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Maths for Machine Learning
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This CampusX playlist is a broad maths-for-ML sequence. It begins with descriptive statistics, probability distributions, the central limit theorem, confidence intervals and hypothesis testing, then covers chi-square tests and ANOVA. The second half turns to linear algebra, including vectors, matrices, eigenvectors, eigen decomposition and singular value decomposition, before ending with optimisation and differential calculus.
Students preparing for data science or machine learning, who need the maths in one connected run, will find it practical. Many session titles are labelled as part of a numbered series, so watching in the listed order keeps the topics building on each other.
A complete end to end playlist to learn all the mathematics required for machine learning and deep learning.
Videos in this playlist include:
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