AI & Machine Learning
Understand and apply the foundations of machine learning — data, models, evaluation, modern AI concepts and responsible development — through hands-on Python projects.
- Planned 30 days
- About 2 h per day
- 7 planned modules
This course is in development and not yet available for purchase. The planned curriculum below is a draft and may change before launch.
Overview
A grounded introduction to machine learning. You learn how AI, machine learning and deep learning differ, the statistics and linear-algebra ideas you need, data quality and preprocessing, supervised and unsupervised learning, regression and classification, validation, overfitting and leakage, metrics, feature engineering, neural-network fundamentals, embeddings, large language models, APIs, retrieval-augmented generation, limitations, privacy, bias and monitoring. The capstone is a small, clearly scoped ML project with an honest evaluation of its limits.
What you will be able to do
- Explain core machine-learning concepts and when ML is appropriate
- Prepare data and train regression and classification models
- Evaluate models with appropriate metrics and avoid leakage and overfitting
- Explain neural networks, embeddings, LLMs and retrieval-augmented generation
- Integrate a model through an API responsibly
- Document a model's limitations, risks and intended use
Before you start
- Basic Python (variables, functions, loops)
- Secondary-school mathematics
Planned curriculum
A draft outline of the modules being written. Lessons are not available yet, and the outline may change after expert review.
Availability
This course is not on sale yet. It becomes available only after its lessons, assessments and resources pass Yumeora's review. When published, it is planned to be included in Yumeora All-Access.