Actually doing your first Machine Learning project can be challenging: How to handle the data? How to identify what is important in the data? How to visualize correlations? How to evaluate your model? How to avoid some common pitfalls? In this workshop, we will work-through a first project in machine learning. We will begin with a concept of a project, ingest the data, visualize and view potential correlations, select, train our model, and evaluate the model. Focusing on the detection of breast cancer as our goal The technologies that will be covered in this workshop include:
- Neural Networks
- Python 3
- Pandas
- Seaborn
- Scikit-learn
- Tensorflow 2
- The class will contain hands-on parts which will be completed on PACE resources within Jupyter Notebooks.
Prerequisites: Some familiarity with Python.
