In this course you will learn the details of linear classifiers like logistic regression and SVM.
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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.
In this chapter you will learn the basics of applying logistic regression and support vector machines (SVMs) to classification problems. You'll use the scikit-learn
library to fit classification models to real data.
In this chapter you will delve into the details of logistic regression. You'll learn all about regularization and how to interpret model output.
In this chapter you will discover the conceptual framework behind logistic regression and SVMs. This will let you delve deeper into the inner workings of these models.
In this chapter you will learn all about the details of support vector machines. You'll learn about tuning hyperparameters for these models and using kernels to fit non-linear decision boundaries.
In this chapter you will learn the basics of applying logistic regression and support vector machines (SVMs) to classification problems. You'll use the scikit-learn
library to fit classification models to real data.
In this chapter you will discover the conceptual framework behind logistic regression and SVMs. This will let you delve deeper into the inner workings of these models.
In this chapter you will delve into the details of logistic regression. You'll learn all about regularization and how to interpret model output.
In this chapter you will learn all about the details of support vector machines. You'll learn about tuning hyperparameters for these models and using kernels to fit non-linear decision boundaries.
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