2 YR ORDINARY DIPLOMA IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Course Duration: 2 Years (4 semesters, including holidays and attachment)
Course Fee: 30,000 Kenyan Shillings per semester (each semester takes 3 months)

weekly commitment: minimum of 2 hours per week

Course Overview:

This course focuses on artificial intelligence (AI) and machine learning (ML), covering foundational principles, algorithms, and applications in various domains.

Year 1

Semester 1: Introduction to Artificial Intelligence

• Week 1-2: Introduction to Artificial Intelligence

• • Overview of AI Concepts and History
  • Applications of AI in Industryand Society
  • AI Ethics and Implications
  • Setting Up AI Development Environment

• Week 3-6: Python Programming for AI

• • Python Basics for AI Programming
  • Libraries for AI Development (NumPy, Pandas)
  • Introduction to Jupyter Notebooks
  • Practical Exercises

• Week 7-10: Machine Learning Fundamentals

• • Supervised and Unsupervised Learning
  • Regression and Classification Algorithms
  • Model Evaluation and Validation
  • Mini Project

• Week 11-14: Deep Learning Basics

• • Neural Networks and Deep Learning Concepts
  • Introduction to TensorFlow and Keras
  • Building Neural Networks
  • Practical Exercises

• Week 15-16: Semester Review and Assessment

• • Review of Key Concepts
  • Practice Problems
  • Mid-Semester Exam

Semester 2: Advanced Machine Learning Techniques

• Week 1-4: Advanced Machine Learning Algorithms

• • Ensemble Methods (Random Forest, Gradient Boosting)
  • Support Vector Machines (SVM)
  • Dimensionality Reduction Techniques
  • Practical Exercises

• Week 5-8: Natural Language Processing (NLP)

• • Text Preprocessing Techniques
  • Text Classification and Sentiment Analysis
  • Named Entity Recognition (NER)
  • Mini Project

• Week 9-12: Computer Vision

• • Image Processing Basics
  • Convolutional Neural Networks (CNNs)
  • Object Detection and Image Classification
  • Practical Exercises

• Week 13-14: Reinforcement Learning

• • Introduction to Reinforcement Learning
  • Q-Learning and Policy Gradient Methods
  • Applications of Reinforcement Learning
  • Practical Exercises

• Week 15-16: Semester Review and Assessment

• • Review of Key Concepts
  • Practice Problems
  • End-of-Semester Exam

Year 2

Semester 3: Advanced Topics in AI and ML

• Week 1-4: Generative Adversarial Networks (GANs)

• • Introduction to GANs
  • Training GANs and Applications
  • GANs for Image and Text Generation
  • Practical Exercises

• Week 5-8: AI Ethics and Responsible AI

• • Ethical Considerations in AI Development
  • Bias and Fairness in AI Systems
  • Regulations and Guidelines
  • Mini Project

• Week 9-12: AI Applications in Industry

• • AI in Healthcare, Finance, and Transportation
  • Case Studies and Use Cases
  • Deploying AI Models in Production
  • Practical Exercises

• Week 13-14: Big Data and AI

• • Integration of Big Data with AI Systems
  • Scalable AI Solutions
  • Handling Large-Scale Data
  • Practical Exercises

• Week 15-16: Semester Review and Assessment

• • Review of Key Concepts
  • Practice Problems
  • Mid-Semester Exam

Semester 4: Capstone Project and Industry Applications

• Week 1-4: Capstone Project

• • Project Planning and Design
  • Implementation of AI and ML Solutions
  • Testing and Evaluation
  • Final Presentation and Evaluation

• Week 5-8: Industry Attachment

• • Attachment in AI and ML Roles
  • Applying Skills in Real-World AI Projects
  • Industry Best Practices
  • Practical Experience

• Week 9-12: Professional Development

• • Career Pathways in AI and ML
  • Building a Professional Portfolio
  • Networking and Career Development
  • Practical Exercises

• Week 13-16: Course Review and Final Exam

• • Comprehensive Course Review
  • Practice Problems
  • Final Exam

Attachment

Students will undertake an industry attachment to gain practical experience in AI and ML applications. This attachment period allows students to apply their knowledge in real-world AI projects, preparing them for professional roles in artificial intelligence and machine learning.

Certification

Upon successful completion of the course, students will receive an "Ordinary Diploma in Artificial Intelligence and Machine Learning" certificate, recognizing their expertise and skills in AI and ML technologies.