After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
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.
Prerequisites
Curriculum
Course outline
1
Applying logistic regression and SVM
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.- scikit-learn refresher50 XP
- KNN classification100 XP
- Comparing models50 XP
- Overfitting50 XP
- Applying logistic regression and SVM50 XP
- Running LogisticRegression and SVC100 XP
- Sentiment analysis for movie reviews100 XP
- Linear classifiers50 XP
- Which decision boundary is linear?50 XP
- Visualizing decision boundaries100 XP
2
Loss functions
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.
3
Logistic regression
In this chapter you will delve into the details of logistic regression. You'll learn all about regularization and how to interpret model output.
4
Support Vector Machines
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.
Linear Classifiers in Python
Course
Complete
