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]
Feature Engineering 2hLesson Objectives
- Resolve missing values using imputation
- Detect and address outliers
- Resolve class imbalance in classification problems
Model Selection 2hLesson Objectives
- Choose an optimal set of predictors to use in a model
- Use model selection metrics for choosing an optimal model
- Use sequential feature selection to choose a set of features for a model
- Identify how high dimensional problems influence model selection
Cross-Validation 2hLesson Objectives
- Understand the role of cross-validation in the machine learning workflow
- Use k-fold cross-validation to check model performance
- Use LOOCV cross-validation to check model performance
- Understand the bias-variance trade-off when choosing the number of folds
Regularization 2hLesson Objectives
- Identify the role of regularization in machine learning
- Use regularized versions of linear regression
- Identify the difference between ridge and LASSO regression
- Standardize the features using helper functions in scikit-learn
Going Beyond Linear Models 2hLesson Objectives
- Implement polynomial regression in scikit-learn
- Define piecewise functions and splines
- Implement regression splines in scikit-learn
- Establish best practices concerning splines
Guided Project: Optimizing Model Prediction 2hLesson Objectives
- Iterate on and optimize a previous model
- Use k-fold cross-validation for model selection
- Use non-linear models to improve model prediction
Projects in this course
Guided Project: Optimizing Model Prediction
In this guided project, we will predict the damage done by forest fires and improve upon an existing model.
The Dataquest guarantee
Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.
We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.
Master skills faster with Dataquest
Go from zero to job-ready
Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.
Build your project portfolio
Build confidence with our in-depth projects, and show off your data skills.
Challenge yourself with exercises
Work with real data from day one with interactive lessons and hands-on exercises.
Showcase your path certification
Impress employers by completing a capstone project and certifying it with an expert review.