AI and ML WORKSHOP
A Hands-on 5-Day Workshop: From Python fundamentals to building and deploying real AI applications
This workshop takes participants on a structured journey from core Python programming to building and deploying real-world AI/ML applications. Each day combines focused theory with a substantial hands-on project, so learners leave with working code and a portfolio-ready capstone application.
By the End of the Workshop, Students Will Be Able To
Concrete, verifiable technical capabilities developed through guided programming labs.
Write Python Confidently
Confidently write Python programs utilizing core syntax, control flow, nested collections, functions, and structured file handling.
Clean & Analyze Datasets
Load, sanitize, manipulate, and visualize real-world messy datasets using high-performance NumPy arrays and Pandas dataframes.
Train Machine Learning Models
Formulate supervised and unsupervised problems: train Linear/Logistic Regression, Decision Trees, Random Forests, and K-Means algorithms.
Understand Neural Architecture
Understand the inner mechanics of neural networks: input/output weights, biases, non-linear activation functions, backpropagation, and loss functions.
Create and Deploy Complete ANN Models
Overcome real-world training obstacles: resolve overfitting, apply normalization techniques, tune hyperparameters, and deploy complete, functional Artificial Neural Network models for capstone demonstration.
5-Day Comprehensive Curriculum
Explore the exact schedule and session activities planned for each day of the workshop.
Day 1: Python Programming
AI/ML/DL intro, Python basics, data types, control flow, functions, file handling.
Opening Ceremony & Orientation
Welcome address, workshop objectives, setup verification, and instructor introductions.
Python Foundations
Core syntax, data types (Lists, Dictionaries), control flow, and assignment operators.
Lunch Break
Hands-on Lab: Logic & Functions
Hands-on lab practicing If/Else logic, for/while loops, and writing modular functions.
Python Problem Solving
Guided algorithmic challenge set and structured file manipulation.
Day 2: Data Science & Machine Learning Introduction
NumPy, Pandas, data cleaning, Matplotlib, Supervised vs. Unsupervised learning, regression, classification.
Data Analysis & Manipulation
NumPy array structures, matrix operations, Pandas DataFrames, data cleaning, and Matplotlib plotting.
Lunch Break
Introduction to Machine Learning
Supervised vs. Unsupervised learning paradigms, train/test split concepts, and model evaluation metrics.
ML Algorithms Coding Lab
Linear Regression and Logistic Regression derived and implemented in code.
Day 3: Machine Learning Algorithms
Decision Trees, Random Forest, K-Means Algorithm, and practical model building.
Advanced ML Algorithms
Decision Trees splitting criteria, Random Forest ensembles, and unsupervised K-Means clustering.
Lunch Break
Hands-on Practice: Model Building
Training and validating classification and clustering pipelines on real-world datasets.
Hands-on Practice: Pipeline Prototyping
Hyperparameter evaluation, cross-validation scoring, and model tuning.
Day 4: Neural Networks & Deep Learning
Introduction to neural networks, building Artificial Neural Network (ANN) models.
Introduction to Neural Networks
Neurons, input/output weights, biases, Activation Functions, Loss Functions, Backpropagation, and Optimizers.
Lunch Break
Model Training: Building the ANN
Defining network layers, forward passes, loss calculation, and backward weight updates.
Hands-on Practice: Custom ANN Model
Coding and training your own complete ANN model in Python/PyTorch.
Day 5: Final Project Building & Presentations
Overfitting control, hyperparameter tuning, project presentations, and certification.
Resolving Issues While Model Building
Overfitting control, normalization & numerical stability, and hyperparameter tuning.
Project Building Sprint: Part 1
Capstone construction and final data integration.
Lunch Break
Project Building Sprint: Part 2
Polishing models, generating evaluation curves, and preparing demonstrations.
Project Presentations
Student presentations defending their model architectures and results.
Closing Ceremony & Certificate Distribution
Graduation recognition and awarding of official certificates.
Meet the Instructors
Alumni and researchers from IIT Hyderabad and NIT Nagpur combining theoretical physics depth with hands-on coding mastery.
Quantum Field Theory, Quantum Chromodynamics, Renormalization, Scattering cross sections and Decay width Calculations.
Quantum Information Theory, Quantum Entanglement, Numerical techniques, Multibody physics, Simulations, Computational techniques for numerical solutions.
Tools & Requirements
Everything needed for full participation in the coding labs.