machine-learning

Machine Learning

Course Overview

Artificial Intelligence (AI) and Machine Learning are rapidly growing fields, and understanding their fundamentals is becoming a valuable skill in today’s job market. As part of modern AI education, this course introduces supervised learning, a core concept in machine learning and AI, guiding students to build a classification model using Python while learning how to use AI to analyze data and make predictions. Through a combination of theory and hands-on practice in our AI classes, students will explore key Artificial Intelligence and Machine Learning techniques, including the mathematical models that power modern AI applications.

Course Objectives

  • Learn the functions and make use of different block types (motion, looks, sounds, pen, data, sensing, events, control, operators, etc.) in their creation
  • Learn strategies for solving problems, designing projects, and communicating ideas
  • Learn to make their very own animations and games
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Python

Students further explore and build a classification model using the Python programming language, learning how to preprocess data, split data into training and testing sets, and evaluate model performance, which are key skills in modern AI education.

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Introduction to Machine Learning

Learn the fundamentals of machine learning and how computers identify patterns from data. In our AI classes, students will explore real-world applications of Artificial Intelligence and discover how these technologies are used in everyday systems and modern digital tools.

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Real-World Applications

Explore various real-world applications of Artificial Intelligence and machine learning, including image recognition and natural language processing, and discover how these technologies power many modern digital systems.

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Supervised Learning

Students will learn the fundamentals of supervised learning, a key concept in Artificial Intelligence (AI) and Machine Learning, where machines are trained using labeled data to make predictions. They will explore how models learn from input–output examples while gaining an understanding of how to use AI in real-world technologies and data-driven applications.

Classification Models

Students will be introduced to regression and classification models and how they are used to analyze data. Through these concepts, students will also gain an understanding of how AI systems use data to identify patterns and make predictions, such as estimating values or categorizing information.

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Problem Solving Techniques

Students will learn techniques to troubleshoot and improve machine learning models used in Artificial Intelligence systems through our AI classes, developing practical skills that are essential in modern AI education. This includes identifying issues in datasets, adjusting model settings, and evaluating results to enhance model performance and prediction accuracy.

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Course Completion

2 Modules (avg. 32 Lessons)

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