AI Foundations & Certification Program

To provide a comprehensive introduction to Artificial Intelligence (AI), equipping participants with foundational knowledge, practical skills, and industry-recognized certification to kickstart their journey in AI.

• To provide participants with a strong foundation in AI concepts and techniques.
• To familiarize participants with tools and frameworks for AI development.
• To enable participants to build and deploy simple AI models.
• To discuss ethical challenges and best practices in AI applications.
• To prepare participants for advanced learning and career opportunities in AI.

1st Week – Introduction & Onboarding

1.1. Course Overview and Expectations
1.2. AI Fundamentals and Responsible AI Introduction
1.3. Environment Setup and Initial Quiz

2nd Week – Python & Data Handling

2.1. Python Best Practices for AI
2.2. Data Collection and Preprocessing
2.3. Exploratory Data Analysis (EDA)

3rd Week – Machine Learning Core

3.1. Basic Algorithms: Linear/Logistic Regression, Decision Trees
3.2. Model Training, Validation, and Metrics
3.3. Advanced ML Techniques: Ensemble Methods and Model Tuning

4th Week – Introduction to Deep Learning

4.1. Neural Networks and Frameworks (TensorFlow, PyTorch, Keras)
4.2. Building a Simple Feedforward Network
4.3. Specialized Architectures: CNNs, RNNs, and LSTMs

5th Week – Practical AI Applications

5.1. Natural Language Processing (NLP) Basics
5.2. Computer Vision and Transfer Learning
5.3. Additional Use Cases (Recommender Systems, Time-Series)

6th Week – Responsible AI & Deployment

6.1. Ethical Considerations: Bias, Privacy, Explainability
6.2. Model Deployment Strategies (APIs, Cloud Platforms, MLOps)
6.3. Practical Deployment Exercise

7th Week – Summative Assessment & Certification

7.1. Final Review and Preparation
7.2. Written/Online Exam and Project Review
7.3. Certification Award and Next Steps in AI

Course Highlights

• Mentor-Led Live Sessions: Weekly interactive lectures, Q&A, and group discussions.
• Self-Paced LMS Modules: Video lectures, readings, quizzes, and coding tutorials.
• Hands-On Projects: Ongoing practical exercises, including Kaggle mini-challenges and real-world case studies.
• Ethical & Responsible AI: Emphasis on bias detection, privacy, and explainability.
• Final Certification: Written/online exam and submission of completed project notebooks for comprehensive skill validation.

This structured approach ensures participants gain foundational knowledge, practical skills, and an understanding of responsible AI, culminating in a recognized certification.