Why this course?

Description

This program has been initiated to enhance the knowledge from beginner to advance for the people who are interested in learning of advanced technologies like machine learning, artificial intelligence, deep learning etc...Other than learning, this program also provides an industrial exposure of working in these software’s and packages with its relevant application.

Technologies and Tools Covered 

• Python
• Open CV
• Keras
• Tensorflow

 LEARNING PATH

 1. Python & OpenCV programming

Introduction to the Concept of python and OpenCV will be dealt with. Simple programs shall be done by the participants. This shall serve as the first step into the entry to the advanced concepts implementation using python

SESSION	CLASS TOPICS
1	Introduction to Image Processing
2	Python -- Introduction ·       Python Strings ·       String Subscript and String slicing ·       If, For, While statements ·       While and else clause on loop ·       Break, continue and pass statement
3	Python on imaging ·       OpenCV library intro ·       Importing , Exporting and Visualizing images ·       Image Transformations ·       Type Conversions ·       Contrast Adjustment and Zooming & Pixel Info Analysis Placing Lines Circles Texts In a Image
4	Image conversions ·       Image Plane Separation ·       Image Gray and B&W ·       HSV Conversions Image block separation and fixing
5	Image filtering ·       Types of Noises in a Image ·       Blur in images ·       Median Filtering on noise removal
6	Region detection ·       Image Contour detection ·       Accessing Video and Storing ·       Accessing camera and storing ·       HSV Conversion based color detection ·       Remote camera accessing

 

2. Machine Learning Algorithms 

Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.

 

SESSION	CLASS TOPICS
1	Machine learning on image processing – an intro ·       Training Dataset. ·       Testing dataset. ·       Algorithms in ML
2.	Face recognition in real time ·       Face detection ·       Dataset Creation ·       Testing data ·       DRLBP pattern recognition
3.	Supervised Learning Methods-Linear Regression, Logistic Regression
4.	K-Nearest Neighbors Algorithm on image
5.	Working on Tensor Flows and Keras

 

Practical Assignments

Assignment 1: Image Transformations

Assignment 2: HSV Conversion based color detection

Assignment 3: Face detection

 

Training Methodology

The Program is mix of Theory sessions, Quizzes, Hands on Sessions, Liver Interaction with Experts, Assignments and Practical Exercises. Maximum Impetus is given to Hands on Sessions so as to enable the participants with the maximum knowledge transfer and satisfaction. The ratio of the theory, practical sessions will be 30:70.