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.
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
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Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our course, you'll master data skills and grow your career.
We believe so strongly in our courses that we offer a full satisfaction guarantee. If you complete a career course on Dataquest and aren't satisfied with your outcome, we'll give you a refund.
Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.
Build confidence with our in-depth projects, and show off your data skills.
Work with real data from day one with interactive lessons and hands-on exercises.
Impress employers by completing a capstone project and certifying it with an expert review.
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