Deploy Machine Learning Image processing Flask App in Cloud

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Complete Image Processing MACHINE LEANRING WEBSITE in CLOUD [TutsNode.com] - Complete Image Processing MACHINE LEANRING WEBSITE in CLOUD 6. Make Pipeline
  • 2. Make pipeline - Get the Prediction.mp4 (140.7 MB)
  • 2. Make pipeline - Get the Prediction.srt (19.4 KB)
  • 1. Train Model and Save in pickle.srt (17.6 KB)
  • 3. Make pipeline - Decision Function.srt (15.9 KB)
  • 4. Make pipeline - pipeline model.srt (7.7 KB)
  • 1. Train Model and Save in pickle.mp4 (122.1 MB)
  • 3. Make pipeline - Decision Function.mp4 (99.6 MB)
  • 4. Make pipeline - pipeline model.mp4 (58.6 MB)
1. Introduction
  • 1. Introduction.srt (3.0 KB)
  • 2. What you are going to develop in this course.html (0.3 KB)
  • 3. Installing Python.srt (4.3 KB)
  • 3. Installing Python.mp4 (33.9 MB)
  • 1. Introduction.mp4 (31.5 MB)
4. Machine Learning
  • 3. HOG Feature Extraction.srt (16.9 KB)
  • 5. HOG Transformer.srt (16.4 KB)
  • 6. Train SGD classifier.srt (16.1 KB)
  • 4. RGB to Gray Transformer.srt (9.7 KB)
  • 1. Import Python libraries and Installations.srt (5.6 KB)
  • 2. Load the Data and split into train and test set.srt (5.5 KB)
  • 7. Model Evalution.srt (5.2 KB)
  • 3. HOG Feature Extraction.mp4 (133.2 MB)
  • 5. HOG Transformer.mp4 (104.2 MB)
  • 6. Train SGD classifier.mp4 (93.8 MB)
  • 4. RGB to Gray Transformer.mp4 (54.4 MB)
  • 7. Model Evalution.mp4 (38.4 MB)
  • 2. Load the Data and split into train and test set.mp4 (33.7 MB)
  • 1. Import Python libraries and Installations.mp4 (30.3 MB)
2. Skimage
  • 1. Download the Resources.html (0.0 KB)
  • 4. Split into rgb array.srt (8.6 KB)
  • 5. Convert image into grayscale.srt (8.6 KB)
  • 6. Image Histogram.srt (6.3 KB)
  • 2. What is Image & Pixels.srt (5.7 KB)
  • 3. Read Image in skimage.srt (4.8 KB)
  • 8. Resize Images to any shape.srt (4.7 KB)
  • 7. Histogram Equalization.srt (4.7 KB)
  • 4. Split into rgb array.mp4 (53.0 MB)
  • 5. Convert image into grayscale.mp4 (53.0 MB)
  • 6. Image Histogram.mp4 (44.0 MB)
  • 7. Histogram Equalization.mp4 (32.6 MB)
  • 3. Read Image in skimage.mp4 (28.2 MB)
  • 8. Resize Images to any shape.mp4 (27.4 MB)
  • 2. What is Image & Pixels.mp4 (20.2 MB)
  • 1.1 skimage.zip (1.2 MB)
3. Image Data Preparation
  • 7. Visualize all images and labels.srt (15.3 KB)
  • 5. Labeling Images.srt (10.5 KB)
  • 6. Read all images from the folders and save in Pickle.srt (9.9 KB)
  • 1. Download the Resources.html (0.1 KB)
  • 2. What we will do .srt (2.5 KB)
  • 4. Get all image filename in list in Python.srt (9.5 KB)
  • 3. Understand the data what we have.srt (2.8 KB)
  • 7. Visualize all images and labels.mp4 (85.8 MB)
  • 5. Labeling Images.mp4 (76.0 MB)
  • 4. Get all image filename in list in Python.mp4 (57.1 MB)
  • 6. Read all images from the folders and save in Pickle.mp4 (50.6 MB)
  • 1.1 dataprepare_machinelearning_pipeline.zip (43.8 MB)
  • 3. Understand the data what we have.mp4 (25.3 MB)
  • 2. What we will do .mp4 (12.8 MB)
7. Image Classification Web App in Flask
  • 11. File Upload Backend Operations (Flask).srt (15.0 KB)
  • 12. Integrate Machine Learning Pipeline Model.srt (14.7 KB)
  • 13. Send Image from HTML to Server Side.srt (14.7 KB)
  • 16. Error Handlers 404, 405, 500.srt (13.5 KB)
  • 9. File Upload (Http Request).srt (11.3 KB)
  • 10. Styling the Page with CSS.srt (11.3 KB)
  • 15. Styling HTML for the Output.srt (4.1 KB)
  • 2. Download the Resources.html (0.0 KB)
  • 6. Navigation Bar.srt (8.7 KB)
  • 3. Start Flask App.srt (7.9 KB)
  • 14. Adjust the image Height and Width Dynamically.srt (7.1 KB)
  • 4. Download Bootstrap & JQuery.srt (6.9 KB)
  • 17. About Page & href.srt (6.2 KB)
  • 8. Inheritance (Layout Page).srt (6.0 KB)
  • 1. Install Visual Studio Code.srt (4.6 KB)
  • 7. Footer.srt (3.8 KB)
  • 5. Import Bootstrap 4.srt (3.1 KB)
  • 16. Error Handlers 404, 405, 500.mp4 (131.7 MB)
  • 12. Integrate Machine Learning Pipeline Model.mp4 (131.3 MB)
  • 13. Send Image from HTML to Server Side.mp4 (120.1 MB)
  • 11. File Upload Backend Operations (Flask).mp4 (104.6 MB)
  • 10. Styling the Page with CSS.mp4 (64.5 MB)
  • 9. File Upload (Http Request).mp4 (64.5 MB)
  • 14. Adjust the image Height and Width Dynamically.mp4 (61.5 MB)
  • 17. About Page & href.mp4 (55.6 MB)
  • 6. Navigation Bar.mp4 (52.2 MB)
  • 3. Start Flask App.mp4 (43.5 MB)
  • 1. Install Visual Studio Code.mp4 (38.8 MB)
  • 4. Download Bootstrap & JQuery.mp4 (38.4 MB)
  • 15. Styling HTML for the Output.mp4 (31.5 MB)
  • 8. Inheritance (Layout Page).mp4 (27.6 MB)
  • 7. Footer.mp4 (19.9 MB)
  • 5. Import Bootstrap 4.mp4 (19.1 MB)
  • 2.1 flask_app.zip (1.7 MB)
5. Grid Search for Best Hyper parameters
  • 2. Grid Search for Parameter Tuning.srt (14.7 KB)
  • 1. Pipeline Model.srt (8.0 KB)
  • 3. Best Estimator.srt (7.0 KB)
  • 2. Grid Search for Parameter Tuning.mp4 (91.1 MB)
  • 1. Pipeline Model.mp4 (49.9 MB)
  • 3. Best Estimator.mp4 (42.0 MB)
8. Deploy Flask in Python Anywhere
  • 5. Deploy you Flask App and get access anywhere from the World.srt (10.0 KB)
  • 1. Create Account in Python Anywhere for Free.srt (6.6 KB)
  • 2. Preparing Requirements.srt (6.1 KB)
  • 6. Common Error you will get while deploying the webapp.srt (4.8 KB)
  • 3. Upload Flask App in Python Anywhere.srt (4.6 KB)
  • 4. Installing Requirements.srt (1.4 KB)
  • 5. Deploy you Flask App and get access anywhere from the World.mp4 (80.6 MB)
  • 1. Create Account in Python Anywhere for Free.mp4 (51.6 MB)

Description


Description

Welcome to Deploy End to End Machine Learning-based Image Classification Web App in Cloud Platform from scratch

Image Processing & classification is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course covers modeling techniques for data preprocessing, model building, evaluation, tuning, and production

We start the course by learning Scikit Image for image processing which is the essential skill required and then we will do the necessary preprocessing techniques & feature extraction to an image like HOG.

After that we will start building the project. In this course you will learn how to label the images, image data preprocessing and analysis using scikit image and python.

Then we will train machine learning here we will see Stochastic Gradient Descenct Classifier for image classification and followed by model evaluation proces and pipeline the machine learning model.

After that we will create web app in Flask by rendering HTML, CSS, Boostrap. Then, we finally deploy web app in Python Anywhere which is cloud platform.

WHAT YOU LEARN ?

Python
Scikit Image
Data Preprocessing
HOG
Base Estimator and TransformerMixIn
SGD Classifier
Create and Make Pipeline Model
Hyperparameter Tuning
Flask
HTTP methods
Deploy in PythonAnywhere

We know that the Image Classification Flask Web App is one of those topics that always leaves some doubts. Feel free to ask question in Q&A, we are happy to answer you question.

I am super excited and see you in the course !!!
Who this course is for:

Anyone who want deploy machine learning web app from scratch
Anyone who want deploy image classification web app from end to end

Requirements

Basic Python Programming
Understanding HTML, CSS, JS

Last Updated 4/2021



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Deploy Machine Learning Image processing Flask App in Cloud

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3 GB
seeders:14
leechers:13
Deploy Machine Learning Image processing Flask App in Cloud

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