Machine LearningBeginner
Math Essentials for Machine Learning
The linear algebra, probability and calculus you actually need.
Taught by Bibek Sharma · Data Scientist
What you'll learn
- Represent data as vectors and matrices and reason about their shapes
- Compute and interpret dot products as similarity and weighted sums
- Read derivatives and gradients as the slope and direction of steepest change
- Explain how gradient descent uses gradients to train a model
- Apply probability basics, common distributions, and expectation to ML problems
- Connect each math idea to a concrete machine-learning use case
About this course
Machine learning sits on three pillars of math: linear algebra for representing data, calculus for improving models, and probability for reasoning under uncertainty. This course teaches exactly those parts, with minimal notation and maximum intuition, so you can read papers, debug models, and understand why algorithms work. Every idea is tied back to a concrete ML use, so you never learn math in a vacuum.
Course content
9 lessons- 1Why Math Is the Language of Machine LearningHow linear algebra, calculus, and probability each show up inside every model you will ever train.Preview
- 2Vectors: Data as Arrows and ListsWhat a vector is, how to picture it, and why every data point in ML is one.Preview
- 3The Dot Product: Similarity and Weighted SumsThe one operation behind predictions, similarity scores, and neural network neurons.
- 4Matrices: Transforming Whole Datasets at OnceHow a grid of numbers represents many data points or a transformation, and why batching matters.
- 5Derivatives: Slope, Sensitivity, and 'Which Way Is Down'What a derivative really measures and why it is the key to making a model less wrong.
- 6Gradients and Gradient Descent: How Models LearnExtending the derivative to many parameters at once and using it to train real models.
- 7Probability Basics: Reasoning Under UncertaintyProbabilities, conditional probability, and independence, with the ML uses that motivate them.
- 8Distributions and Expectation: The Shape of Data and Its AverageCommon distributions, expected value, and variance, and how they describe and drive ML.
- 9Putting It Together: A Mental Model of Machine LearningAssembling vectors, dot products, gradients, and probability into one clear picture of how models work.