Build and Deploy Machine Learning App in Cloud with Python

Convert a color image to grayscale with a Python library by combining red, green, and blue channels into a 2d grayscale array of 0 to 1, and save the result.

Learn how to resize an image to different shapes using transform.resize, setting output shapes like 200x300 or 800x1200 and adjusting from the original 400x600.

Prepare the dataset for image classification by understanding image size and shape, labeling data, and building a 20-class model in Python.

Explore the dataset structure with 20 class folders and 80 by 80 pixel images, showing partially pre-processed data. Learn how to load images and perform Python data preparation.

This lecture builds an rgb to gray transformer using a base estimate and transformer mixin, applying fit and transform to single or multiple images to produce grayscale outputs.

Explore building a hog transformer to extract histogram oriented gradients from grayscale images, transforming image data into robust features for machine learning.

Test your model with a classification report on the test data, report accuracy, precision, and recall, and discuss multiclass performance and hyperparameter tuning using group search method.

The lecture demonstrates grid search cross-validation to tune hyperparameters for a pipeline model, systematically enumerating orientation, pixels, loss type, and learning rate to maximize accuracy.

Create a pipeline to generate predictions by loading a classifier, preprocessing images (resize to 80x80, grayscale, hog features), scaling with a saved scaler, and predicting in a Flask web app.

Compute class decision values, convert to probabilities with softmax, visualize results, and extract the top five predictions for a machine learning pipeline.

Extend Django templates with a base layout and blocks to create reusable templates, override the body section, and render and test an upload page in the browser.

Create an image upload feature using a multipart http request, with an upload button and a file input named 'image', sending data to the flash gap for processing.

Prepare the Python web app dependencies by creating a requirements.txt, list libraries such as Flask, pandas, and SciPy with their versions, and install them using python -r requirements.txt.

Deploy a Flask web app to the cloud, configure the web app with the framework, upload your files, test with sample images, and access the app from anywhere.