Overview
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Module 1: Fundamental Concepts in Artificial Intelligence (AI) and Machine Learning
By the end of this module, you'll be able to:
- Distinguish between supervised, unsupervised, and reinforcement learning, and identify the type of machine learning most suitable for certain scenarios.
- Assess the effectiveness of neural networks in handling unstructured and unlabeled data compared to other machine learning techniques.
- Evaluate statements on the role of continuous refinement in machine learning models.
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Module 2: Dynamic Integration of AI in Education
By the end of this module, you'll be able to:
- Recognize how AI is being applied in an educational context.
- Compare the use of AI-powered software in enhancing learning engagement and determine its impact on the learning process.
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Module 3: AI Subsets and Their Application in Education
This module explores various subsets within AI such as natural language processing (NLP), computer vision, and recommendation systems and discusses the integration of AI-powered tools into a learning environment.
By the end of this module, you'll be able to:
- Understand the main advantages of applying AI-powered tools into a learning environment.
- Analyze the differences between different AI subsets and their applications in education.
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Categories: Artificial Intelligence Courses, Machine Learning Courses, Computer Vision Courses, Deep Learning Courses, Neural Networks Courses
