Computer Vision- Instructor Led Course

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    Anshu Pandey
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Computer Vision- Instructor Led Course

This course is part of specialization programs

This course can be applied to multiple Specializations or Professional programs. Completing this course will count towards your learning in any of the following programs


Pre-skills/ Prerequisites

A learner must have completed Deep Learning Basic Course



Course Curriculum

Module – 1 – Image Processing – 6 Hours

  • Getting started with Images
  • Image Processing with OpenCV
  • Image Acquisition and manipulation using OpenCV
  • Video Processing
  • Edge Detection
  • Corner Detection
  • Color Detection
  • Face Detection
  • Image Scaling for ANN
  • Face Detection in an image frame
  • Object detection


Module 2 – Convolution Neural Networks – 6 Hours

  • Convolutional Neural Networks
  • CNN Architecture
  • The Neuroscientific Basis for Convolutional Networks
  • Convolution Process
  • MaxPooling, dropout
  • Maths behind CNNs
  • Feature Extraction
  • Variants of the Basic Convolution Function
  • Efficient Convolution Algorithms
  • Variety of Convolutional Networks
  • Implementing CNNs using Keras
  • MNIST Data – Digit Classification using CNN
  • Fashion Product Identification using CNN
  • CNN implementation example using Pytorch


Module 3 – Transfer Learning for Computer Vision – 8 Hours

  • Understanding the concept of Transfer Learning
  • Ideas for transfer learning approach for different situations
  • Overview of imagenet datasets and pre-trained models – VGG16, VGG19, ResNet50, MobileNet
  • Implementing Transfer Learning-based Image Classifier using ResNet50
  • Optimizing Transfer Learning-based models
  • Using other pre-trained models from Tensorflow hub for Computer Vision applications
  • Video Processing using Neural Networks


Module 4 – Object Localization and Object Detection – 8 Hours

  • Object Localization Overview
  • Image augmentation
  • Bounding box augmentation
  • Object Localization using Transfer Learning
  • Object Detection Overview
  • Region based CNN
  • Fast RCNN
  • Faster RCNN
  • Implementing RCNN using Tensorflow
  • Introduction to SSD
  • Working of SSD
  • Implementation of SSD using tensorflow


Module 7 – Image Segmentation – 4 Hours

  • Image segmentation introduction
  • Encoder Decoder approach for Image segmentation
  • Masked RCNN
  • Working with Unet – Encoder Decoder approach
  • Implementation with Python


Module 5 – Generative Adversarial Networks – 4 Hours

  • Introduction to GANs
  • Applications of GANs
  • Generator and Discriminators
  • DCGANs
  • Implementation of DCGANs using TensorFlow


Module 6 – Capstone Project

  • Working Final Project
  • Splitting final Project into phases
  • Working on structuring project
  • Summary


Course Features

  • More than 80% hands-on session
  • Project-oriented learning
  • Cloud-based LMS
  • Experienced Trainer
  • Masterclass for Expert
  • Standard reading materials
  • Study resources
  • Graded Quizzes
  • Real-time case study-based projects
  • Discussion Forum


Machine Requirement

  1. Windows Machine (Windows 7 or Above) /Linux Machine
  2. Only 64 Bit
  3. 8 GB RAM 
  4. NVIDIA Graphics Card (Recommended)


Fee INR 10,000/- including all Tax and Services
Duration 36 Hours Hand On Training + 10 Hours Project Work
Mode of Training Online Live Instructor-Led Class



Upcoming Batch Schedule From – 24th Sept 2020, 6:30 PM to 8 PM, IST (Monday to Friday )

Ask for one to one online meeting with our technical expert.



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A Technical expert and a passionate trainer has expertise in the field of Artificial Intelligence, Machine Learning and IoT, he has a proven work record of delivering more than 100+ workshops and Technical Training in various technologies and domains at the top MNCs, premier organizations including IITs, NITs and other premier educational organizations. He has delivered 50+ corporate Training to clients from India and abroad. Skills- Programming Languages: Python, Arduino, R, MATLAB Scripting Languages: HTML, JS, CSS AI Skills: Machine Learning – Linear & Logistic Regression, Clustering, Artificial Neural Networks, SVM, Genetic Algorithm, Fuzzy Logic, Natural Language Processing, PCA, CNN, LSTM AI, ML Tools & Platforms: Python, Tensorflow, Keras, Azure ML, AWS, Apache Spark Hardware: Arduino, Raspberry Pi, AVR, Intel Edison, Xbee, LoRaWAN IoT Platforms: AWS IoT, Thingworx, ThingsBoard, IBM Watson IoT, ThingSpeak, myDevices, Twilio, node-red, Ubidots, PubNub, IoT Gateway


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