Marsof Academy
SyllabusPricingSign in
Course 2

Machine learning

Just enough maths and the classic algorithms, from scratch

  • ≈ 132 h
  • Level: intermediate
  • 4 units
  • 36 lessons

A bridge to the school maths you will need (functions, powers, logarithms, sums and notation), for anyone whose maths is rusty; linear algebra, probability, calculus and optimisation implemented in code; regression, classification, trees and clustering written by hand and then with scikit-learn, the library the industry uses; and how to evaluate a model honestly, detect bias and comply with regulation.

Create my accountSee pricing

What you'll be able to do

  • A classifier trained on real data and evaluated without cheating
  • Regression, k-NN, trees and k-means implemented from scratch, and their professional version with scikit-learn pipelines
  • A recommender and a forecasting model evaluated with time-based splits, with no leakage from the future
  • A fairness audit and a model card for a real model

Prerequisites

It helps to have done first (or to master what it teaches):

Units of the course

Unit 1 · Maths bridge

The school maths you are going to need, revisited from scratch and with Python. Functions and slopes, powers and roots, exponentials and logarithms, sums and notation, percentages and scientific notation, and how to read a formula without fear. If you already know it, prove it in the placement test and go straight to the next unit.

7 lessons · ≈ 11 h of study · optional

  1. Functions, graphs and slope
  2. Powers and roots
  3. Exponentials and logarithms
  4. Log-loss and softmax
  5. Summations, subscripts and vectors as lists
  6. Percentages, proportions and scientific notation
  7. Reading formulas without fear

See the unit: Maths bridge

Unit 2 · Maths for AI

Linear algebra, probability, statistics, calculus and optimisation, implemented in code.

12 lessons · ≈ 29 h of study

  1. Vectors and the dot product
  2. Matrices and matrix multiplication
  3. Derivatives: slope, limit and the power rule
  4. Differentiation rules
  5. Gradient descent
  6. Probability and conditional probability
  7. Bayes' theorem
  8. Statistics and distributions
  9. Partial derivatives and the gradient
  10. The chain rule
  11. SGD, momentum and Adam
  12. Linear transformations and eigenvalues

See the unit: Maths for AI

Unit 3 · Machine Learning

Classic algorithms implemented from scratch and then with scikit-learn (pipelines, cross-validation and hyperparameter search).

13 lessons · ≈ 38 h of study

  1. Linear regression with several variables
  2. Logistic regression and classification
  3. Model evaluation
  4. Overfitting, underfitting and regularisation
  5. k-NN and decision trees
  6. Unsupervised learning
  7. Gradient boosting and XGBoost
  8. Support vector machines (SVM)
  9. Anomaly detection
  10. Recommender systems
  11. Time series and forecasting
  12. The professional version: scikit-learn
  13. Choosing a model without cheating: cross-validation and GridSearchCV

See the unit: Machine Learning

Unit 4 · AI ethics, bias and regulation

Building systems that work for everyone and comply with the law. Bias and fairness metrics, privacy and the GDPR, the EU AI Act and responsible deployment practices (model cards, human oversight, red-teaming and incidents).

4 lessons · ≈ 10 h of study

  1. Bias and fairness in ML models
  2. Privacy and data protection in AI
  3. The EU Artificial Intelligence Act (AI Act)
  4. Responsible deployment — document, oversee, attack and respond

See the unit: AI ethics, bias and regulation

Course projects

  • An ML model with real data: Palmer penguins ≈ 38 h
    Real, messy field data (CC0): cleaning, a leak-free split, a baseline, logistic regression with NumPy, cross-validation, calibration, a model card and the professional version with scikit-learn.

The full explanations, auto-graded exercises, exams and projects are inside the academy. Estimated hours for someone starting from scratch: lessons with practice and review, challenges, exams and the recommended projects. It's the same estimate you'll see inside the academy.

Along the way you earn yang, the academy's currency: 10 for every lesson, 50 for every unit exam you pass and 150 for every verified project, plus 15 for today's challenge and 100 for the weekly one. You spend it in the shop on mascots and colours; it can't be bought with money.

Related blog articles

Shall we start?

Create your account and activate your subscription: you get the whole syllabus from day one.

Create my accountSee pricing