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Machine Learning: the fundamentals
Duration : 7 hoursLanguage : EnglishLevel : BeginnerSoftware : A browser; Python is optional, only if you want to reproduce the calculations yourself
Prerequisites:High-school-level algebra (solve a first-degree equation, read a line y = a x + b)Ability to read a simple chart (scatter plot, curve)No programming experience required
Free
Lessons marked “open access” work without an account.
Description
A path into machine learning for people who have never touched it. You start from a scatter plot and a line that fits it, learn to measure a model's error and correct it, then move on to classification (confusion matrix, precision, recall, ROC), neural networks, embeddings and large language models. Every concept rests on a small dataset you can compute by hand, not on a formula you have to take on faith. The course ends with production deployment and fairness, two topics introductory courses too often skip.
Curriculum
Lessons marked “open access” work without an account.
Objectives
- Explain what a linear regression model actually computes and how it learns
- Choose and interpret a loss function (L1, L2, MAE, MSE)
- Read a confusion matrix and compute precision, recall and F1 score by hand
- Understand what a ROC curve is for and what AUC measures
- Tell numerical data apart from categorical data, and spot overfitting
- Explain what neural networks and embeddings are used for
- Describe how a language model predicts the next word, from n-grams to LLMs
- List the questions to ask before shipping a model to production, including on bias
Skills you'll gain
Linear and logistic regressionReading a confusion matrixComputing precision, recall, F1 score, ROC and AUCNeural network and embedding conceptsLanguage model basics (tokens, n-grams, context)Production deployment and algorithmic fairness reference points
Content type
Leçons écrites avec exemples chiffrésTableaux de calcul pas à pasExercices corrigésQuiz interactifs
Your instructor
Équipe OmniLearn