Machine Learning using Python

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



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