This repository contains all course assignments for my Data Science with Python, ML.NET and NimbusML course and will get you up to speed with Microsoft's new ML.NET library and the NimbusML package for Python.
By working through the code examples, you will learn how to design, train, and evaluate complex AI models in Python. The NimbusML package will allow you to call the powerful ML.NET machine learning library directly from your Python code. I'll provide you with all the code, libraries, and data sets you need to get started.
Please note that this repository only contains code examples with no additional support.
If you prefer a full-featured e-learning experience with live coaching, please check out my online course here:
https://www.machinelearningadvantage.com/datascience-with-python-nimbusml
Transforming data: Processing California housing data
Regression: Predict taxi fares in New York
Case study: Predict house prices in Iowa
Binary classification: Predict heart disease in Ohio
Case study: Detect credit card fraud in Europe
Multiclass classification: Recognize handwriting
Evaluating models: Detect SMS spam messages
Case study: Flag toxic comments on Wikipedia
Decision trees: Predict Titanic survivors
Case study: Predict Diabetes in Pima indians
Ensembles: Predict bike demand in Washington DC
Clustering: Classify Iris flowers