IIT Delhi Certificate Programme in Applied Data Science and Artificial Intelligence: From Fundamentals to Deployment - Batch 02

Module 3: Machine Learning with Examples

• Supervised Learning (Linear, Logistic Regression)
• Classification Algorithms (Decision Trees, KNN, Naive Bayes, SVM)
• Ensemble Methods (Random Forest, Gradient Boosting)
• Clustering Algorithms (K-means, DBSCAN)
• Model Evaluation Metrics
• Cross-Validation
• Hyperparameter Tuning
• XGBoost, LightGBM, stacking/blending
• Focused mini-project: “Credit Risk Classifier using ML”

Learning Outcomes

• Build supervised models for regression and classification tasks
• Implement popular ML algorithms like Decision Trees, SVM, KNN, Naive Bayes
• Use ensemble methods including Random Forest and Gradient Boosting
• Apply advanced models like XGBoost and LightGBM for high performance
• Combine models using stacking and blending for better accuracy
  1. Evaluate models using metrics like accuracy, F1-score, and ROC-AUC
  2. Tune models using cross-validation and hyperparameter search
  3. Apply concepts in a real-world mini-project: Credit Risk Classifier

Module 4: Deep Learning

• Neural Networks Basics
• Training Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transfer Learning (e.g., ResNet, BERT, Transformers)
• Hands-on with TensorFlow/Keras in Google Colab

Learning Outcomes

• Understand the architecture and training of neural networks
• Build and train CNNs for image tasks and RNNs for sequence data
• Apply Transfer Learning using pre-trained models like ResNet (vision) and BERT (text)
• Develop and train deep learning models using TensorFlow/Keras in Google Colab
  1. Gain hands-on experience in building scalable, real-world DL solutions

Module 5: Applied Industry Cases

• OpenCV:
  1. Image processing
  2. Face detection
  3. Contour analysis
  4. Object tracking
  5. Real-time video apps
• NLP:
  1. Generative AI and LLMs
  2. Prompt engineering
  3. Using OpenAI APIs
  4. Building Q&A bots with LLMs
  5. Langchain Framework
  6. Dspy
• AI Projects:
  1. AI in Healthcare
  2. Forecasting using Time Series
  3. E-commerce Recommender System

Learning Outcomes

• Apply OpenCV for image processing, face detection, object tracking, and real-time video analysis.
• Use NLP and LLMs for prompt engineering, Q&A bots, and text generation with OpenAI APIs.
• Build smart applications using LangChain, Dspy, and Generative AI techniques.
• Develop AI solutions for healthcare, time series forecasting, and recommender systems.
• Gain hands-on experience in building real-world AI/ML projects across multiple domains.

Module 6: Applied model Deployment and Special Topics

• Introduction to Model Deployment
• Containerisation with Docker
• Deployment Frameworks (Flask, FastAPI)
• Cloud Deployment (AWS, GCP, Azure)
• Model Monitoring and Management
• CI/CD for ML Models
• MLOps Principles
• Detecting and diagnosing faults
• MLflow or W&B for model tracking
• Real deployment demo (e.g., Streamlit app + backend API)

Learning Outcomes

• Understand the end-to-end process of deploying machine learning models in production.
• Containerise machine learning models using Docker for scalable deployment.
• Deploy models as APIs using frameworks such as Flask and FastAPI.
• Implement cloud-based deployment solutions using AWS, GCP, or Azure.
• Monitor model performance in production and manage updates to deployed models.
• Integrate CI/CD pipelines for continuous model deployment and scaling using MLFlow or W&B.
• Apply MLOps principles to manage the entire machine learning lifecycle from development to deployment.
• Build real-world apps with Streamlit and backend APIs.

🔁 Offer track-wise capstone options:

• Generative AI project
• AI in Healthcare
• Forecasting using Time Series
• E-commerce Recommender System