This website uses cookies to personalize content and analyse traffic in order to offer you a better experience. Cookie policy

Accept

|| Enhance your Learning with BIT's Cutting edge platform

computer vision training ,features of BIT coaching classes ,global impact ,cutting edge technologies ,research opportunities and industry demand

Certificate

|| What will I learn?

  • Understand the fundamental principles and concepts of computer vision.
  • Process and analyze images and videos using computer vision techniques.
  • Implement algorithms for feature detection and matching.
  • Develop methods for object recognition and classification.
  • Apply deep learning techniques to computer vision tasks.
  • Solve real-world problems using computer vision technologies.

|| What will I learn?

  • Understand the fundamental principles and concepts of computer vision.
  • Process and analyze images and videos using computer vision techniques.
  • Implement algorithms for feature detection and matching.
  • Develop methods for object recognition and classification.
  • Apply deep learning techniques to computer vision tasks.
  • Solve real-world problems using computer vision technologies.

|| Requirements

  • Proficiency in a programming language (Python preferred)
  • Familiarity with machine learning concepts
  • Basic understanding of linear algebra, calculus, and probability

|| Requirements

  • Proficiency in a programming language (Python preferred)
  • Familiarity with machine learning concepts
  • Basic understanding of linear algebra, calculus, and probability


    Our computer vision course content encompasses a comprehensive journey into the realm of teaching computers to understand visual data. It begins with foundational concepts, introducing students to the fundamental principles of image processing to refine and manipulate images for analysis. From there, the course progresses into more intricate techniques, including feature extraction, where students learn to identify key elements within images. Advancing further, students explore object detection and recognition, mastering algorithms that can locate and categorize objects within visual data. A significant portion of the course is dedicated to deep learning methodologies, particularly convolutional neural networks (CNNs), renowned for their efficacy in deciphering images. As the curriculum unfolds, students delve into semantic segmentation, object tracking, and the complexities of 3D computer vision, gaining insights into interpreting visual data in both 2D and 3D domains. Throughout the journey, emphasis is placed on real-world applications spanning diverse fields such as facial recognition, medical imaging, autonomous vehicles, and augmented reality. The course is punctuated with hands-on projects, providing students with practical experience in implementing computer vision solutions and honing their skills. By the course's conclusion, students emerge equipped with a comprehensive toolkit and deep understanding, ready to navigate the intricacies of computer vision and contribute to its ever-evolving landscape.

    computer vision Learning pathway ,Computer vision roadmap ,input data ,preprocessing ,feature extraction ,action


    • Computer Vision
    • Image Formation
    • Sampling and Quantisation
    • Image Processing – flipping, cropping, rotating, scaling
    • Image statistics & Histogram
    • Spatial Resolution
    • Gray level/Intensity Resolution
    • Spatial Filtering
    • Convolution
    • Smoothing, Sharpening
    • Color Space Conversion & Histogram


    • Thresholding for Binarization
    • Morphological Operations
    • Image Gradient
    • Bounding Box
    • Sobel’s Edge Detection Operator
    • Template Matching
    • Image Feature – Keypoint and Descriptor
    • Harris Corner Detector
    • Object Detection with HoG
    • Stream Video Processing with OpenCV

    • Computer Vision
    • Convolution Neural Networks (CNN)
    • Why CNN? Building an Intuition for CNN
    • CNN, Kernels, Channels, Feature Maps, Stride, Padding
    • Receptive Fields, Image Output Dimensionality Calculations, MNIST Dataset
    • Explorations with CNN
    • MNIST CNN Intuition, Tensorspace.js, CNN Explained, CIFAR 10 Dataset Explorations with CNN
    • Dropout & Custom Image Classification for Cat and Dog Datasets
    • Deployment in Heroku, AWS or Azure


    • CNN Architectures
    • LeNet-5
    • AlexNet, VGGNet
    • Inception, ResNet
    • Data Augmentation
    • Benefits of Data Augmentation
    • Exploring Research Papers
    • Exploring Augmentor


    • Object Detection Basics
    • What is Object Detection?
    • Competitions for Object Detection
    • Bounding Boxes
    • Bounding Box Regression
    • Intersection over Union (IoU)
    • Precision & Recall
    • What is Average Precision?
    • Practical Training using Tensorflow1.x
    • Custom Model Training in TFOD1.x
    • Our Custom Dataset
    • Doing Annotations or labelling data
    • Pretrained Model from Model Zoo
    • Files Setup for Training
    • Export Frozen Inference Graph
    • Inferencing with our trained model in Colab, Training in Local
    • Inferencing with our trained model in Local


    • Practical Training using Tensorflow2.x
    • Introduction to TFOD2.x
    • Using the Default Colab Notebook
    • Google Colab & Drive Setup


    • Visiting TFOD2.x Model Garden
    • Inference using Pretrained Model
    • Inferencing in Local with a pretrained model


    • Practical Object Detection Using YOLO V5
    • Introduction for YoloV5
    • YoloV5 Google Colab Setup
    • Inferencing using Pre-Trained Model


  • Prompt Engineering
  • Why Prompt Engineering?
  • ChatGPT
  • Few Standard Definitions:
  • Label
  • Logic
  • Model Parameters (LLM Parameters)
  • Basic Prompts and Prompt Formatting
  • Elements of a Prompt:
  • Context
  • Task Specification
  • Constraints
  • General Tips for Designing Prompts:
  • Be Specific
  • Keep it Concise
  • Be Contextually Aware
  • Test and Iterate
  • Prompt Engineering Use Cases
  • Information Extraction
  • Text Summarization
  • Question Answering
  • Code Generation
  • Text Classification
  • Prompt Engineering Techniques
  • N-shot Prompting
  • Zero-shot Prompting
  • Chain-of-Thought (CoT) Prompting
  • Generated Knowledge Prompting

Get in touch

|| Scope of Computer Vision

Read more
placement report

|| Computer Vision Career Option in India

Read more

|| Computer Vision holds a Prominent Position in Indian job Market 

Read more

|| Job Roles and Salary

computer vision  Course in Vadodara ,Computer Vision Engineer ,Research scientist ,machine learning algorithm ,software developer  ,data scientist ,autonomous vehicle engineer



Certificate

|| Average Salary for Computer Vision in India

Read more

|| Become an Expert in Computer Vision

computer vision-skills to master ,image processing techniques ,machine learning and deep learning ,3D computer vision ,Semantic segmentation ,Software Development and Deployment

Certificate

|| Some Prominent Companies in India that use Computer Vision

Read more

|| Top Hiring Companies

Hiring Companies ,Top Companies ,Job Placement ,Patterns,Cognizant,Ananta ,Tech Mahindra ,Rapido ,Accenture ,Top Hiring Companies at BIT , Top Placement Companies at BIT

Certificate

|| Get Certified, Get Ahead

Three easy steps will unlock your Computer Vision Certification:

 

  • Finish the online / offline course of Computer Vision Course and the Assignment
  • Take on and successfully complete a number of industry-based Projects
  • Pass the Computer Vision Certification exam


The certificate for this Computer Vision Certification will be sent to you through our learning management system, where you can also download it. Add  a link to your certificate to your CV or LinkedIn profile.


Certificate

|| Frequently asked question

BIT offers a wide range of programs catering to various interests and career paths. These may include academic courses, vocational training, professional development, and more. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs.

This course is suitable for students, professionals, and enthusiasts interested in gaining expertise in computer vision. It caters to individuals with varying levels of experience, from beginners with no prior knowledge to experienced practitioners looking to specialize in computer vision techniques.

Most reputable Computer Vision courses offer a certificate of completion, which can enhance your credentials and be added to your resume or LinkedIn profile. It's essential to verify the accreditation and recognition of the issuing institution or organization.

Yes, many Computer Vision courses are available online, offering flexibility in terms of timing and location. Online courses often provide video lectures, interactive exercises, and discussion forums to facilitate learning.

After completing the course, you may continue to have access to course materials, online resources, alumni networks, career services, and professional development opportunities to support your continued learning and career growth.

For any questions or assistance regarding the enrolment process, admissions requirements, or program details, please don't hesitate to reach out to our friendly admissions team. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs or Visit Our Centers – Sayajigunj, Waghodia Road, Manjalpur in Vadodara, Anand, Nadiad, Ahmedabad

BIT prides itself on providing high-quality education, personalized attention, and hands-on learning experiences. Our dedicated faculty, state-of-the-art facilities, industry partnerships, and commitment to student success make us a preferred choice for students seeking a rewarding educational journey.

BIT committed to supporting students throughout their academic journey. We offer a range of support services, including academic advising, tutoring, career counselling, and wellness resources. Our goal is to ensure that every student has the tools and support they need to succeed.

We will use Python and libraries such as OpenCV, TensorFlow, and Keras. Course materials include lecture notes, videos, and relevant articles.

Yes, the Computer Vision course by BIT is designed to equip you with practical skills that are directly applicable to real-world projects.

There will be a discussion forum for students to ask questions and collaborate. Instructors and teaching assistants will also provide support.
-->
Call Now!
Special Offer!
Tap Here!