Learning path · Beginner

AI Foundations

Understand models, evaluate them and use them responsibly.

Start with how language models work, then learn the data and evaluation skills behind machine learning. Finish with a responsible model card and a small prediction project.

6 weeks 4 courses 46 lessons 4 certificates

Start this path Browse tutorials

The sequence

4 courses, in the right order.

Each step assumes only what came before it. Follow the sequence or jump to a course you need.

Step 1 · Beginner

AI Literacy for Developers

Open course

Understand what language models do, how to prompt them, how to verify output and how to protect private data. A practical course for developers using AI as an assistant, not an oracle.

A reliable AI workflow

0/10 lessons0%

AI Literacy

Step 2 · Beginner

Python Foundations

Open course

Learn programming fundamentals with Python — variables, control flow, functions, collections, classes, files, tests and packaging — then ship a small project you can extend.

First stepsPython from zeroStructure your codeShape the dataBuild real programsQuality and deliveryShip it

0/16 lessons0%

Python Foundations

Step 3 · Beginner

Pandas & Data Analysis

Open course

Pandas is the working language of data analysis in Python. Load, clean, filter, group, merge and visualise real tables, then finish with a reproducible analysis project.

Pandas from first Series to analysis project

0/10 lessons0%

Pandas & Data Analysis

Step 4 · Intermediate

Machine Learning Basics

Open course

A practical introduction to supervised and unsupervised learning with scikit-learn. Split data correctly, train regression and classification models, evaluate them and avoid the classic traps.

Machine learning from data to a responsible model

0/10 lessons0%

Machine Learning Basics

Portfolio project

Responsible prediction project

Build a small prediction model and document its limits.

about 7 hours Portfolio piece

The brief

Choose a tabular dataset, define the prediction task, create a baseline, train a model, evaluate it by subgroup and write a model card with intended and out-of-scope uses.

DeliverableA notebook, a model card, the evaluation table and a short demo.

Stretch goals

Add a second model and compare them.
Add drift monitoring for a simulated month of data.

Acceptance criteria

0/6

A baseline is reported next to the model.
The train and test split happens before preprocessing.
At least two metrics are reported with a confusion matrix.
Performance is broken down by a meaningful subgroup.
The model card states intended and out-of-scope uses.
The demo includes a human override or appeal path.

Path complete

Finish with proof, not just a folder of files.

Complete every course in this path to collect 4 verifiable certificates. Each one has a unique public ID and a QR code that resolves to a live status record.

How certification works Verify a credential

AI Foundations46 lessons · 10 hours

AI Literacy for DevelopersPython FoundationsPandas & Data AnalysisMachine Learning Basics

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