Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI Literacy for Everyone is a plain-language foundation for anyone who wants to understand the technology now reshaping work and society — no coding or mathematics required. You learn what machine learning actually is, how tools like large language models generate their answers, and crucially where they fail: bias, hallucination and overconfidence. The course is practical, covering how to use AI tools effectively and responsibly, how to judge AI claims critically, and what the ethical and workplace implications are. You finish confident to reason about, and work alongside, AI. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
AI Literacy for Everyone is a non-technical foundation in how modern AI works, what it can and cannot do, and how to use it responsibly at work and in daily life.
📋 Course Objectives
1. Explain in plain terms how machine learning and LLMs work.
2. Recognise what AI can and cannot reliably do.
3. Identify bias, hallucination and other failure modes.
4. Use everyday AI tools effectively and responsibly.
5. Judge AI claims and implications critically.
👥 Who Should Enroll?
• Professionals in any field adapting to AI
• Managers and decision-makers
• Students and lifelong learners
• Anyone wanting to understand AI without jargon
🚀 Key Learning Outcomes
• Confidence to understand and discuss AI.
• Practical skill using everyday AI tools well.
• A critical, ethics-aware perspective.
• 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
AI Fundamentals, Mathematics, and Foundations
Analyze the mathematical prerequisites for AI, including linear algebra, calculus, and probability theory • Develop a comprehensive understanding of AI fundamentals, including machine learning, deep learning, and neural networks • Evaluate the importance of data structures and algorithms in AI, including arrays, linked lists, stacks, and queues
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines using tools like Apache Beam, Apache Spark, and AWS Glue • Configure data preprocessing techniques, including data cleaning, feature scaling, and data transformation • Develop and deploy feature engineering pipelines using techniques like feature extraction, selection, and construction
Model Architecture, Algorithm Design, and Methods
Implement and evaluate different machine learning algorithms, including supervised, unsupervised, and reinforcement learning • Develop and design model architectures, including convolutional neural networks, recurrent neural networks, and transformers • Analyze and compare the performance of different model architectures and algorithms on various datasets
Training, Hyperparameter Optimization, and Evaluation
Configure and optimize hyperparameters using techniques like grid search, random search, and Bayesian optimization • Develop and implement model training pipelines using tools like TensorFlow, PyTorch, and Scikit-learn • Evaluate and analyze model performance using metrics like accuracy, precision, recall, and F1-score
Deployment, MLOps, and Production Workflows
Design and implement model deployment pipelines using tools like Docker, Kubernetes, and TensorFlow Serving • Develop and configure MLOps workflows, including model monitoring, logging, and alerting • Configure and optimize production workflows, including model serving, scaling, and load balancing
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and evaluate the ethical implications of AI systems, including bias, fairness, and transparency • Develop and implement bias mitigation techniques, including data preprocessing, feature engineering, and model regularization • Configure and optimize responsible AI practices, including model interpretability, explainability, and accountability
Industry Integration, Business Applications, and Case Studies
Develop and implement AI solutions for various industries, including healthcare, finance, and retail • Analyze and evaluate the business value of AI systems, including ROI, cost savings, and revenue growth • Configure and optimize AI-powered workflows, including automation, augmentation, and decision support
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Apache Beam |
| Covered Tool / Platform | Apache Spark |
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