GPMS TRANSPORTAL APPLIED AI COURSES

• A structured introduction to AI fundamentals, practical applications, and essential AI development skills.
• Level: Intermediate to Advanced
• Prerequisites: Grade 12 AI or equivalent foundational knowledge
• Assessments: Weekly practical assignments, Mid-term project, Final project

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Module : Fundamentals of AI & Machine Intelligence (Weeks 1-2)

Learning Outcome

Establish a strong foundation in AI principles.

1.1 Understanding AI models and their real-world applications
1.2 Introduction to deep learning, neural networks, and cognitive AI
1.3 AI ethics, bias, and responsible AI development

Practical Component
Implementing basic AI models using Python and Dataviv AI Lab tools

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Module 2 : AI for Data Science & Big Data Analytics (Weeks 3-4)

Learning Outcome

Applying AI techniques to analyze large-scale data.

2.1 AI in data cleaning, processing, and feature engineering
2.2 AI for predictive analytics and decision-making
2.3 AI-powered big data analysis and visualization

Practical Component
Building AI-powered analytics dashboards using Dataviv AI Lab

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Module 3 : Computer Vision and AI-powered Image Processing (Weeks 5-6)

Learning Outcome

Understanding AI-driven vision technologies.

3.1 AI for image recognition, classification, and segmentation
3.2 AI-powered facial recognition and object detection
3.3 AI in healthcare imaging and industrial quality control

Practical Component
Developing AI-driven image processing models using Dataviv AI Lab

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Module 4 : AI for Natural Language Processing (NLP) (Weeks 7-8)

Learning Outcome

Understanding how AI processes human language.

4.1 AI in text classification, sentiment analysis, and summarization
4.2 AI-powered conversational agents and virtual assistants
4.3 AI in speech recognition and deep voice synthesis

Practical Component
Building a simple chatbot or AI voice generator using NLP models

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Module 5 : AI for Robotics and Intelligent Systems (Weeks 9-10)

Learning Outcome:

Exploring AI-powered automation and robotics.

5.1 AI-powered robotic control and autonomous movement
5.2 AI in reinforcement learning and adaptive robotics
5.3 AI for industrial automation and smart manufacturing

Practical Component
Simulating an AI-controlled robotic system at Dataviv AI Lab

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Module 6 : AI Capstone Project (Weeks 11-12)

Learning Outcome

Applying AI knowledge to a real-world challenge.

6.1 Identifying an AI-driven problem statement
6.2 Developing and testing an AI model for a practical use case
6.3 Presenting and documenting the project results

Practical Component
Hands-on AI project development and demonstration

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