PythonDjango App- Create Deploy a Computer Vision Model

Python/Django App- Create & Deploy a Computer Vision Model, available at $54.99, has an average rating of 4.25, with 43 lectures, based on 74 reviews, and has 454 subscribers.

You will learn about 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. This course is ideal for individuals who are One who wants to create full stack portal with client side(html, css, javascript) and server side(Python) functionality. or One who wants to save his trained ML/DL model in python for future predictions. or One who knows how to create a ML/DL model in python but dont know how to deploy it. or One who wants to host his model as Web Server. or 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. or One who wants to code practical implementation using open source libraries like tensorflow and Keras. It is particularly useful for One who wants to create full stack portal with client side(html, css, javascript) and server side(Python) functionality. or One who wants to save his trained ML/DL model in python for future predictions. or One who knows how to create a ML/DL model in python but dont know how to deploy it. or One who wants to host his model as Web Server. or 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. or One who wants to code practical implementation using open source libraries like tensorflow and Keras.

Enroll now: Python/Django App- Create & Deploy a Computer Vision Model

Summary

Title: Python/Django App- Create & Deploy a Computer Vision Model

Price: $54.99

Average Rating: 4.25

Number of Lectures: 43

Number of Published Lectures: 43

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: $89.99

Quality Status: approved

Status: Live

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.

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 dont 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.

Target Audiences

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 dont 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.

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.

Course Curriculum

Chapter 1: Course Overview

Lecture 1: Course Structure and Contents

Lecture 2: Proof Of Concept – Car Damage Detection

Lecture 3: POC 2.0 – Single page portal without refresh using AJAX

Lecture 4: POC 3.0 – Integrating KYC functionality to the portal

Lecture 5: Upgrading to the latest Django Version

Lecture 6: Installation – Anaconda, Django and Atom

Lecture 7: Anaconda Prompt Basics

Lecture 8: Working with Jupyter Notebook

Chapter 2: Project – Car Damage Detection (Computer Vision Model)

Lecture 1: Project Overview

Lecture 2: Convolutional Neural Network (CNN) Concept – Part 1

Lecture 3: Convolutional Neural Network (CNN) Concept – Part 2

Lecture 4: VGG16 Architecture and Transfer Learning

Lecture 5: First Check – Car or not (Part 1)

Lecture 6: First Check – Car or not (Part 2)

Lecture 7: Second Check – Car damaged or not (Part 1)

Lecture 8: Second Check – Car damaged or not (Part 2)

Lecture 9: Second Check – Car damaged or not (Part 3)

Lecture 10: Third Check – Location of Damage (Part 1)

Lecture 11: Third Check – Location of Damage (Part 2)

Lecture 12: Third Check – Location of Damage (Part 3)

Lecture 13: Fourth Check – Severity of Damage (Part 1)

Lecture 14: Fourth Check – Severity of Damage (Part 2)

Lecture 15: Fourth Check – Severity of Damage (Part 3)

Lecture 16: Integration – Combining all the Checks

Chapter 3: Django – Creating Full Stack Portal

Lecture 1: Full Stack Architecture

Lecture 2: Creating Project and App in Django

Lecture 3: Creating the home Page of the portal – Part 1

Lecture 4: Creating the home Page of the portal – Part 2

Lecture 5: Creating the Second page of the portal – Part 1

Lecture 6: Creating the second page of the portal – part 2

Lecture 7: Creating the second Page of the portal – Part 3

Lecture 8: Creating the second Page of the portal – Part 4

Chapter 4: Full Stack POC – Combining Client and Server Side

Lecture 1: Integration – Combining Client and Server Side – Part 1

Lecture 2: Integration – Combining Client and Server Side – Part 2

Chapter 5: Deployment – Hosting your Django powered portal on world wide web

Lecture 1: Understanding Github, Git and Pythonanywhere

Lecture 2: Creating a sample django project to host on pythonanywhere

Lecture 3: Hosting your Django project on World Wide Web

Chapter 6: POC 2.0 – Code updates using AJAX

Lecture 1: Coding the portal AJAX way

Chapter 7: POC 3.0 – Integrating KYC web app to the portal

Lecture 1: Retrain the model on new set of images – feature extraction

Lecture 2: Retrain the model on new set of images – create and save the classifier

Lecture 3: Retrain the model on new set of images – make predictions

Lecture 4: Integrate KYC in the existing portal

Chapter 8: POC Upgrade to Django 3.2.2

Lecture 1: POC upgrade to Django version 3.2.2

Instructors

Ashar Siddiqui
Solution Architect using Advance Digital Technologies

Rating Distribution

1 stars: 4 votes

2 stars: 3 votes

3 stars: 6 votes

4 stars: 25 votes

5 stars: 36 votes

Frequently Asked Questions

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