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⭐ New — Tech skills

Artificial Intelligence Fundamentals

From zero to your first AI model

🎓 Beginner — no prior AI experience⏱ 18 hours of content📄 PDF workbook🎥 Video lessons📱 Phone / laptop

A beginner-friendly, project-based introduction to Artificial Intelligence and Machine Learning. You will understand what AI really is (and is not), how neural networks learn, and build your own working models in Python using scikit-learn and PyTorch. No maths degree required — just curiosity and a laptop.

What's inside this pack

✓180-page PDF workbook
✓12 hours of video lessons
✓6 hands-on Python projects
✓Jupyter notebooks (ready to run)
✓Prompt-engineering cheat sheet
✓Certificate of completion

Sample lessons — read before you buy

5 previews

1Lesson 1 — What is AI, really?

Artificial Intelligence is not magic — it is pattern recognition at scale. A modern AI system learns a function f(x) that maps inputs (an image, a sentence, a spreadsheet row) to outputs (a label, a next word, a prediction). Three main families: (1) Supervised learning — learns from labelled examples, (2) Unsupervised learning — finds structure in unlabelled data, (3) Reinforcement learning — learns from reward signals. ChatGPT is a supervised + RLHF model trained to predict the next token in a sequence.

2Lesson 2 — How a Neural Network Learns

A neuron computes y = σ(w·x + b) where w are weights, b is a bias, and σ is a non-linear activation (ReLU, sigmoid, tanh). Training uses gradient descent: (1) forward pass — compute predictions, (2) loss — measure how wrong we are (e.g. MSE, cross-entropy), (3) backward pass — compute ∂loss/∂w with the chain rule, (4) update — w ← w − η·∂loss/∂w. Do this millions of times over your dataset. That's it. That is the entire training loop.

3Lesson 3 — Your First Model in Python (scikit-learn)

from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model = LogisticRegression().fit(X_train, y_train) print('accuracy:', model.score(X_test, y_test)) In 5 lines you have trained a classifier. The project in this lesson uses the famous Iris dataset to predict flower species from petal measurements — accuracy is typically above 95%.

4Lesson 4 — Prompt Engineering for LLMs

Large Language Models are conditioned by their prompt. Four techniques that measurably improve output quality: (1) Role — 'You are a senior tax consultant in South Africa.', (2) Format — 'Answer as a numbered list of at most 5 bullets.', (3) Few-shot — give 2–3 input/output examples before the real query, (4) Chain-of-thought — add 'Think step by step before answering.' Combining all four typically doubles the accuracy on reasoning tasks.

5Lesson 5 — Ethics, Bias & Hallucinations

AI models inherit the bias of their training data. A résumé screener trained on 10 years of hires from a male-dominated industry will down-rank women. Mitigations: audit the dataset, add fairness constraints, keep a human in the loop for high-stakes decisions. Hallucinations happen because LLMs are trained to be plausible, not truthful — always verify factual claims against a trusted source before acting on them.

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