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.
Undergraduate students from all streams with an interest in AI and Machine Learning.
- 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
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.
- Continuous assessment through quizzes, assignments, and project work.
- Final project presentation and report and MCQ Test.
No specific prerequisites are required, but a basic understanding of mathematics and programming (e.g., Python) is beneficial.