Introduction

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task.artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents":

Contents

1. Introduction Of Machine Learning

• Introduction of Artificial Intelligence and Machine Learning

• Brief introduction to Machine Learning for AI

• Classification of Machine Learning

• Difference between Machine Learning and Artificial Intelligence

• Machine Learning Techniques

• Types of Learning

• Machine Learning System Design

• Supervised Learning- Regression

• Classification

• Future scope, Machine Learning And Artificial Intelligence

2. Introduction of Machine Learning Algorithm

• Regression

• Backpropagation

• Logistic Regression

• Decision Tree

• k-Nearest Neighbors (KNN)

• Clustering

3. Python/Anaconda

• Introduction to python and anaconda

• Conditional Statements

• Looping, Control Statements

• Lists, Tuple ,Dictionaries

• String Manipulation

• Functions

• Installing Packages

• Introduction of Various Tool

• Introduction of Anaconda

• Working on spyder ,Jupyter notebook

4. Working on Various Python Library

• Installing library and packages for machine learning and data science

• Matplotlib

• Scipy and Numpy

• Pandas

• IPython toolkit

• scikit-learn

5. Introduction of OpenCv

• Installing Opencv library

• Introduction of Opencv and its function

• Reading and Writing Image and Video

• Creating Different Shape

• Introduction of Haar-Casecade Classifier

• Working with images and videos

• Hand-written Recognition

Projects

• Simple Spam-Detecting Machine Learning Classifier

• Predict whether a loan applicant was likely to pay back their loan.

• Stock Market Data Analysis

• House Price Prediction using Regression

• Phishing Website Classification

• ODI Score Predictor

• Classification of Solar Flares

• Car-Number Plate Detection

• Face Detection /Eye Detection

• Working with wine Data Set

• Breast Cancer Wisconsin (Diagnostic) Classification

Eligibility Criteria

• Students having valid ID cards from recognized educational institutions are eligible for the workshop.

Requirements

• There are no prerequisite for attending this workshop. Anyone interested can participate in this workshop.

• Students will have to bring their own laptop.

Duration: 2 Days (7-8 Hours per day)

Contact:

Balram Gupta

8319553974

[email protected]