Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
3 Hour(s) 25 Minute(s)
Language
English
Taught by
Vineeta Vashistha
Rating
Course Overview
Quick Starter on Object Detection and Image Classification on Videos using Deep Learning, OpenCV, YOLO and CNN Models
Master Real-Time Object Detection with Deep Learning
Dive into the world of computer vision and learn to build intelligent video analytics systems. This comprehensive course covers everything from foundational concepts to advanced techniques, including:
Video Analytics Basics: Understand the 3-step process of capturing, processing, and saving video data.
Object Detection Powerhouse: Explore state-of-the-art object detection models like Haar Cascade, HOG, Faster RCNN, R-FCN, SSD, and YOLO.
Real-World Applications: Implement practical projects like people footfall tracking, automatic parking management, and real-time license plate recognition.
Deep Learning Mastery: Learn to train and deploy deep learning models for image classification and object detection using frameworks like TensorFlow and Keras.
Hands-On Experience: Benefit from line-by-line code walkthroughs and dedicated support to ensure a smooth learning journey.
Exciting News!
We've just added two new, hands-on projects to help you master real-world computer vision applications:
Real-Time License Plate Recognition System Using YOLOv3: Dive deep into real-time object detection and recognition.
Training a YOLOv3 Model for Real-Time License Plate Recognition: Learn to customize and train your own YOLOv3 model. Don't miss this opportunity to level up your skills!
Why Enroll?
Industry-Relevant Skills: Gain in-demand skills to advance your career in AI and machine learning.
Practical Projects: Build a strong portfolio with real-world applications.
Expert Guidance: Learn from experienced instructors and get personalized support.
Flexible Learning: Access course materials and assignments at your own pace.
Unlock the power of computer vision and start building intelligent systems today!
Course Content
- 10 section(s)
- 94 lecture(s)
- Section 1 Course Starter
- Section 2 Introduction to Video Architecture and Use Cases
- Section 3 Video Analytics and Processing with Codec
- Section 4 Object Detection - Human Detection with Euclidean Distance
- Section 5 Object Detection Models - Haar Cascade, HOG, Faster RCNN, R-FCN, SSD, YOLO
- Section 6 Object Detection Implementation on Videos using Haar Cascade, HOG and YOLO
- Section 7 Training Image Classification Model using Deep Learning on Google Colab
- Section 8 Image Classification Implementation on Videos using Trained Inception V3 Model
- Section 9 Object Tracking using SORT Framework
- Section 10 Project - Real-Time License Plate Recognition System Using YOLOv3
What You’ll Learn
- Learn how to implement Video Analytics using Deep Learning concepts
- Understand how to implement Object Detection Models on Videos using Python
- Build your own Deep Learning model using Transfer Learning for Image Classification
- Executable Code of Faster RCNN, YOLO, HOG and Haar Cascade for Object Detection
- Build a technical solution containing both Object Detection and Image Classification
- Develop Image Classification Model using InceptionV3 model architecture
- Learn to implement SORT Framework for Object Tracking
- Executable Code of SORT for People Footfall Tracking and Automatic Parking Management
Skills covered in this course
Reviews
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PParker
This course is absolutely amazing ! It offers a perfect balance of theory and hands-on projects making complex concepts easy to understand. The line-by-line code walkthroughs and responsive support made the learning process smooth and enjoyable. Highly recommend it for anyone looking to dive into computer vision and build industry-relevant skills !
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JJavier Baltierrez Castillo
Los conceptos se explican de forma didáctica y fácil de entender
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SSachit Desai
I believe that this course is indispensable for developers in the machine learning domain, thanks to its meticulous approach, comprehensive coverage, and practical projects. Also, I particularly commend the dedication to providing timely support within 24 hours for any issues encountered during the course. The line-by-line code walkthroughs have been instrumental in grasping the intricacies of object detection implementation on videos and training models for image classification.
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UUmang
It's a wonderful course if you want to learn how to work on videos using Deep Learning. The instructor is quite detailed in explaining the concept and code works smoothly. Overall a good experience with course !
