Mathematics

These are my personal study notes taken while working through the Mathematics for Machine Learning and Data Science Specialization

These are my personal study notes taken while working through the Mathematics for Machine Learning and Data Science Specialization offered by DeepLearning.AI on Coursera. All theoretical content, definitions, and examples are derived from that course. My own contributions are limited to additional annotations, connections to telecommunications and network engineering, and Python implementations.

Source: Coursera. Mathematics for Machine Learning and Data Science Specialization. DeepLearning.AI. Retrieved from https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science

Areas of Study

Linear Algebra

Linear algebra is about manipulating vectors and matrices to do powerful calculations.

Reference: https://www.coursera.org/learn/machine-learning-linear-algebra

Calculus

Differential and integral calculus, optimization methods.

Reference: https://www.coursera.org/learn/machine-learning-calculus

Probability

Probability theory, random variables, distributions, and their properties.

Reference: https://www.coursera.org/learn/machine-learning-probability-and-statistics

Statistics

Statistical inference, hypothesis testing, and regression.

Reference: https://www.coursera.org/learn/machine-learning-probability-and-statistics

Notes for graduate research and professional development.

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