Supervised ML
In this course, you will learn how to train Machine Learning models to make predictions, both numerical (regression) and categorical (classification). These include linear models, probabilistic models, and neural networks.
You will learn how to evaluate these models, and improve them, in order to best fit your data.
There are video lectures and code labs, so you can apply what you’ve learned!
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Chapter 1 - Linear Models
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Chapter 2 - Probabilistic Models
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Chapter 3 - Ensemble Methods
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Chapter 4 - Neural Networks
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Chapter 5 - Evaluating and Improving Models
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Model Evaluation, testing, validation, and error metrics
Model Evaluation
Training, Testing
Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
Types of Errors: Overfitting and Underfitting
Cross Validation and K-fold Cross Validation
Model Evaluation Graphs
Grid Search
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Final Lab - The Titanic Dataset
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Retake this course?
Retaking this course from the beginning will reset all of your tracked progress.