

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