AI with Python for Teens (Ages 13–19) – Level 1

Course Content

1. Introduction to Artificial Intelligence
   • What Artificial Intelligence is
   • Examples of AI in everyday life
   • Artificial Intelligence, Machine Learning and Deep Learning
   • How traditional programming differs from Machine Learning
   • How machines learn patterns from data
   • Introduction to supervised and unsupervised learning
2. Understanding Data for Machine Learning
   • Why data is important for AI
   • Understanding datasets, rows and columns
   • Features and target values
   • Numerical and categorical data
   • Loading datasets with Pandas
   • Exploring data using Python
   • Creating simple charts with Matplotlib
3. Building Your First Machine Learning Model
   • Introduction to the Machine Learning workflow
   • Selecting features and a target
   • Introduction to scikit-learn
   • Creating a Machine Learning model
   • Training a model using fit()
   • Making predictions using predict()
   • Comparing predictions with real values
4. Regression – Predicting Numbers
   • Understanding regression
   • Examples of regression problems
   • Understanding the relationship between variables
   • Building a Linear Regression model
   • Training the model using data
   • Making numerical predictions
   • Visualising predictions
   • Understanding prediction errors
5. Classification – Predicting Categories
   • Understanding classification
   • Regression vs classification
   • Examples of classification problems
   • Preparing features and class labels
   • Building a simple classification model
   • Training the classifier
   • Making category predictions
   • Comparing predicted and actual classes
6. Training and Testing Machine Learning Models
   • Why a model should not be tested using the same data it learned from
   • Understanding training data and testing data
   • Creating a train-test split with scikit-learn
   • Training a model using the training dataset
   • Testing the model with unseen data
   • Introduction to model accuracy
   • Understanding correct and incorrect predictions
7. Machine Learning Project
   • Explore a real-world dataset
   • Identify suitable features and a target
   • Prepare the data for Machine Learning
   • Split the data into training and testing sets
   • Build and train a Machine Learning model
   • Use the model to make predictions
   • Evaluate the results
   • Visualise and explain the findings
   • Present the completed AI project
8. What Comes Next in AI?
   • How Machine Learning progresses to Deep Learning
   • Introduction to neural networks
   • Examples of computer vision and modern AI applications

Course Dates, Prices & Enrolment