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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 Xgboost in Python.
What should I learn from this recipe?
You will learn:
- How to do Fashion MNIST image classification using Xgboost in Python
- How to create training and testing dataset using scikit-learn.
- How to report confusion matrix.
Support SETScholars for Free End-to-End Applied Machine Learning and Data Science Projects & Recipes by becoming a member of WA Center For Applied Machine Learning and Data Science (WACAMLDS). Membership fee only $1.75 per month (on annual plan) and you will get access to 425+ end-to-end Python & R Projects.
Western Australian Center for Applied Machine Learning & Data Science – Membership
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Support SETScholars for Free End-to-End Applied Machine Learning and Data Science Projects & Recipes by becoming a member of WA Center For Applied Machine Learning and Data Science (WACAMLDS). Membership fee only $1.75 per month (on annual plan) and you will get access to 425+ end-to-end Python & R Projects.
Western Australian Center for Applied Machine Learning & Data Science – Membership
Learn by Doing With DataCamp
How to use VarianceScaling initializer to a Deep Learning Model in Keras