AI Foundations & Certification Program
Building a Solid Foundation in Artificial Intelligence for the Future.
Skills you will gain:
The AI Foundations & Certification Program is designed for individuals looking to understand and apply AI concepts across industries. The program covers the essentials of AI, machine learning, data analysis, and neural networks, emphasizing real-world applications. Participants will gain hands-on experience with tools and frameworks and earn certification, validating their skills for academic and professional growth.
Aim: 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.
Program Objectives:
- 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.
What you will learn?
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.
Intended For :
- Students, professionals, and AI enthusiasts from any discipline
- Beginners with no prior coding or AI experience
- Entrepreneurs exploring AI-driven innovation
- Anyone looking to transition into AI-related fields
Career Supporting Skills