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The Python Computer Vision Professional program is designed to provide participants with comprehensive skills and knowledge in developing and deploying computer vision applications using Python. This course covers fundamental concepts and advanced techniques in computer vision, enabling professionals to build robust applications for various real-world scenarios.
Join the Python Computer Vision Professional program and become proficient in creating cutting-edge computer vision solutions!
Course Overview
The Python Computer Vision Professional program is designed to provide participants with comprehensive skills and knowledge in developing and deploying computer vision applications using Python. This course covers fundamental concepts and advanced techniques in computer vision, enabling professionals to build robust applications for various real-world scenarios.
Program Objectives
By the end of this program, participants will be able to:
- Understand the principles and fundamentals of computer vision.
- Work with popular Python libraries and frameworks for computer vision.
- Develop and deploy computer vision models for image and video analysis.
- Implement object detection, image classification, pose detection, and segmentation.
- Utilize deep learning techniques for advanced computer vision tasks.
- Apply computer vision solutions to real-world problems in various industries.
- Optimize and evaluate computer vision models for performance and accuracy.
Target Audience
This program is ideal for:
- Data scientists and machine learning engineers seeking to specialize in computer vision.
- Software developers looking to integrate computer vision capabilities into their applications.
- Researchers and academics interested in the latest advancements in computer vision.
- Students and recent graduates aiming to enter the field of computer vision and AI.
Course Profile: Data Wrangling Professional Program
Prerequisites
Participants should have:
- Basic knowledge of Python programming.
- Understanding of fundamental machine learning concepts.
- Familiarity with basic data manipulation and analysis using libraries such as NumPy and Pandas.
Course Modules
Module 1: Introduction to Computer Vision
- Overview of computer vision and its applications
- Basic image processing techniques
- Understanding image data and formats
Module 2: Working with OpenCV
- Introduction to OpenCV library
- Image processing with OpenCV: filtering, transformations, and edge detection
- Handling video streams and camera input
Module 3: Image Classification with Convolutional Neural Networks (CNNs)
- Fundamentals of CNNs
- Building image classification models with TensorFlow, Keras, and FastAi
- Data augmentation techniques
- Fine-tuning pre-trained models
Module 4: Object Detection and Segmentation
- Introduction to object detection algorithms: YOLO, SSD, and Faster R-CNN
- Implementing object detection models using TensorFlow, FastAi, and OpenCV
- Image segmentation techniques
- Semantic and instance segmentation
Module 5: Pose Detection and Image Recognition
- Fundamentals of pose detection
- Implementing pose detection models with OpenCV and TensorFlow
- Image recognition techniques
- Building and training image recognition models with FastAi and Keras
Module 6: Advanced Computer Vision with Deep Learning
- Understanding transfer learning and its applications
- Building and training custom deep learning models
- Implementing Generative Adversarial Networks (GANs) for image generation
- Real-time computer vision applications
Module 7: Specialized Computer Vision Applications
- Face detection and recognition
- Optical character recognition (OCR)
- Gesture and motion detection
- Medical imaging and analysis
Module 8: Model Optimization and Deployment
- Techniques for model optimization and acceleration
- Evaluating model performance and accuracy
- Deploying computer vision models on edge devices
- Creating REST APIs for computer vision models
Module 9: Hands-On Projects and Applications
- Real-world computer vision projects
- Hands-on assignments and exercises
- Integrating computer vision solutions into applications
- Capstone project: end-to-end computer vision application
Tools and Technologies
- Python: OpenCV, TensorFlow, Keras, PyTorch, FastAi, NumPy, Pandas
- Jupyter Notebooks: For interactive coding and model development
- OpenCV: For image and video processing
- TensorFlow/Keras: For building and training deep learning models
- PyTorch/FastAi: For advanced deep learning tasks
- Flask/FastAPI: For deploying models as web services
Evaluation and Certification
Participants will be assessed through:
- Quizzes and assignments for each module
- A capstone project demonstrating their computer vision skills
- Final exam
Upon successful completion, participants will receive a "Python Computer Vision Professional" certificate, recognizing their expertise in developing and deploying computer vision applications using Python.
Course Duration
The program is designed to be completed over 10 weeks, with a combination of online lectures, hands-on exercises, and project work.
Enrollment
If the 'Apply for Course' button is active you may enroll apply for enrollment to this course now. For enrollment details and course schedules, please visit our website or contact our admissions office.
How To Apply- Application Steps
Follow the steps in the sequence listed below to apply
- STEP1:Â Click on the APPLY FOR COURSE button on the Course page. This action reserves a seat on the program for you
- STEP2: In the Course details page, click on the Announcements & News tab. Follow the link in the tab content to complete and submit your Cohort Enrollment Application
- Step3: Once your Program Seat Reservation and Cohort Enrollment Application are recieved, your application will be moved into the processing queu.
- STEP4: If your application is approved, you will recieve a notification on your portal as well as an email
Contact Information
- Email: admissions@onecampusacademy.com
- Phone: +1 (475) 209-1037
- Website: learn.onecampusacademy.com
Course Currilcum
Course Instructors
