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

Introduction to Python

Control Flow (Conditionals, Loops)

Functions and Modules

Data Structures (Lists, Dictionaries, Sets, Tuples)

Object-Oriented Programming

Error Handling

Libraries Overview (NumPy, Pandas, Matplotlib)

Scientifics Computing and Graphing

Real-world scripting use-cases (e.g., file parsing, web scraping)

Projects like “Python Web Scraper” or “Data Cleaner Script”

Develop proficiency in writing Python programs to solve computational problems.

Understand core programming concepts such as data types, control flow, functions, and OOP principles.

Manipulate data structures such as lists, dictionaries, and sets efficiently.

Utilise key Python libraries (NumPy, Pandas, Matplotlib) for data manipulation and visualisation.

Debug and handle errors in Python programs effectively.

File parsing and web scraping for real-world data collection.

Complete hands-on mini-projects like a Python Web Scraper and Data Cleaner Script.

Data Cleaning Techniques

Data Normalisation and Standardisation

Feature Selection

Dimensionality Reduction

Handling Categorical Variables

Feature Engineering

Balancing Datasets

Include SQL for dataset querying

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

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”

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

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

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

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

Image processing,

face detection,

contour analysis,

object tracking,

real-time video apps

Generative AI and LLMs

Prompt engineering

Using OpenAI APIs

Building Q&A bots with LLMs

Langchain Framework

Dspy

AI in Healthcare

Forecasting using Time Series

E-commerce Recommender System

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.

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)

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.

Generative AI project

AI in Healthcare

Forecasting using Time Series

E-commerce Recommender System

Graduates/Diploma Holders (only 10+2+3) from a recognized university.

Preference will be given to graduates in Computer Science/IT, Electronics, Electrical, Physics, or a relevant stream.

Candidates pursuing a graduation degree in any discipline are also eligible.