Learn AI and Machine Learning: train bots, build apps, and predict with data in our exciting course

Course description

During the course, students are introduced to machine learning (ML) and Problem Solving Processes to train an AI Bot to detect patterns. Through assignments, students gain insight into the parallels between human cognition and AI. They analyze apps that use ML to understand how it aids in decision-making by identifying data patterns. Students help a computer classify data using applied learning techniques and explore ML applications for recommendations. They import trained models into App Lab to develop a book recommendation app, incorporating welcome screens and event-driven programming. Students learn about model bias, use Model Cards, and practice importing models with numerical data into App Lab, documenting decisions to enhance user experience. In a final project, students simulate a zombie outbreak to predict low-risk areas using data from a neighboring town, leveraging AI Lab's data visualization tools to understand pattern recognition and prediction. By the end of the course, students will have a practical understanding of machine learning, from building and training models to making predictions and creating applications.

To sign up for the DEMO lesson please contact us by email: [email protected]

After the course students:

• Explain the concept of machine learning and its similarities to human mental models.
• Differentiate between supervised and unsupervised learning.
• Use the Problem-Solving Process to train a robot for pattern detection.
• Apply ML techniques to simulate real-world scenarios
• Analyse strategies used by computer models to make decisions based on data patterns.
• Train a model using AI Lab to recognize shapes and make recommendations
• Create an app using ML and integrate models into App Lab.
• Evaluate trained ML models for bias and effectiveness.
• Predict numerical data patterns using AI Lab tools.
• Utilize data visualization tools to identify high-relationship features in data.