Intro to Machine Learning (Part 2)

Monday, Nov. 9, 12:30-3:30 p.m.

Online

These hands-on sessions guide learners through the end-to-end machine learning lifecycle using Python and Scikit-learn. Participants evaluate when to apply machine learning to real-world challenges and implement initial baseline approaches for straightforward datasets. Through interactive coding exercises, learners import data, preprocess features, and build, train, and evaluate fundamental models. The curriculum contrasts key supervised learning algorithms including Linear and Logistic Regression, Decision Trees, and Random Forests while introducing unsupervised methods like K-Means clustering to discover latent structures within unlabeled data. Learners quantify model performance using regression metrics like R2 and Mean Squared Error alongside classification tools such as accuracy and confusion matrices.

Presented by ACENET

Event Listing 2026-11-09 12:30:00 2026-11-09 15:30:00 America/St_Johns Intro to Machine Learning (Part 2) These hands-on sessions guide learners through the end-to-end machine learning lifecycle using Python and Scikit-learn. Participants evaluate when to apply machine learning to real-world challenges and implement initial baseline approaches for straightforward datasets. Through interactive coding exercises, learners import data, preprocess features, and build, train, and evaluate fundamental models. The curriculum contrasts key supervised learning algorithms including Linear and Logistic Regression, Decision Trees, and Random Forests while introducing unsupervised methods like K-Means clustering to discover latent structures within unlabeled data. Learners quantify model performance using regression metrics like R2 and Mean Squared Error alongside classification tools such as accuracy and confusion matrices. Register here Online ACENET