Udemy - End to End Image Classification Web App Deploy in Cloud

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Udemy - End to End Image Classification Web App Deploy in Cloud (Size: 3 GB)
  0 0 B
  001 Introduction.en.srt 3.1 KB
  1 640.5 KB
  001 Introduction.mp4 31.5 MB
  2 308.4 KB
  002 Installing Python.en.srt 4.5 KB
  002 Installing Python.mp4 33.9 MB
  003 Download the Resources.html 921.6 B
  003 skimage.zip 1.2 MB
  3 721.1 KB
  004 What is Image & Pixels.en.srt 5.9 KB
  004 What is Image & Pixels.mp4 20.2 MB
  4 943.3 KB
  5 955.4 KB
  005 Read Image in skimage.en.srt 5 KB
  005 Read Image in skimage.mp4 28.2 MB
  006 Split into rgb array.en.srt 8.9 KB
  006 Split into rgb array.mp4 53 MB
  6 427.2 KB
  007 Convert image into grayscale.en.srt 8.9 KB
  007 Convert image into grayscale.mp4 53 MB
  7 786.8 KB
  008 Image Histogram.en.srt 6.5 KB
  8 368.4 KB
  008 Image Histogram.mp4 44 MB
  9 203.6 KB
  009 Histogram Equalization.en.srt 4.9 KB
  009 Histogram Equalization.mp4 32.6 MB
  010 Resize Images to any shape.en.srt 5 KB
  010 Resize Images to any shape.mp4 27.4 MB
  10 900.5 KB
  011 Download the Resources.html 1 KB
  11 236.2 KB
  011 dataprepare-machinelearning-pipeline.zip 43.8 MB
  12 401.2 KB
  012 What we will do _.en.srt 2.6 KB
  012 What we will do _.mp4 12.8 MB
  13 21.5 KB
  013 Understand the data what we have.en.srt 2.9 KB
  013 Understand the data what we have.mp4 25.3 MB
  014 Get all image filename in list in Python.en.srt 9.9 KB
  014 Get all image filename in list in Python.mp4 57.1 MB
  14 476.8 KB
  15 481.4 KB
  015 Labeling Images.en.srt 10.9 KB
  015 Labeling Images.mp4 76 MB
  016 Read all images from the folders and save in Pickle.mp4 50.6 MB
  16 492.8 KB
  016 Read all images from the folders and save in Pickle.en.srt 10.3 KB
  017 Visualize all images and labels.mp4 85.8 MB
  17 396.2 KB
  017 Visualize all images and labels.en.srt 16 KB
  018 Import Python libraries and Installations.en.srt 5.9 KB
  018 Import Python libraries and Installations.mp4 30.3 MB
  18 954.2 KB
  019 Load the Data and split into train and test set.en.srt 5.8 KB
  019 Load the Data and split into train and test set.mp4 33.7 MB
  19 358.6 KB
  020 HOG Feature Extraction.mp4 133.2 MB
  20 652.6 KB
  020 HOG Feature Extraction.en.srt 17.6 KB
  021 RGB to Gray Transformer.en.srt 10.1 KB
  021 RGB to Gray Transformer.mp4 54.4 MB
  022 HOG Transformer.en.srt 17.2 KB
  022 HOG Transformer.mp4 104.2 MB
  23 864.1 KB
  023 Train SGD classifier.en.srt 16.8 KB
  023 Train SGD classifier.mp4 93.8 MB
  024 Model Evalution.en.srt 5.4 KB
  024 Model Evalution.mp4 38.4 MB
  24 417.3 KB
  025 Pipeline Model.en.srt 8.4 KB
  025 Pipeline Model.mp4 49.9 MB
  25 393 KB
  026 Grid Search for Parameter Tuning.en.srt 15.3 KB
  026 Grid Search for Parameter Tuning.mp4 91.1 MB
  26 131.6 KB
  027 Best Estimator.en.srt 7.3 KB
  027 Best Estimator.mp4 42 MB
  27 110.2 KB
  28 983.5 KB
  028 Train Model and Save in pickle.en.srt 18.4 KB
  028 Train Model and Save in pickle.mp4 122.1 MB
  29 222.2 KB
  029 Make pipeline - Get the Prediction.en.srt 20.3 KB
  029 Make pipeline - Get the Prediction.mp4 140.7 MB
  30 525 KB
  030 Make pipeline - Decision Function.en.srt 16.6 KB
  030 Make pipeline - Decision Function.mp4 99.6 MB
  031 Make pipeline - pipeline model.en.srt 8 KB
  031 Make pipeline - pipeline model.mp4 58.6 MB
  032 Download the Resources.html 921.6 B
  032 flask-app.zip 1.7 MB
  033 Start Flask App.mp4 43.5 MB
  33 571.6 KB
  033 Start Flask App.en.srt 8.3 KB
  34 602.5 KB
  034 Download Bootstrap & JQuery.en.srt 7.2 KB
  034 Download Bootstrap & JQuery.mp4 38.4 MB
  035 Import Bootstrap 4.en.srt 3.2 KB
  035 Import Bootstrap 4.mp4 19.1 MB
  35 90.2 KB
  036 Navigation Bar.en.srt 9 KB
  036 Navigation Bar.mp4 52.2 MB
  36 309.6 KB
  37 410.4 KB
  037 Footer.en.srt 4 KB
  037 Footer.mp4 19.9 MB
  38 478.6 KB
  038 Inheritance (Layout Page).en.srt 6.2 KB
  038 Inheritance (Layout Page).mp4 27.6 MB
  039 File Upload (Http Request).mp4 64.5 MB
  39 562.1 KB
  039 File Upload (Http Request).en.srt 11.8 KB
  040 Styling the Page with CSS.en.srt 11.8 KB
  040 Styling the Page with CSS.mp4 64.5 MB
  40 711.1 KB
  41 504.3 KB
  041 File Upload Backend Operations (Flask).en.srt 15.6 KB
  041 File Upload Backend Operations (Flask).mp4 104.6 MB
  042 Integrate Machine Learning Pipeline Model.en.srt 15.3 KB
  042 Integrate Machine Learning Pipeline Model.mp4 131.3 MB
  42 836.4 KB
  43 413.1 KB
  043 Send Image from HTML to Server Side.en.srt 15.3 KB
  043 Send Image from HTML to Server Side.mp4 120.1 MB
  044 Adjust the image Height and Width Dynamically.en.srt 7.4 KB
  44 645.7 KB
  044 Adjust the image Height and Width Dynamically.mp4 61.5 MB
  45 719.8 KB
  045 Styling HTML for the Output.en.srt 4.3 KB
  045 Styling HTML for the Output.mp4 31.5 MB
  046 Error Handlers 404, 405, 500.en.srt 14.1 KB
  046 Error Handlers 404, 405, 500.mp4 131.7 MB
  46 256.5 KB
  047 About Page & href.en.srt 6.5 KB
  47 818.1 KB
  047 About Page & href.mp4 55.6 MB
  048 Create Account in Python Anywhere for Free.mp4 51.6 MB
  48 77.8 KB
  048 Create Account in Python Anywhere for Free.en.srt 6.9 KB
  049 Preparing Requirements.en.srt 6.4 KB
  049 Preparing Requirements.mp4 44.9 MB
  49 970.8 KB
  050 Upload Flask App in Python Anywhere.mp4 29.5 MB
  50 225 KB
  050 Upload Flask App in Python Anywhere.en.srt 4.8 KB
  51 352.7 KB
  051 Installing Requirements.en.srt 1.5 KB
  051 Installing Requirements.mp4 21.7 MB
  052 Deploy you Flask App and get access anywhere from the World.en.srt 10.4 KB
  052 Deploy you Flask App and get access anywhere from the World.mp4 80.6 MB
  053 Common Error you will get while deploying.en.srt 5 KB
  053 Common Error you will get while deploying.mp4 43 MB
  054 Bonus Lecture_ Next Steps.html 1.6 KB
  TutsNode.com.txt 102.4 B
  [TGx]Downloaded from torrentgalaxy.to .txt 614.4 B
  ▲ 157 total files

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 3/2021

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