About Workshop:

1. To make the learner identify potential zones of uses of AI and ML.

2. Providing experience in working with real-time applications of  Machine Learning to the learner.

Prerequisites: Candidate must be aware of Python Programming

Attendee: Any UG/PG Students

Content:

Module 1

• Introduction to Artificial Intelligence
• Applications of AI & Current trends
• Different AI Techniques
• AI Agents
• PEAS Analysis
• Agent Environment Analysis
• Different Types of AI Agents
• Machine Learning
• Introduction and Applications of Machine Learning
• Supervised and Unsupervised Learning
• Classification & Regression Problem
• Clustering, Anomaly Detection
• Getting started with Linear Regression
• Mathematics behind Linear Regression
• Building Linear Model
• Gradient Descent Algorithm
• Error Correction

Module 2

• Getting started with python programming
• Installing Anaconda
• Python variables, lists, tuples and dictionaries
• Control Structure in Python
• Defining Functions in Python
• Using modules and packages
• Numpy for Data computation
• Matlplotlib for Data Visualization
• Pandas for data exploration
• Using scikit-learn
• Creating linear regression models using scikit-learn

Module 3

• Getting Started with Artificial Neural Networks
• Introduction to neurons, weights
• Activation Function
• Input Layers, Hidden Layers and Output Layers
• Single layer perceptron Model
• Multilayer Neural Network
• Back Propagation Algorithm introduction
• Programming Neural Network using Python
• Building Regression models using ANN
• Classification Examples using ANN

Module 4

• K Nearest Neighbor Models
• Using KNN for Data Classification
• Building Models using KNN
• Support Vector Machine – Applications and Mathematics
• Using SVM for classification
• Projects