Python/Django App- Create & Deploy a Computer Vision Model
Full Stack Computer Vision web app using python and Django, Transfer Learning, CNN, Keras, html, CSS, JavaScript, Ajax.
Ratings 4.22 / 5.00
What You Will Learn!
- Creating a full stack computer vision model using Transfer Learning in Python. The course will include details on how to create a computer vision model in python, and how to host it on server using Django.
- How to save and deploy any python ML/DL model you have created using Django.
- How to deploy a model in Production, Client Side(html, CSS) and Server side(Python) programming. All open source and free to use technologies.
- Learn Django and Integrating a python code with the Django Framework.
- How to create a user interface(UI) for your python code or ML/DL model that can take input from user, pass the input to your ML/DL model and renders back the results to UI.
- How to utilize transfer learning for feature extraction thus helping train new models without the need of a powerful GPU.
- Re-usability : how to quickly retrain the model that you create on new set of images.
- How to create an end to end computer vision project.
Description
This Course has been designed for the developers who are able to train ML/DL models, but they struggle when it comes to saving the model for future use or when it comes to deploying the model through a full stack portal.
This course will teach you how to train and create computer vision model from scratch, how to utilize transfer learning for feature extraction, how to save those models using pickle, and how to deploy the models using Django framework.
Who Should Attend!
- One who wants to create full stack portal with client side(html, css, javascript) and server side(Python) functionality.
- One who wants to save his trained ML/DL model in python for future predictions.
- One who knows how to create a ML/DL model in python but don't know how to deploy it.
- One who wants to host his model as Web Server.
- Students who want to create a project. The models can be retrained on new set image really quickly and projects like KYC or any other image classification projects can be created end to end.
- One who wants to code practical implementation using open source libraries like tensorflow and Keras.
