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Artificial Intelligence (AI) – Practitioner
Machine Learning, Applied AI & AI Development
Target Audience – High School Students (Grades 9–12) and learners who have completed AI Foundations or have basic knowledge of AI, Python, and data.
Program Pathway – Practitioner
Total Duration – 84 Hours
Delivery – Cohort-Based + Instructor-Led + Hands-On Labs
Program Goal
This intermediate-level program builds on AI Foundations and introduces students to Machine Learning, data preparation, AI model development, Computer Vision, Natural Language Processing, and applied AI projects.
Hands-On Learning
Students learn through instructor-led lessons, Python exercises, real-world datasets, practical labs, mini-projects, assessments, and a final AI project.
What Students Build
● Machine Learning models
● Data preparation skills
● Basic Computer Vision solution
● Basic NLP solution
● AI application
● Responsible AI understanding
● Applied AI project
Recommended Lab Requirements
Each student should ideally have:
● Windows or Mac laptop/desktop
● 8 GB RAM recommended
● 10 GB+ free storage
Windows or Mac is recommended. Chromebooks are not preferred for this pathway.
Next Step
After successfully completing AI Practitioner, students can progress to AI Specialist, where they explore Deep Learning, Advanced Computer Vision, Generative AI, Large Language Models, AI Agents, and advanced AI projects.
| Program Structure | ||
| Module | Topic | Hours |
| Module 1 | Machine Learning & Data Preparation | 16 |
| Module 2 | Machine Learning Model Development | 20 |
| Module 3 | Computer Vision & NLP | 16 |
| Module 4 | AI Evaluation, Deployment & Responsible A | 14 |
| Module 5 | AI Practitioner Project | 18 |
| Total | 84Hours | |
Curriculum
- 5 Sections
- 0 Lessons
- 84 Hours
Expand all sectionsCollapse all sections
- Module 1 – Machine Learning & Data Preparation0 Items✅ Topics :- Machine Learning Fundamentals • Supervised & Unsupervised Learning • Classification • Regression • Data Preprocessing • Features & Labels • Training & Testing Data • Pandas • NumPy • Scikit-learn
- Module 2 – Machine Learning Model Development0 Items✅ Topics :- Classification • Regression • Decision Trees • Random Forest • Model Training • Predictions • Accuracy • Precision • Recall • Confusion Matrix • Overfitting • Underfitting • Model Comparison .
- Module 3 – Computer Vision & NLP0 Items✅ Topics :- Computer Vision • Image Classification • Image Processing • NLP Fundamentals • Text Classification • Sentiment Analysis • Chatbots • Generative AI Concepts
- Module 4 – AI Evaluation, Deployment & Responsible AI0 Items✅ Topics :- Model Evaluation • Testing • Reliability • Model Limitations • Deployment Concepts • AI Applications • Data Privacy • Bias & Fairness • AI Safety • Responsible AI • Generative AI Risks.
- Module 5 – AI Practitioner Project0 Items✅ Topics :- Problem Identification • Data Selection • Data Preparation • Model Development • Testing • Evaluation • Responsible AI Review • Documentation • Final Presentation Final Project: Build and present an applied AI solution.
