Certificate Programme in Applied Data Science and Artificial Intelligence: From Fundamentals to Deployment ( Batch 3)

Details

• Data Cleaning Techniques

• Data Normalisation and Standardisation

• Feature Selection

• Dimensionality Reduction

• Handling Categorical Variables

• Feature Engineering

• Balancing Datasets

• Include  SQL for dataset querying

Learning Outcomes

• Clean and preprocess raw datasets by handling missing values and outliers

• Normalize and standardize data for consistent model input

• Apply feature selection and dimensionality reduction techniques

• Encode categorical variables and engineer new features

• Balance imbalanced datasets to improve model fairness

• Use basic SQL queries to extract, filter, and join data from structured databases

Details

• 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

  • Evaluate models using metrics like accuracy, F1-score, and ROC-AUC

  • Tune models using cross-validation and hyperparameter search

• Apply concepts in a real-world mini-project: Credit Risk Classifier

Details

• 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

• Gain hands-on experience in building scalable, real-world DL solutions

Opencv

• Image processing,

• face detection,

• contour analysis,

• object tracking,

• real-time video apps

NLP

• Generative AI and LLMs

• Prompt engineering

• Using OpenAI APIs

• Building Q&A bots with LLMs

• Langchain Framework

• Dspy

AI project

• AI in Healthcare

• Forecasting using Time Series

• 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. 

Details

• Introduction to Model Deployment

• Containerizsation 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. 

Details

Offer track-wise capstone options:

• Generative AI project

• AI in Healthcare

• Forecasting using Time Series

• E-commerce Recommender System

Anyone with a Graduate or Diploma (10+2+3) qualification from a recognised university can apply for the data science course offered by CEP, IIT Delhi. Preference is given to those with backgrounds in Computer Science, IT, Electronics, Electrical, Physics, or related fields.

This CEP, IIT Delhi data science certification course is delivered online through live lectures, supported by tutorials and hands-on projects. Classes are held every Sunday that allow working professionals or students to engage with the course without interrupting their weekly schedules.

This data science course by CEP, IIT Delhi, includes Python programming, data cleaning, supervised and unsupervised learning, neural networks, natural language processing, and model deployment using cloud tools, Docker, and industry-relevant MLOps practices.

The data science programme offered by CEP, IIT Delhi, uses live online sessions, real case studies, hands-on tutorials, and a capstone project. It supports application-focused learning guided by expert faculty and supplemented by interactive tools and collaboration opportunities.

Yes, the CEP IIT Delhi data science online course offers an optional one-day campus immersion. This six-hour visit gives you a chance to interact in person with IIT Delhi faculty and peers, complementing your online learning experience.

This CEP IIT Delhi online data science course offers a flexible learning format with practical exposure to AI tools, real-world datasets, and deployment frameworks. It prepares you for roles requiring both technical depth and hands-on ability.

Yes. Upon meeting the minimum requirements, you will receive a certificate issued by CEP, IIT Delhi. The CEP IIT Delhi data science certification is awarded based on attendance, project completion, and performance in the end-of-programme assessment.