DataCamp
FreeLinear Classifiers in Python
4.8(333)66k studentsINTERMEDIATEEnglish
Korshub's take
Master Logistic Regression and SVMs in Python
Linear classifiers represent fundamental building blocks in supervised machine learning. This intermediate course provides both practical experience and deep conceptual grounding in scikit-learn classification models.
Course Highlights
- Model Implementation: Build, evaluate, and fine-tune logistic regression models and support vector machines (SVMs).
- Scikit-Learn Workflows: Follow structured ML steps to train, test, and tune your models effectively.
- Conceptual Mechanics: Gain insight into the mathematical intuition behind decision boundaries and algorithmic behavior.
Target Audience
Intermediate Python users and data science practitioners looking to strengthen their supervised learning foundations with scikit-learn.
About this course
In this course you'll learn all about using linear classifiers, specifically logistic regression and support vector machines, with scikit-learn. Once you've learned how to apply these methods, you'll dive into the ideas behind them and find out what really makes them tick. At the end of this course you'll know how to train, test, and tune these linear classifiers in Python. You'll also have a conceptual foundation for understanding many other machine learning algorithms.
Price
Free
This course is free to enrol.
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