Programme Parameters
Foundation
8 Weeks (80 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Unveiling the Foundations of Artificial Intelligence — this comprehensive 8-week program provides a panoramic overview of Artificial Intelligence, exploring its key principles, applications, and methodologies. Participants will gain deep insights into core AI concepts including machine learning, neural networks, natural language processing, and computer vision. Across 8 Weeks, you will build practical fluency in machine learning, neural networks, and natural language processing, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Unveiling the Foundations of Artificial Intelligence — this comprehensive 8-week program provides a panoramic overview of Artificial Intelligence, exploring its key principles, applications, and methodologies. Participants will gain deep insights into core AI concepts including machine learning, neural networks, natural language processing, and computer vision.
📋 Course Objectives
1. Get comfortable working with machine learning.
2. Build practical fluency in neural networks.
3. Gain working command of natural language processing.
4. Put AI Enablement techniques to work on real datasets and case studies.
5. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
• Data and computational scientists moving into machine learning
🚀 Key Learning Outcomes
• Confidence to implement machine learning in real projects.
• Confidence to reason about neural networks in real projects.
• Confidence to apply natural language processing in real projects.
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Introduction to Artificial Intelligence
Explore the definition, history, and evolution of AI alongside key concepts of intelligence and automation • Analyze the philosophical and ethical implications of AI development across different eras • Navigate the AI ecosystem including popular programming languages (Python, R) and frameworks (TensorFlow, PyTorch)
Machine Learning
Master supervised learning techniques including regression, classification, and evaluation metrics • Apply unsupervised learning algorithms such as K-means clustering and PCA dimensionality reduction • Implement reinforcement learning concepts including reward systems and Deep Q-Learning
Deep Learning
Build neural networks from scratch understanding activation functions, loss functions, and backpropagation • Architect advanced deep learning models including CNNs, RNNs, and Generative Adversarial Networks • Optimize model performance through hyperparameter tuning and regularization techniques
Natural Language Processing
Process and represent text data using N-grams, Bag of Words, and TF-IDF vectorization • Develop core NLP applications including sentiment analysis, named entity recognition, and machine translation • Leverage transformer architectures like BERT and GPT for modern language understanding tasks
Computer Vision
Process images and videos using fundamental image processing and object detection techniques • Deploy advanced vision architectures like U-Net and Mask R-CNN for image segmentation • Solve real-world problems in healthcare diagnostics, automotive safety, and security surveillance
AI in Practice
Implement AI solutions across healthcare, finance, retail, robotics, and smart city applications • Investigate emerging trends in AI research and scientific discovery methodologies • Evaluate responsible AI frameworks, ethical implications, and regulatory compliance requirements
Advanced Topics in AI
Design explainable AI models that provide transparency and build user trust • Apply federated learning techniques for privacy-preserving decentralized machine learning • Contribute to AI for social good initiatives in environmental sustainability and public health
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | NLTK |
| Covered Tool / Platform | spaCy |
| Covered Tool / Platform | OpenCV |
| Covered Tool / Platform | Jupyter Notebook |
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