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End to End ML Project - Fashion MNIST - Description

Objective

Fashion-MNIST is a dataset of Zalando's article images —consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label.

The objective of the project is - to use Fashion-MNIST data set to identify (predict) different fashion products(articles) from the given images using Machine Learning.

We will be following the below steps to solve this problem:

  1. Importing the libraries

  2. Using some pre-defined utility functions

  3. Loading the data

  4. Cleaning the data

  5. Dividing the dataset into training and test dataset using train_test_split in the ratio 85:15

  6. Training several models and analyzing their performance to select a model

  7. Use dimensionality reduction to improve the ‘training’,
    ‘fine-tuning’ and ‘prediction’ time.

  8. Fine-tuning the model by finding the best hyper-parameters and features

  9. Evaluating selected model using test dataset


Acknowledgements

Cloudxlab is using this “Fashion MNIST” problem for its machine learning learners for learning and practicing. Fashion-MNIST dataset is a collection of fashion article's images provided by Zalando . We thank Zalando Research for hosting the dataset.


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