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Artificial Intelligence (AI) – Specialist
Deep Learning, Generative AI, Robotics & Advanced AI Development
Target Audience – High School Students (Grades 9–12) and learners who have completed AI Practitioner or have basic knowledge of Python, Machine Learning, data preparation, and AI development.
Program Pathway – Specialist
Total Duration – 96 Hours
Delivery – Cohort-Based + Instructor-Led + Hands-On Labs
Program Goal
The AI Specialist program builds on the AI Practitioner pathway and introduces students to
advanced AI technologies including Deep Learning, Neural Networks, Computer Vision, Natural Language Processing, Generative AI, Large Language Models, Robotics, and Autonomous Systems. Students develop advanced AI applications and prepare for AI Engineering projects.
Hands-On Learning
Students learn through instructor-led lessons, Python exercises, Deep Learning experiments, Computer Vision and NLP activities, Generative AI labs, LLM application development, Robotics simulations, autonomous system activities, mini-projects, assessments, and a final AI Specialist project.
What Students Build
Students develop practical experience with Deep Learning models, neural network applications, Computer Vision solutions, NLP applications, Generative AI applications, LLM-powered applications, AI assistants, basic AI agent workflows, robotics applications, autonomous system simulations, and advanced AI projects.
Recommended Lab Requirements
Each student should ideally have:
● Windows or Mac desktop/laptop
● 8 GB RAM minimum
● 16 GB RAM recommended
● 10 GB+ free storage
Windows/Mac systems are recommended. Chromebooks are not preferred.
Cloud-based environments may be used for intensive Deep Learning activities, so a dedicated
GPU is not required.
Next Step
Students who complete AI Specialist can progress to the AI Engineering Capstone, exploring AI system architecture, advanced AI application development, model deployment, cloud AI, APIs, MLOps, Edge AI, advanced AI agents, robotics, autonomous AI, and end-to-end AI engineering projects.
| Program Structure | ||
| Module | Topic | Hours |
| Module 1 | Deep Learning & Neural Networks | 20 |
| Module 2 | Advanced Computer Vision & NLP | 20 |
| Module 3 | Generative AI & Large Language Models | 18 |
| Module 4 | Robotics & Autonomous Systems | 18 |
| Module 5 | AI Specialist Project | 20 |
| Total | 96Hours | |
Curriculum
- 5 Sections
- 0 Lessons
- 96 Hours
Expand all sectionsCollapse all sections
- Module 1 – Deep Learning & Neural Networks0 Items✅ Topics :- Deep Learning Fundamentals, Neural Networks, Artificial Neurons, Layers, Weights & Biases, Activation Functions, Forward Propagation, Backpropagation, Loss Functions, Model Training, Training & Validation, Overfitting & Underfitting, Model Optimization, Convolutional Neural, Networks, TensorFlow, Keras.
- Module 2 – Advanced Computer Vision & NLP0 Items✅ Topics :- Computer Vision, Image Classification, Image Processing, Object Detection, Feature Extraction, Convolutional Neural Networks, Transfer Learning, Video Intelligence Fundamentals, NLP , Fundamentals, Text Processing, Tokenization, Text Classification, Sentiment Analysis, Text Similarity, Embeddings, Transformers Fundamentals.
- Module 3 – Generative AI & Large Language Models0 Items✅ Topics :- Generative AI Fundamentals, Large Language Models, Tokens, Context Windows, Embeddings, Prompt Engineering, Zero-Shot & Few-Shot Prompting, AI APIs, AI Chatbots, AI Assistants, Document Question Answering, Retrieval-Augmented Generation (RAG) Fundamentals, AI Agents Fundamentals, Hallucinations, Generative AI Limitations, AI Safety.
- Module 4 – Robotics & Autonomous Systems0 Items✅ Topics :- Introduction to Robotics, Physical AI, Robot Components, Sensors, Actuators, Controllers, Robotics Programming Fundamentals, AI-Based Perception, Computer Vision for Robotics, Sensor Data, Sensor Fusion Fundamentals, Robot Decision-Making, Path Planning, Navigation, Obstacle Detection & Avoidance, Autonomous Systems, AI-Based Control, Human-Robot Interaction, Autonomous Vehicles & Drones, Robotics Safety.
- Module 5 – AI Specialist Projec0 Items✅ Topics :- Problem Identification, AI Solution Design, Data Selection & Preparation, Model Selection, Deep Learning Integration, Computer Vision or NLP Integration, Generative AI Integration, LLM Integration, Robotics & Autonomous System Concepts, AI Application Development, API Integration, Testing, Evaluation, Responsible AI Review, Documentation, Final Presentation.Final Project: Build and present an advanced AI-powered solution using one or more Specialist-level AI technologies.
