Artificial Intelgence

COURSE DESCRIPTION

Artificial Intelligence is a module based course, in this course we will cover different modules from beginning to advance . students will gain hand on Experience on different AI based projects.

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.

This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

 

COURSE CONTENTS

Module 1 : Machine learning1 month
Introduction to Machine Learning
Learning Techniques in ML
Steps of Machine Learning
Complete Pathway with associated libraries and packages
Binary and Multi classification using machine learning models
Classification and Implementation of different classifiers
Regression and Implementation of different Regressors
Clustering and Implementation of different clustering techniques
Data Preprocessing Techniques
Selection of best fit model with respect to your data
Training Machine Learning models
Model testing and Validation
Designing Decision supporting system using Machine Learning
Estimation of next Value prediction using Machine Learning
Module 2 : Deep learning1 month
Introduction to Neural Networks and Deep Learning.
Difference between Machine Learning and Deep Learning
Binary and Multi classification using Deep learning models
Perceptron and Implementation of Multi Layer perceptron.
Binary and Multi classification using machine learning models
Convolution neural Network
VGG and ALaxnet
Recurrent Neural network
LSTM
Designing an advance decision supporting system using Deep Learning
Prediction of next value using Deep Learning
Hyperparameters tuning, Regularization and Optimization
Image classification using Deep Learning
Implementation of time series analysis using Deep Learning models
Module 3 : Image processing 1 month
Introduction to Image processing
Histograms
Fourier Transform – FFT
Noise - linear and nonlinear filters
Noise - Edge Detection
Filters, Contrast, Transformation and Morphology
Image restoration, Noise, Segmentation and Contours
Geometrical operation on an image
Manual object detection using HSV
Module 4 : Computer vision 1 month
Introduction to computer vision
Difference between Image processing and Computer vision
Object detection
Image segmentation
Face detection using Cascade classifier
Face Recognition
Object tracking
Video and Image Basics in computer vision
Working with OpenCV Dlib and Facenet