Udemy - Project- End to End Machine Learning Web App Deploy in Cloud

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Deploy End to End Machine Learning Web App in Cloud Platform [TutsNode.com] - Deploy End to End Machine Learning Web App in Cloud Platform 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. Installing Python.srt (4.3 KB)
  • 2. 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)
  • 3. Read Image in skimage.srt (4.8 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)
  • 8. Resize Images to any shape.srt (4.7 KB)
  • 7. Histogram Equalization.srt (4.7 KB)
  • 5. Convert image into grayscale.mp4 (53.0 MB)
  • 4. Split into rgb array.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)
  • 4. Get all image filename in list in Python.srt (9.5 KB)
  • 2. What we will do .srt (2.5 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
  • 10. File Upload Backend Operations (Flask).srt (15.0 KB)
  • 11. Integrate Machine Learning Pipeline Model.srt (14.7 KB)
  • 12. Send Image from HTML to Server Side.srt (14.7 KB)
  • 15. Error Handlers 404, 405, 500.srt (13.5 KB)
  • 8. File Upload (Http Request).srt (11.3 KB)
  • 9. Styling the Page with CSS.srt (11.3 KB)
  • 5. Navigation Bar.srt (8.7 KB)
  • 2. Start Flask App.srt (7.9 KB)
  • 13. Adjust the image Height and Width Dynamically.srt (7.1 KB)
  • 3. Download Bootstrap & JQuery.srt (6.9 KB)
  • 16. About Page & href.srt (6.2 KB)
  • 7. Inheritance (Layout Page).srt (6.0 KB)
  • 14. Styling HTML for the Output.srt (4.1 KB)
  • 6. Footer.srt (3.8 KB)
  • 4. Import Bootstrap 4.srt (3.1 KB)
  • 1. Download the Resources.html (0.0 KB)
  • 15. Error Handlers 404, 405, 500.mp4 (131.7 MB)
  • 11. Integrate Machine Learning Pipeline Model.mp4 (131.3 MB)
  • 12. Send Image from HTML to Server Side.mp4 (120.1 MB)
  • 10. File Upload Backend Operations (Flask).mp4 (104.6 MB)
  • 9. Styling the Page with CSS.mp4 (64.5 MB)
  • 8. File Upload (Http Request).mp4 (64.5 MB)
  • 13. Adjust the image Height and Width Dynamically.mp4 (61.5 MB)
  • 16. About Page & href.mp4 (55.6 MB)
  • 5. Navigation Bar.mp4 (52.2 MB)
  • 2. Start Flask App.mp4 (43.5 MB)
  • 3. Download Bootstrap & JQuery.mp4 (38.4 MB)
  • 14. Styling HTML for the Output.mp4 (31.5 MB)
  • 7. Inheritance (Layout Page).mp4 (27.6 MB)
  • 6. Footer.mp4 (19.9 MB)
  • 4. Import Bootstrap 4.mp4 (19.1 MB)
  • 1.1 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.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)
  • 2. Preparing Requirements.mp4 (44.9 MB)
  • 6. Common Error you will get while deploying.mp4 (43.0 MB)
  • 3. Upload Flask App in Python Anywhere.mp4 (29.5 MB)

Description


Description

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

Image Processing and 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 with programming in SKIMAGE which is the essential skill required and then we will do the necessary preprocessing techniques and feature extraction with an image.

Then throughout the course, we will work on the project, providing you with complete training. We will use the powerful functionality built into skimage, sklearn, flask as well as other fundamental libraries such as NumPy, matplotlib, statsmodels.

After that, we will develop the website in Flask and deploy the entire website in Python Anywhere.

With these tools we will master the most widely used models out there:

– Python

– Skimage

– 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 Image Classification Flask Web App is one of those topics that always leaves some doubts.

Until now.

This course is exactly what you need to comprehend once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes – everything is included.
Who this course is for:

Anyone who want deploy machine learning web app from strach

Requirements

Basic Python Programming
Understanding HTML, CSS, JS

Last Updated 3/2021



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Udemy - Project- End to End Machine Learning Web App Deploy in Cloud

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Download torrent
3 GB
seeders:13
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Udemy - Project- End to End Machine Learning Web App Deploy in Cloud

Torrent hash: 8EA44A3E9327C344AE038D4E7290DA0CBF31EFE2