Every model you will ever train is built on a small set of mathematical ideas. You do not need a maths degree to work in AI and data science, but you do need to understand statistics well enough to judge whether a result is real, and linear algebra and calculus well enough to know what a model is actually doing when it learns.
You'll start with distributions, the shape of your data, then move to probability and Bayes' theorem for reasoning under uncertainty. From there the module covers p-values and hypothesis testing, followed by the two pieces of maths behind machine learning: vectors and matrices for representing data, and derivatives for understanding how models improve through gradient descent.
With these foundations in place, you'll be ready for the next module, Python for Data Science, where you'll put them to work on real data with NumPy and pandas.
What a distribution is, why the bell curve shows up everywhere in real data, and how mean and standard deviation describe its shape.
A visual walk through conditional probability and Bayes' theorem, and how new evidence should update what you believe.
What a p-value actually measures, how it connects to hypothesis testing, and the common ways it gets misread.
The first chapter of Essence of Linear Algebra: how to think about vectors as arrows, lists of numbers, and the building block of data.
A visual explanation of eigenvectors and eigenvalues, the idea behind techniques such as PCA that you'll meet later in the course.
An intuitive tour of derivatives and rates of change, the idea that powers gradient descent and model training.