Description

Course Description

This 16-week industry-oriented certification focuses on how AI is applied to solve real-world problems across sectors such as healthcare, finance, retail, and manufacturing. The course emphasizes practical implementation, deployment, and project-based learning using modern AI tools and frameworks. Learners will gain the ability to design, train, evaluate, and deploy AI models, with a strong focus on ethics, explainability, and business value.

Intended Audience

• Industry professionals and domain experts seeking to apply AI in their fields
• Software engineers, IT professionals, and data engineers aiming to integrate AI solutions into workflows
• Product managers and business leaders who need AI literacy to manage AI-driven initiatives
• Students and early-career professionals who have prior exposure to ML and want to upskill in applied AI

Prerequisite Knowledge

• Basic knowledge of Python programming
• Familiarity with core ML concepts (supervised/unsupervised learning)
• No prior deep learning or deployment experience required

Course Highlights

• Focus on 70% hands-on, 30% theory
• Application-driven case studies across multiple industries
• Training in modern AI frameworks: TensorFlow, PyTorch, Hugging Face, OpenCV, MLflow, Docker
• Dedicated module on AI ethics, fairness, and explainability
• End-to-end Capstone Project involving deployment of an AI solution

Topics

• AI adoption across industries
• Case studies: healthcare, finance, retail, manufacturing
• Python refresher for AI workflows
• Introduction to Jupyter/Colab and project environment setup

Learning Outcomes

• Understand the scope and impact of AI in different domains
• Recognize key opportunities and limitations of AI adoption
• Set up a practical environment for applied AI projects

Labs / Hands-on

• Explore case studies and map AI opportunities in a chosen domain
• Set up Python environment, install essential AI libraries

Assessment

• Short quiz on AI in industry
• Submission: “AI Use Case Mapping” exercise for learner’s domain of choice

Module 2: Data for AI (Week 3–4)

Topics

• Data collection, cleaning, preprocessing
• Feature engineering for industry problems
• Data pipelines and automation (intro to PySpark, Airflow basics)

Learning Outcomes

• Prepare raw data for AI model training
• Build scalable pipelines for large datasets
• Automate preprocessing for repeatability

Labs / Hands-on

• Clean and preprocess a retail/finance dataset
• Build a mini data pipeline with PySpark

Assessment

• Mini Project 1: End-to-end data pipeline for an industry dataset

Topics

• Supervised learning (classification, regression) in industry use cases
• Unsupervised learning: clustering, anomaly detection, customer segmentation
• Business metrics vs ML metrics (precision/recall vs ROI, cost impact)

Learning Outcomes

• Apply ML to solve practical business problems
• Evaluate models with both technical and business KPIs
• Communicate AI results in business-relevant terms

Labs / Hands-on

• Fraud detection model (classification)
• Customer segmentation project using clustering
• Compare ML models with cost-sensitive metrics

Assessment

• Mini Project 2: End-to-end ML solution for a finance/retail dataset

Module 4: Deep Learning & Advanced AI (Week 9–12)

Topics

• Neural networks in practice: CNNs, RNNs, transfer learning
• Transformers and pre-trained models (BERT, ResNet)
• Applications: computer vision (OCR, defect detection), NLP (chatbots, summarization), recommendation systems

Learning Outcomes

• Train and fine-tune DL models for real-world tasks
• Apply transfer learning to domain-specific problems
• Implement applied NLP and computer vision solutions

Labs / Hands-on

• Image classification using CNNs
• Sentiment analysis with BERT
• OCR + summarization workflow for invoices

Assessment

• Mini Project 3: Applied deep learning prototype (vision or NLP)

Topics

• Responsible AI: fairness, bias, explainability
• AI model deployment: APIs, containers, CI/CD
• Monitoring, retraining, and lifecycle management (MLOps basics)

Learning Outcomes

• Identify and mitigate bias in AI models
• Deploy AI models into production-like environments
• Monitor and manage deployed models

Labs / Hands-on

• Bias detection on credit scoring dataset (using SHAP/LIME)
• Deploy a fraud detection model using Docker + FastAPI
• Track experiments with MLflow

Assessment

• Practical deployment exercise: containerize and deploy a trained ML model

 

Module 6: Capstone Project (Week 15–16)

Topics

• Problem scoping and dataset selection
• Model building, evaluation, deployment
• Documentation and presentation

Learning Outcomes

• Execute an end-to-end AI project with real-world complexity
• Demonstrate deployment readiness and ethical considerations
• Communicate AI project outcomes to technical and non-technical audiences

Labs / Hands-on

• Full-cycle project implementation (choose domain: healthcare, finance, retail, manufacturing)

Assessment

• Capstone Project submission and presentation: deployed AI product prototype with report