This workshop will introduce participants to machine learning in Python. We’ll start with a brief explanation of Anaconda and the Jupyter notebook environment (although not required for the participant, the instructor will be using these tools). After an introduction to classification, regression and model selection, we’ll use a couple of example datasets to demonstrate how to create, apply and evaluate models in Scikit-learn. Although not required, we recommend all participants to have a basic knowledge of Python.
Machine Learning in Python (Scikit-Learn)
This workshop will cover the essentials of supervised machine learning algorithms using Python’s Scikit-learn library. We will focus on Naive-Bayes and Support Vector Machines (SVM). The workshop is designed for intermediate to advanced Python users. The session will be held in a computer lab and participants can choose to work on practice exercises either individually or in small groups.
This workshop will cover the essentials of unsupervised machine learning algorithms using Python’s Scikit-learn library. We will focus on K-Means and Principal Component Analysis (PCA). The workshop is designed for intermediate to advanced Python users. The session will be held in a computer lab and participants can choose to work on practice exercises either individually or in small groups.
