This interactive course is an introduction to the world of Machine Learning (ML). It discovers some supervised learning algorithms and discusses when and how to use them. It begins by introducing the data pipeline and its processes, before moving on to statistical and visualization approaches to conduct exploratory and descriptive analytics on data in an effort to answer the question “what happened in the past?”. From there, you will explore the art of data preparation, including data cleaning, missing values, outlier detection, and feature transformation and engineering. Next, we will introduce predictive analytics to answer the question “What will happen in the future?”. We will cover techniques for classifying and predicting data for the supervised learning algorithm, such as k-NN, Naïve Bayes, Decision Tree and Random Forest, and provide guidance in deciding which ones to use. Finally, participants will learn about statistical evaluation methods used in comparing the performance of predictive modelling techniques. This course balances theory and practice. You will use practical concepts of ML applications to understand real-world situations. Topics include Data preparation, ML theory, ML process, ML algorithms, and Model evaluation.

Workshop Prerequisites:

Python programming + understanding of data preparation