Machine learning careers are built on a small number of durable foundations that outlast any single framework or model release. This track covers them in order: the maths and data handling you cannot skip, the classical algorithms that still solve most business problems, the neural-network concepts behind modern systems, how large language models work and are used, and how a model becomes a reliable, monitored, responsibly deployed service. Each lesson ends with an action step that adds to a project portfolio.

For developers, analysts and career changers who want a clear, honest map of the field rather than a tour of this year’s tools.

Modules

Foundations

Lessons

• The Map of the Field Preview
• The Mathematics You Actually Need
• Python and the Data Toolkit

Working With Data

Lessons

• Data Quality and Preparation
• Feature Engineering
• Evaluation and Validation

Classical Machine Learning

Lessons

• Linear Models and Why They Endure
• Trees, Forests and Boosting
• Unsupervised Methods and Dimensionality

Deep Learning

Lessons

• Neural Networks from the Ground Up
• Convolutional and Sequence Models
• Training at Scale and Experiment Tracking

Language Models and Generative AI

Lessons

• How Large Language Models Work
• Prompting, Retrieval and Tool Use
• Fine-Tuning and When Not To

Production and Responsibility

Lessons

• From Notebook to Service
• Monitoring Models in the Wild
• Responsible AI and the Career Ahead