| 0 | 843.3 KB | ||
| 1. Bonus Lecture Next Steps.html | 716.8 B | ||
| 1. Create Account in Python Anywhere for Free.mp4 | 51.6 MB | ||
| 1. Create Account in Python Anywhere for Free.srt | 6.6 KB | ||
| 1. Download the Resources.html | 102.4 B | ||
| 1. Import Python libraries and Installations.mp4 | 30.3 MB | ||
| 1. Import Python libraries and Installations.srt | 5.6 KB | ||
| 1. Install Visual Studio Code.mp4 | 38.8 MB | ||
| 1. Install Visual Studio Code.srt | 4.6 KB | ||
| 1. Introduction.mp4 | 31.5 MB | ||
| 1. Introduction.srt | 3 KB | ||
| 1. Pipeline Model.mp4 | 49.9 MB | ||
| 1. Pipeline Model.srt | 8 KB | ||
| 1. Train Model and Save in pickle.srt | 17.6 KB | ||
| 1 | 828.5 KB | ||
| 1. Train Model and Save in pickle.mp4 | 122.1 MB | ||
| 1.1 dataprepare_machinelearning_pipeline.zip | 43.8 MB | ||
| 1.1 skimage.zip | 1.2 MB | ||
| 2 | 298.4 KB | ||
| 10. Styling the Page with CSS.mp4 | 64.5 MB | ||
| 10. Styling the Page with CSS.srt | 11.3 KB | ||
| 11. File Upload Backend Operations (Flask).mp4 | 104.6 MB | ||
| 11. File Upload Backend Operations (Flask).srt | 15 KB | ||
| 12. Integrate Machine Learning Pipeline Model.mp4 | 131.3 MB | ||
| 12. Integrate Machine Learning Pipeline Model.srt | 14.7 KB | ||
| 13. Send Image from HTML to Server Side.mp4 | 120.1 MB | ||
| 13. Send Image from HTML to Server Side.srt | 14.7 KB | ||
| 14. Adjust the image Height and Width Dynamically.mp4 | 61.5 MB | ||
| 14. Adjust the image Height and Width Dynamically.srt | 7.1 KB | ||
| 15. Styling HTML for the Output.mp4 | 31.5 MB | ||
| 15. Styling HTML for the Output.srt | 4.1 KB | ||
| 16. Error Handlers 404, 405, 500.mp4 | 131.7 MB | ||
| 16. Error Handlers 404, 405, 500.srt | 13.5 KB | ||
| 17. About Page & href.mp4 | 55.6 MB | ||
| 17. About Page & href.srt | 6.2 KB | ||
| 2. Download the Resources.html | 0 B | ||
| 2. Grid Search for Parameter Tuning.mp4 | 91.1 MB | ||
| 2. Grid Search for Parameter Tuning.srt | 14.7 KB | ||
| 2. Load the Data and split into train and test set.mp4 | 33.7 MB | ||
| 2. Load the Data and split into train and test set.srt | 5.5 KB | ||
| 2. Make pipeline - Get the Prediction.mp4 | 140.7 MB | ||
| 2. Make pipeline - Get the Prediction.srt | 19.4 KB | ||
| 2. Preparing Requirements.mp4 | 44.9 MB | ||
| 2. Preparing Requirements.srt | 6.1 KB | ||
| 2. What is Image & Pixels.mp4 | 20.2 MB | ||
| 2. What is Image & Pixels.srt | 5.7 KB | ||
| 2. What we will do .mp4 | 12.8 MB | ||
| 2. What we will do .srt | 2.5 KB | ||
| 2. What you are going to develop in this course.html | 307.2 B | ||
| 2.1 flask_app.zip | 1.7 MB | ||
| 3. Best Estimator.mp4 | 42 MB | ||
| 3. Best Estimator.srt | 7 KB | ||
| 3. HOG Feature Extraction.mp4 | 133.2 MB | ||
| 3. HOG Feature Extraction.srt | 16.9 KB | ||
| 3. Installing Python.mp4 | 33.9 MB | ||
| 3. Installing Python.srt | 4.3 KB | ||
| 3. Make pipeline - Decision Function.mp4 | 99.6 MB | ||
| 3. Make pipeline - Decision Function.srt | 15.9 KB | ||
| 3. Read Image in skimage.mp4 | 28.2 MB | ||
| 3. Read Image in skimage.srt | 4.8 KB | ||
| 3. Start Flask App.mp4 | 43.5 MB | ||
| 3. Understand the data what we have.mp4 | 25.3 MB | ||
| 3. Understand the data what we have.srt | 2.8 KB | ||
| 3. Upload Flask App in Python Anywhere.mp4 | 29.5 MB | ||
| 3 | 721.1 KB | ||
| 3. Start Flask App.srt | 7.9 KB | ||
| 3. Upload Flask App in Python Anywhere.srt | 4.6 KB | ||
| 4. Download Bootstrap & JQuery.mp4 | 38.4 MB | ||
| 4. Download Bootstrap & JQuery.srt | 6.9 KB | ||
| 4. Get all image filename in list in Python.mp4 | 57.1 MB | ||
| 4. Get all image filename in list in Python.srt | 9.5 KB | ||
| 4. Installing Requirements.mp4 | 21.7 MB | ||
| 4. Make pipeline - pipeline model.mp4 | 58.6 MB | ||
| 4. Make pipeline - pipeline model.srt | 7.7 KB | ||
| 4. RGB to Gray Transformer.mp4 | 54.4 MB | ||
| 4. RGB to Gray Transformer.srt | 9.7 KB | ||
| 4. Split into rgb array.mp4 | 53 MB | ||
| 4 | 943.3 KB | ||
| 4. Installing Requirements.srt | 1.4 KB | ||
| 4. Split into rgb array.srt | 8.6 KB | ||
| 5. Convert image into grayscale.mp4 | 53 MB | ||
| 5. Convert image into grayscale.srt | 8.6 KB | ||
| 5 | 955.4 KB | ||
| 5. Deploy you Flask App and get access anywhere from the World.mp4 | 80.6 MB | ||
| 5. Deploy you Flask App and get access anywhere from the World.srt | 10 KB | ||
| 5. HOG Transformer.mp4 | 104.2 MB | ||
| 5. HOG Transformer.srt | 16.4 KB | ||
| 5. Import Bootstrap 4.mp4 | 19.1 MB | ||
| 5. Import Bootstrap 4.srt | 3.1 KB | ||
| 5. Labeling Images.mp4 | 76 MB | ||
| 5. Labeling Images.srt | 10.5 KB | ||
| 6. Common Error you will get while deploying the webapp.mp4 | 43 MB | ||
| 6. Common Error you will get while deploying the webapp.srt | 4.8 KB | ||
| 6. Navigation Bar.mp4 | 52.2 MB | ||
| 6. Train SGD classifier.mp4 | 93.8 MB | ||
| 6 | 427.2 KB | ||
| 6. Image Histogram.mp4 | 44 MB | ||
| 6. Image Histogram.srt | 6.3 KB | ||
| 6. Navigation Bar.srt | 8.7 KB | ||
| 6. Read all images from the folders and save in Pickle.mp4 | 50.6 MB | ||
| 6. Read all images from the folders and save in Pickle.srt | 9.9 KB | ||
| 6. Train SGD classifier.srt | 16.1 KB | ||
| 7 | 786.8 KB | ||
| 7. Footer.mp4 | 19.9 MB | ||
| 7. Footer.srt | 3.8 KB | ||
| 7. Histogram Equalization.mp4 | 32.6 MB | ||
| 7. Histogram Equalization.srt | 4.7 KB | ||
| 7. Model Evalution.mp4 | 38.4 MB | ||
| 7. Model Evalution.srt | 5.2 KB | ||
| 7. Visualize all images and labels.mp4 | 85.8 MB | ||
| 7. Visualize all images and labels.srt | 15.3 KB | ||
| 8. Inheritance (Layout Page).srt | 6 KB | ||
| 8. Resize Images to any shape.mp4 | 27.4 MB | ||
| 8 | 358.4 KB | ||
| 8. Inheritance (Layout Page).mp4 | 27.6 MB | ||
| 8. Resize Images to any shape.srt | 4.7 KB | ||
| 9 | 193.6 KB | ||
| 9. File Upload (Http Request).mp4 | 64.5 MB | ||
| 9. File Upload (Http Request).srt | 11.3 KB | ||
| TutsNode.com.txt | 102.4 B | ||
| [TGx]Downloaded from torrentgalaxy.to .txt | 614.4 B | ||
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| ▲ 161 total files | |||
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
| torrent name | size | uploader | age | seed | leech |
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Udemy - Machine Learning Project - Build and Deploy Real AI with Python Posted by
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2.2 GB | freecoursewb | 7 months | 7 | 0 |
| 3.1 GB | freecoursewb | 3 years | 0 | 4 | |
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[ FreeCourseWeb ] Udemy - Build & Deploy Machine Learning Web Application In Cloud Posted by
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[ DevCourseWeb ] Udemy - Project- End to End Machine Learning Web App Deploy in Cloud Posted by
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