+1 thing you know about AI

AI Upskilling Program

Get Seriously Good
With AI.

Learn how AI systems work & how to build what wasn’t possible before with 6,000+ global learners.

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The community

Six thousand learners, fifty-six countries

Drag the globe, or pick a city and it will spin to it. Every marker is a place with people currently working through the program.

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and 47 more countries6,661

AI Glossary

All you need to know in AI

Every field nests inside the one before it. Click any of the 89 terms to open its explainer, each has its own page with a plain-language definition.

The syllabus

Seven modules, taken in order

Twenty-six weeks full-time, thirty-six part-time. The first two modules are open to read in full — the rest unlock when you register.

Module I

AI Engineering Foundations

Weeks 1–5 · Open

Production PythonWrite clean, reusable Python for AI applications. Master functions, data structures, OOP, modules, environments, debugging and the coding patterns used in real projects.

Data Engineering with PythonTurn raw data into something models can use. Work with NumPy and Pandas to clean, transform, join, analyse and visualise real-world datasets.

SQL & Data SystemsQuery and structure the data behind AI products. Learn joins, aggregations, window functions, relational modelling, indexes and efficient analytical queries.

APIs & Developer WorkflowLearn how AI applications connect to real software. Work with REST APIs, JSON, Git, virtual environments and the developer workflow used to build and ship projects.

Analytics for AI TeamsTurn model and business data into decisions. Build clear reports and dashboards while learning the analytics layer used to communicate AI performance and outcomes.

Module II

Applied Machine Learning

Weeks 6–11 · Open

ML Pipelines & Feature EngineeringPrepare real-world data for modelling. Handle missing values, encoding, scaling, feature creation, leakage, train/test splits and cross-validation correctly.

Classification SystemsBuild models that make decisions from labelled data using logistic regression, decision trees and gradient boosting — then evaluate what actually works.

Regression & ForecastingPredict continuous outcomes using linear models and regularisation. Learn residual analysis, error metrics and how to turn predictions into useful decisions.

Similarity Search & RecommendersLearn how machines measure similarity using distance and nearest-neighbour methods — the foundation behind recommendations, semantic search and modern retrieval systems.

Ensemble LearningCombine multiple models to build stronger predictors with Random Forests, bagging and boosting. Understand feature importance, overfitting and model trade-offs.

Model Evaluation & ExplainabilityGo beyond accuracy. Evaluate models with precision, recall, F1, ROC-AUC and business metrics, then understand why a model made a prediction.

Support Vector MachinesUnderstand margins, kernels and high-dimensional decision boundaries — and when classical ML can still outperform a more complex neural approach.

Module III

Specialisations

Weeks 16–22

Big DataDistributed processing with Spark, partitioning, and designing pipelines that survive a dataset larger than memory.

Time SeriesTrend, seasonality, stationarity, ARIMA and modern forecasting — plus backtesting that does not lie to you.

Natural Language ProcessingTokenisation, embeddings, transformers, retrieval-augmented generation and evaluating a system whose output is text.

TensorFlow & KerasBuilding, training and serving deep models — plus the deployment step most courses skip entirely.

Deep LearningCNNs, RNNs, attention, fine-tuning and agentic workflows that chain models, tools and memory together.

Modules III–VII

Register to unlock the rest

Creating an account opens the full syllabus, the project briefs and the reading list. It takes a minute and costs nothing.

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Real World Projects

Six months from your first dataset
to an AI system you can defend.

Don’t just learn AI, ship systems that work. Every stage of the program ends with something you can demonstrate, explain and add to your portfolio.

github.com/aifolks/retail-intelligence-dashboardRubric score 86

Margin by region, refreshed nightly

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Stories

The people who finished

Six thousand learners, and these are the outcomes we hear about most: a first ML role, a promotion into a data team, or a career changed after a decade in something else.

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Writing

From the blog

All posts →

11 Aug 20265 min read

Decision Trees in ML

Learn what decision trees are, how they work, how impurity and information gain are used, and why they are important in machine learning.

GlossaryRead →

1 Aug 20265 min read

Types Of AI Agents

Learn the five main types of AI agents: simple reflex, model-based reflex, goal-based, utility-based, and learning agents.

GuideRead →

30 Jul 20262 min read

What is a correlation matrix?

In simple words, it helps you see which variables move together, which move in opposite directions, and which have almost no relationship.

GlossaryRead →

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