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    Moodle is an open-source Learning Management System (LMS) that provides educators with the tools and features to create and manage online courses. It allows educators to organize course materials, create quizzes and assignments, host discussion forums, and track student progress. Moodle is highly flexible and can be customized to meet the specific needs of different institutions and learning environments.

    Moodle supports both synchronous and asynchronous learning environments, enabling educators to host live webinars, video conferences, and chat sessions, as well as providing a variety of tools that support self-paced learning, including videos, interactive quizzes, and discussion forums. The platform also integrates with other tools and systems, such as Google Apps and plagiarism detection software, to provide a seamless learning experience.

    Moodle is widely used in educational institutions, including universities, K-12 schools, and corporate training programs. It is well-suited to online and blended learning environments and distance education programs. Additionally, Moodle's accessibility features make it a popular choice for learners with disabilities, ensuring that courses are inclusive and accessible to all learners.

    The Moodle community is an active group of users, developers, and educators who contribute to the platform's development and improvement. The community provides support, resources, and documentation for users, as well as a forum for sharing ideas and best practices. Moodle releases regular updates and improvements, ensuring that the platform remains up-to-date with the latest technologies and best practices.

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Available courses

Artificial Intelligence and Machine Learning.

Course Title

Artificial Intelligence (AI) and Machine Learning (ML)

Course Summary

This course provides trainees with a foundational understanding of Artificial Intelligence (AI) and Machine Learning (ML) and how these technologies are transforming modern society. It introduces key concepts such as intelligent systems, data-driven decision-making, and automated learning. Learners explore how machines learn from data, recognize patterns, and make predictions. Through interactive activities and real-world examples, the course builds practical awareness of AI applications, ethical considerations, and future career opportunities in AI-related fields.

Learning Outcomes

By the end of the course, Trainees will be able to:

  1. Define Artificial Intelligence and Machine Learning and explain their key concepts.

  2. Distinguish between AI and Machine Learning and describe how they work together.

  3. Identify different types of machine learning: supervised, unsupervised, and reinforcement learning.

  4. Explain basic machine learning algorithms and their uses.

  5. Interpret simple data sets used in machine learning processes.

  6. Apply AI and ML concepts to solve basic real-world problems.

  7. Evaluate ethical, social, and security issues related to AI technologies.

  8. Demonstrate beginner-level use of AI or ML tools in practical scenarios.

Sample Interactive Learning Activities

  1. AI in Daily Life Brainstorming
    Trainees identify and discuss examples of AI they interact with daily (e.g., voice assistants, recommendations, mobile apps).

  2. Machine Learning Sorting Activity
    Trainees classify examples into supervised, unsupervised, or reinforcement learning using cards or digital tools.

  3. Data Pattern Discovery Exercise
    Trainees work in small groups to analyze a simple dataset and identify patterns or trends.

  4. Algorithm Role-Play
    Trainees simulate how a machine learning algorithm works by acting as “data,” “model,” and “decision-maker.”

  5. Case Study Discussion
    Groups analyze a real-world AI application (e.g., AI in healthcare or agriculture) and present benefits and risks.

  6. Ethics Debate
    Trainees debate ethical issues such as data privacy, bias, and job automation caused by AI.

  7. Mini Project
    Trainees use a beginner-friendly AI tool to create a simple prediction or classification model.