Machine Learning Scikit-Learn
Model Selection and Final Evaluation
Machine Learning Scikit-Learn
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Introduction to Machine Learning
This topic introduces students to the core concept of machine learning and gets their development environment set up before diving into hands-on model building.
This topic covers the essential steps needed to prepare raw data before it can be used to train a machine learning model, a critical stage that heavily impacts model performance.
This topic introduces regression, a core supervised learning technique used to predict continuous numerical values, along with how to evaluate how well a regression model performs.
This topic introduces classification, another core supervised learning technique used to predict categorical outcomes, covering several popular algorithms and how to evaluate them.
This topic introduces unsupervised learning techniques, where models find patterns in data without labeled outcomes, covering clustering and dimensionality reduction.
This final lesson teaches students how to compare multiple trained models, select the best-performing one, and conduct a final evaluation before considering the model ready for use.