Course overview

Throughout this machine learning course, you’ll learn what’s happening in the model training process. You’ll also explore scikit-learn, one of the most useful and robust open-source machine learning libraries for the Python programming language.


You’ll learn concepts such as K-Nearest Neighbors (KNN) Algorithms and error metrics such as the Mean Squared Error and the Root Mean Squared Error. You’ll also learn about hyperparameter optimization, a technique used to optimize machine learning algorithms to improve trained models. Then you’ll dig into some k-fold cross-validation to perform more rigorous testing for your model.


Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview. In the guided project, you’ll build a model to predict car prices. This project is a chance for you to combine the skills you learned in this course and practice the machine learning workflow.

Key skills

  • Understanding the key concepts of machine learning

  • Avoiding common pitfalls in machine learning

  • Employing logical operators and comparison operators

  • Optimizing models for accuracy and efficiency

Course outline

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Projects in this course

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