The amount of data and the complexity of machine learning models have grown exponentially which led to the development of additional methods and techniques to improve accuracy of predictive models.
In this course, you will learn how to best select a model. You’ll get a strong understanding of cross-validation in the machine learning workflow and how to use k-fold and LOOCV cross-validation techniques to check performance.
Then, you’ll learn how to use regularization in machine learning including activities such as using regularized versions of linear regression, identifying the difference between ridge and LASSO regression or standardizing the features using helper functions in scikit-learn.
Finally, you’ll go beyond linear models by implementing polynomial regression in scikit-learn, defining piecewise functions and splines, implementing regression splines in scikit-learn and establishing best practices concerning splines
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll combine your new skills in a project to optimize a predictive model.
- Distinguishing between different optimization techniques
- Identifying the best optimization approach for your project
- Applying optimization methods to improve your model
- Employing machine learning tools on various optimization methods
Optimizing Machine Learning Models in Python [6 lessons]
Projects in this course
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