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Patrick Kennedy

Associate Consultant @Conversant

Course overview

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.

Key skills

  • 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

Course outline

Optimizing Machine Learning Models in Python [6 lessons]

Feature Engineering 1h

Lesson Objectives
  • Resolve missing values using imputation
  • Detect and address outliers
  • Resolve class imbalance in classification problems

Model Selection 1h

Lesson 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 1h

Lesson 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 1h

Lesson 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 1h

Lesson 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 1h

Lesson 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

For this project, we’ll step into the role of data scientists to predict forest fire damage using optimized machine learning models in Python.

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.

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Challenge yourself with exercises

Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

Showcase your path certification

Showcase your path certification

Impress employers by completing a capstone project and certifying it with an expert review.

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Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”


Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”


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Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”

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