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PRACTICAL AI AND ML COURSE

AI and Machine Learning Foundations

Learn the reasoning, data preparation, model evaluation, and responsible-use practices behind machine learning. Every topic has a runnable Python example for Open Editor.

What you will learn

  • Frame an AI problem as a specific prediction, classification, or grouping task.
  • Inspect, clean, and summarize data before training a model.
  • Understand core model ideas and evaluate predictions fairly.
  • Recognize overfitting, bias, privacy, and the limits of automated decisions.

Practice method: use the examples to make the calculations visible. These lessons explain concepts with plain Python; production ML typically uses specialized libraries and carefully governed data.

1. Data foundations

Start with a question

Useful machine learning begins with a narrow question, a measurable success criterion, and data that relates to the desired outcome.

temperatures = [18, 20, 22, 24]
average = sum(temperatures) / len(temperatures)
print(average)

Features are model inputs

A feature is an input a model uses to make a prediction. Good features are relevant, available at prediction time, and measured consistently.

2. Models and evaluation

A model maps inputs to outputs

Regression predicts a number, classification predicts a category, and clustering groups similar records. The right model depends on the problem and data.

def predict_price(size):
    return 50000 + 120 * size

print(predict_price(80))

Evaluate on unseen data

Keep some data separate from training. A model that only performs well on examples it has already seen is not yet useful.

3. Responsible AI

Models can amplify errors or unfairness present in their data. Evaluate performance across relevant groups, protect personal data, give people meaningful oversight, and do not use a model beyond what its evidence supports.

Human judgment remains essential: a prediction is evidence, not a decision. High-impact uses require domain expertise, review, documentation, and ongoing monitoring.

Before you move on

  • I can distinguish regression, classification, and clustering.
  • I can identify features and a target outcome.
  • I know why a test set is held back.
  • I can explain overfitting.
  • I can choose a metric that matches a problem.
  • I consider fairness, privacy, and human oversight.