AI and Machine Learning

Course Description:

This 30-hour certificate course in Artificial Intelligence and Machine Learning will provide students from diverse academic backgrounds with a foundational understanding of these cutting-edge technologies. AI and Machine Learning are transforming various industries, and this course aims to equip students with the knowledge and skills necessary to leverage the power of data-driven decision-making, automation, and intelligent systems.

Target Audience:

Undergraduate students from all streams with an interest in AI and Machine Learning.

Course Objectives:

• Understand the fundamental concepts and applications of AI and Machine Learning.
• Learn how to apply AI and Machine Learning techniques to real-world problems.
• Gain hands-on experience with AI tools and frameworks.
• Explore ethical and societal implications of AI and Machine Learning.
• Develop a basic project demonstrating AI and Machine Learning capabilities.

Course Duration: 30 hours

Syllabus:

Module 1: Introduction to AI and ML (2 hours)

• What is Artificial Intelligence?
• What is Machine Learning?
• Types and applications of AI and ML.
• Ethical considerations in AI and ML.

Module 2: Data Preprocessing (2 hours)

• Data collection and cleaning.
• Feature selection and engineering.
• Data scaling and normalization.

Module 3: Supervised Learning (4 hours)

• Linear Regression.
• Logistic Regression.
• Decision Trees and Random Forests.
• Support Vector Machines.
• Model evaluation and selection.

Module 4: Unsupervised Learning (4 hours)

• Clustering techniques (K-Means, Hierarchical).
• Principal Component Analysis (PCA).
• Anomaly detection.

Module 5: Natural Language Processing (6 hours)

• Introduction to Neural Networks.
• Tokenization and Text Preprocessing.
• Text Classification with NLP.
• Speech Processing.

Module 6: Hands-on Projects (8 hours)

• Students work on small AI and ML projects to apply the knowledge gained.

Module 7: Ethics and Future Trends (2 hours)

• Ethical considerations in AI.
• Future trends in AI and Machine Learning.

Module 8: Course Review and Certification (2 hours)

• Review of key concepts and student projects.
• MCQs and evaluation.

Assessment:

• Continuous assessment through quizzes, assignments, and project work.
• Final project presentation and report and MCQ Test.

Prerequisites:

No specific prerequisites are required, but a basic understanding of mathematics and programming (e.g., Python) is beneficial.