Virtual Practice Labs     Virtual CodeX     CodeX

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Perform ML programming tasks with Python, such as splitting data and standardizing data, and classification using nearest neighbors and ridge regression. Then, test your skills by answering assessment questions after performing principle component analysis, visualizing correlations, training a naive Bayes model and a support vector machine model.
This lab provides access to several tools commonly used in machine learning, including:
Microsoft Excel 2016
Visual Studio Code
Anaconda
Jupyter Notebook + JupyterHub
Pandas, NumPy, SiPy
Seaborn Library
Spyder IDE
This lab is part of the ML Programmer track of the Aspire Machine Learning Architect Journey.

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