DataVisionary: Bridging the Realm of Data Science and Computer Vision

360h

Month 1: Foundations of Data Science and Computer Vision

Introduction to Data Science and Computer Vision
00:00

Data Exploration and Visualization
00:00

Image Formation and Representation
00:00

Introduction to Image Processing Techniques
00:00

Introduction to Machine Learning for Computer Vision
00:00

Month 2: Image Feature Extraction and Representation

Month 3: Object Detection and Recognition

Month 4: Image Classification and Deep Learning Models

Month 5: Image and Video Analysis

Month 6: Advanced Topics and Applications in Computer Vision

4.44 /5

Anurag Gupta

78 Courses

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Artificial Intelligence

Computer Vision

Data Science

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Intensive Professional Training

Machine Learning

Microsoft 365

Programming Languages

Short Term Specialised Training

Thesis Writing

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Select Your Favourite Category And Start Learning.

Artificial Intelligence

Computer Vision

Data Science

Fast Track training Course

Fundamental Training Programs

Intensive Professional Training

Machine Learning

Microsoft 365

Programming Languages

Short Term Specialised Training

Thesis Writing

DataVisionary: Bridging the Realm of Data Science and Computer Vision

Intermediate

360h

29

Course content

Month 1: Foundations of Data Science and Computer Vision

Introduction to Data Science and Computer Vision00:00

Data Exploration and Visualization00:00

Image Formation and Representation00:00

Introduction to Image Processing Techniques00:00

Introduction to Machine Learning for Computer Vision00:00

Month 2: Image Feature Extraction and Representation

Feature Extraction Techniques (SIFT, SURF, HOG)00:00

Feature Descriptors and Matching00:00

Image Segmentation Methods00:00

Deep Learning for Feature Extraction (CNN-based approaches)00:00

Transfer Learning for Computer Vision Tasks00:00

Month 3: Object Detection and Recognition

Object Detection Techniques (Haar cascades, R-CNN, SSD, YOLO)00:00

Region-based Convolutional Neural Networks (R-CNN, Fast R-CNN, Faster R-CNN)00:00

Single Shot MultiBox Detector (SSD)00:00

You Only Look Once (YOLO)00:00

Advanced Object Recognition and Tracking00:00

Month 4: Image Classification and Deep Learning Models

Convolutional Neural Networks (CNNs) for Image Classification00:00

Architectures (LeNet, AlexNet, VGG, ResNet, Inception, etc.)00:00

Fine-tuning and Transfer Learning for Image Classification00:00

Handling Imbalanced Datasets in Computer Vision00:00

Advanced Deep Learning Models for Computer Vision00:00

Month 5: Image and Video Analysis

Image Segmentation and Semantic Segmentation00:00

Image and Video Captioning00:00

Image Super-Resolution and Style Transfer00:00

Video Analysis and Activity Recognition00:00

Month 6: Advanced Topics and Applications in Computer Vision

Generative Adversarial Networks (GANs) for Computer Vision00:00

3D Computer Vision and Depth Estimation00:00

Multimodal Deep Learning for Vision and Language00:00

Ethical Considerations and Bias in Computer Vision00:00

Emerging Trends and Future Directions in Computer Vision00:00

Material Includes

Instructor

Anurag Gupta

Student Ratings & Reviews

Material Includes

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Introduction to Data Science and Computer Vision
00:00

Data Exploration and Visualization
00:00

Image Formation and Representation
00:00

Introduction to Image Processing Techniques
00:00

Introduction to Machine Learning for Computer Vision
00:00

Feature Extraction Techniques (SIFT, SURF, HOG)
00:00

Feature Descriptors and Matching
00:00

Image Segmentation Methods
00:00

Deep Learning for Feature Extraction (CNN-based approaches)
00:00

Transfer Learning for Computer Vision Tasks
00:00

Object Detection Techniques (Haar cascades, R-CNN, SSD, YOLO)
00:00

Region-based Convolutional Neural Networks (R-CNN, Fast R-CNN, Faster R-CNN)
00:00

Single Shot MultiBox Detector (SSD)
00:00

You Only Look Once (YOLO)
00:00

Advanced Object Recognition and Tracking
00:00

Convolutional Neural Networks (CNNs) for Image Classification
00:00

Architectures (LeNet, AlexNet, VGG, ResNet, Inception, etc.)
00:00

Fine-tuning and Transfer Learning for Image Classification
00:00

Handling Imbalanced Datasets in Computer Vision
00:00

Advanced Deep Learning Models for Computer Vision
00:00

Image Segmentation and Semantic Segmentation
00:00

Image and Video Captioning
00:00

Image Super-Resolution and Style Transfer
00:00

Video Analysis and Activity Recognition
00:00

Generative Adversarial Networks (GANs) for Computer Vision
00:00

3D Computer Vision and Depth Estimation
00:00

Multimodal Deep Learning for Vision and Language
00:00

Ethical Considerations and Bias in Computer Vision
00:00

Emerging Trends and Future Directions in Computer Vision
00:00