This page provides a collection of resources related to machine learning math concepts. Whether you are a beginner or an experienced ML practitioner, these resources should help you deepen your understanding of the mathematical foundations of machine learning.
Key Concepts
- Linear Algebra: Understanding vectors, matrices, and transformations is crucial for many machine learning algorithms.
- Probability and Statistics: Probability theory and statistics form the basis for many ML models and decision-making processes.
- Optimization: Optimization techniques are used to find the best parameters for machine learning models.
Learning Resources
- Introduction to Linear Algebra - A comprehensive introduction to linear algebra.
- Probability and Statistics for Machine Learning - A course covering the fundamentals of probability and statistics for ML.
Further Reading
- Machine Learning Yearning - A book by Andrew Ng that covers practical aspects of machine learning.
- Deep Learning Specialization - A series of courses on deep learning by Andrew Ng.
Linear Algebra
Probability_Statistics
Optimization
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