Description
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.
Prerequisites
Basic Python knowledge is required
Details
Learning Objectives
- Determine whether machine learning is appropriate for a given real-world problem and identify entry-level approaches for simple datasets.
- Differentiate between supervised learning techniques including Linear/Logistic Regression, Decision Trees, and Random Forests.
- Utilize unsupervised methods (such as K-Means clustering) to uncover hidden patterns, clusters, and structures in unlabeled datasets.
- Write interactive Python code using Scikit-learn to import datasets, preprocess features, and train baseline machine learning models.
- Navigate the full machine learning lifecycle, transitioning seamlessly from data preparation to model training, evaluation, and inference.
- Calculate and interpret performance metrics using classification accuracy and confusion matrices alongside R2 scores and Mean Squared Error (MSE) for regression models.
- Benchmark computational efficiency and cross-validation scores to ensure models generalize accurately to unseen data.
Who Should Enrol
This series of workshops are for anyone with basic Python knowledge looking to take their first steps into machine learning and real-world problem solving.