In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to do Fashion MNIST image classification using GradientBoosting in Python.
What should I learn from this recipe?
You will learn:
- How to do Fashion MNIST image classification using GradientBoosting in Python.
- How to create training and testing dataset using scikit-learn.
- How to report confusion matrix.
How to predict wine-class (wine data) using a keras deep learning model:
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
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