Computer Vision Corporate Training Course

Edstellar's Computer Vision instructor-led training course equips you with the ability to interpret and leverage visual data through cutting-edge technology. It provides techniques to extract meaningful information from images and video data to make informed business decisions. Upskill the workforce to efficiently interpret the visual world.

24 - 40 hrs
Instructor-led (On-site/Virtual)
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Computer Vision Training

Drive Team Excellence with Computer Vision Corporate Training

On-site or Online Computer Vision Training - Get the best Computer Vision training from top-rated instructors to upskill your teams.

Computer vision combines hardware and algorithms to attempt to understand images and scenes. It involves the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. This technology is utilized in various applications, from simple tasks like scanning barcodes to complex ones like self-driving cars.

The Computer Vision instructor-led training course offered by Edstellar is structured to build a solid computer vision foundation, focusing on the latest developments and techniques in the field. This virtual/onsite Computer Vision training course equips professionals with the skills in computer vision and CNN architecture to transform the organization's operational efficiency and strategic capabilities.

Computer Vision Training for Employees: Key Learning Outcomes

Develop essential skills from industry-recognized Computer Vision training providers. The course includes the following key learning outcomes:

  • Integrate advanced machine learning models to automate and improve visual tasks
  • Adapt computer vision capabilities to align with emerging technologies and market trends
  • Construct innovative solutions to industry-specific challenges using computer vision techniques
  • Apply computer vision methodologies to improve operational efficiency and product development
  • Design algorithms that effectively interpret and analyze images and video for various applications
  • Synthesize information from multiple visual sources to create comprehensive data analysis strategies
  • Evaluate complex visual data to understand and enhance decision-making processes within the organization

Key Benefits of the Training

  • Training helps teams to analyze customer behaviors and preferences
  • A well-trained workforce improves security and safety in the workplace
  • By training employees, organizations can reduce visual data processing time
  • Professionals can ensure higher accuracy in defect detection and quality assurance
  • Trained teams with the insights of visual data can inform strategic decisions and innovation initiatives

Computer Vision Training Topics and Outline

This Computer Vision Training curriculum is meticulously designed by industry experts according to the current industry requirements and standards. The program provides an interactive learning experience that focuses on the dynamic demands of the field, ensuring relevance and applicability.

  1. Definition and scope of computer vision
    • What is Computer vision?
    • Goals of Computer Vision
    • Applications of Computer Vision
  2. Key Milestones in Computer Vision Development
    • Early Work in Computer Vision
    • The Development of Digital Cameras
    • The Rise of Artificial Intelligence and Machine Learning
    • Modern Applications of Computer Vision
  3. Applications and Use Cases in Industry
    • Medical Imaging and Diagnosis
    • Robotics and Autonomous Systems
    • Surveillance and Security
    • Self-driving Cars
    • Augmented Reality and Virtual Reality
  1. Basic components of a vision system
    • Image acquisition device
    • Image processing unit
    • Feature extraction module
    • Pattern recognition module
    • Output interface
  2. Data flow in vision systems
    • Image acquisition
    • Pre-processing
    • Feature extraction
    • Pattern recognition
    • Output generation
  3. Hardware and software requirements
  4. Hardware requirements
    • Processing power
    • Memory
    • Storage
  5. Software requirements
    • Operating system
    • Programming languages
    • Libraries and frameworks
  1. Pixel structure and image data types
    • Pixel structure
    • Color representation
    • Image resolution
  2. Image data types
    • Integer images
    • Floating-point images
    • Compressed images
  3. Color spaces and transformations (RGB, HSV, grayscale)
    • RGB color space
    • HSV color space
    • Grayscale images
    • Color space transformations
  4. Image file formats (JPEG, PNG, BMP, TIFF)
    • JPEG format
    • PNG format
    • BMP format
    • TIFF format
  1. Image acquisition techniques
    • Cameras
    • Scanners
    • Sensors
  2. Pre-processing steps
    • Noise reduction
    • Image enhancement
    • Registration
    • Segmentation
  3. Feature extraction and pattern recognition
  4. Feature extraction techniques
    • Corners
    • Edges
    • Blobs
  5. Pattern recognition techniques
    • Template matching
    • Statistical methods
    • Machine learning techniques
  1. The role of convolution in feature detection
    • Convolutional filters
    • Feature maps
  2. Types of filters (Sobel, Scharr, Laplacian)
    • Sobel filter
    • Scharr filter
    • Laplacian filter
  3. Pooling concepts (max, average)
    • Max pooling
    • Average pooling
  1. Sampling theory and aliasing
    • Nyquist theorem
    • Aliasing
  2. Spatial and temporal resolution
    • Spatial resolution
    • Temporal resolution
  3. Downsampling and upsampling strategies
  4. Downsampling strategies
    • Averaging
    • Interpolation
  5. Upsampling strategies
    • Nearest neighbor
    • Bilinear interpolation
    • Bicubic interpolation
  1. Image enhancement techniques (histogram equalization, normalization)
    • Histogram equalization
    • Normalization
  2. Spatial domain filtering
    • Linear filters
    • Non-linear filters
  3. Frequency domain filtering (Fourier transform, band-pass filters)
    • Fourier transform
    • Band-pass filters
  1. Understanding the structure of a convolutional layer
    • Convolution operation
    • Activation functions
    • Pooling layers
  2. Role of non-linearity (activation functions)
    • Sigmoid function
    • Tanh function
    • ReLU function
  3. Training CNNs: Loss functions and backpropagation
  4. Loss functions
    • Cross-entropy loss
    • Mean squared error
    • Backpropagation
  1. Padding techniques and their impact on layer output size
    • Zero padding
    • Same padding
    • Valid padding
  2. Detailed pooling strategies (global average pooling, RoI pooling)
    • Global average pooling
    • RoI pooling
  3. Advanced filtering and feature map interpretation
    • Dilated convolution
    • Atrous convolutions
    • Feature map visualization
  1. LeNet, AlexNet, and VGG
  2. LeNet architecture
    • Introduction to LeNet
    • Architecture of LeNet
  3. AlexNet architecture
    • Introduction to AlexNet
    • Architecture of AlexNet
  4. VGG architecture
    • Introduction to VGG
    • Architecture of VGG
  5. GoogLeNet and ResNets
  6. GoogLeNet architecture
    • Introduction to GoogLeNet
    • Architecture of GoogLeNet
  7. ResNet architecture
    • Introduction to ResNet
    • Architecture of ResNet
  8. Modern architectures: EfficientNet and capsule networks
  9. EfficientNet architecture
    • Introduction to EfficientNet
    • Architecture of EfficientNet
  10. Capsule networks
    • Introduction to capsule networks
    • Applications of capsule networks
  1. Concepts and benefits of transfer learning
    • Definition of transfer learning
    • Benefits of transfer learning
  2. Source and target tasks in transfer learning
    • Source task
    • Target task
  3. Pre-trained models and fine-tuning
    • Pre-trained models
    • Fine-tuning

1. Transfer learning strategies (feature extraction, fine-tuning)

  • Feature extraction
  • Fine-tuning

2. Managing data imbalance with transfer learning

  • Dealing with data imbalance
  • Using transfer learning to address data imbalance

3. Transfer learning in different domains (NLP, computer vision, etc.)

  • Transfer learning in NLP
  • Transfer learning in computer vision
  • Transfer learning in other domains

This Corporate Training for Computer Vision is ideal for:

What Sets Us Apart?

Computer Vision Corporate Training Prices

Elevate your team's Computer Vision skills with our Computer Vision corporate training course. Choose from transparent pricing options tailored to your needs. Whether you have a training requirement for a small group or for large groups, our training solutions have you covered.

Request for a quote to know about our Computer Vision corporate training cost and plan the training initiative for your teams. Our cost-effective Computer Vision training pricing ensures you receive the highest value on your investment.

Request for a Quote

Our customized corporate training packages offer various benefits. Maximize your organization's training budget and save big on your Computer Vision training by choosing one of our training packages. This option is best suited for organizations with multiple training requirements. Our training packages are a cost-effective way to scale up your workforce skill transformation efforts..

Starter Package

125 licenses

64 hours of training (includes VILT/In-person On-site)

Tailored for SMBs

Most Popular
Growth Package

350 licenses

160 hours of training (includes VILT/In-person On-site)

Ideal for growing SMBs

Enterprise Package

900 licenses

400 hours of training (includes VILT/In-person On-site)

Designed for large corporations

Custom Package

Unlimited licenses

Unlimited duration

Designed for large corporations

View Corporate Training Packages

This Corporate Training for Computer Vision is ideal for:

The Computer Vision training course is designed for software engineers and developers, data scientists, product managers, R&D professionals, IT professionals, quality assurance engineers, project managers, and business analysts.

Prerequisites for Computer Vision Training

The Computer Vision training can be taken by professionals with a basic understanding of programming and data analysis.

Assess the Training Effectiveness

Bringing you the Best Computer Vision Trainers in the Industry

The instructor-led Computer Vision Training training is conducted by certified trainers with extensive expertise in the field. Participants will benefit from the instructor's vast knowledge, gaining valuable insights and practical skills essential for success in Computer Vision practices.

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