machine-learning

Machine Learning

Course Overview

Machine Learning is a rapidly growing field, and understanding the basics can be a valuable skill in today’s job market. This course provides an introduction to supervised learning, a fundamental area of machine learning, by building a classification model using Python. Through a combination of theory and hands-on practice, students will learn about various mathematical models and techniques used in machine learning.

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

Introduction to Machine Learning

Learn the fundamentals of machine learning and how computers learn patterns from data. Students will also explore a few real-world applications of machine learning and how it is used in everyday technology.

Real-World Applications

Explore various real-world applications of machine learning, including image recognition and natural language processing.

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Supervised Learning

Students will learn the fundamentals of supervised learning, where machines are trained using labeled data to make predictions. They will explore how models learn from input–output examples and how this method is applied in real-world technologies.

Classification Models

Students will be introduced to regression and classification models and how they are used to analyze data. They will learn how these models 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. This includes identifying issues in datasets, adjusting model settings, and evaluating results to enhance model performance.

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Course Completion

2 Modules (avg. 32 Lessons)

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