Introduction
In this course, you will learn how to master the skills of scikit-learn more deeply through hands-on practice.
ðŊ Tasks
In this course, you will learn:
- How to use decision trees and understand validation curves
- How to cluster data and gain insights from it
- How to classify data using support vector machines (SVM) and naive Bayes
- How to perform linear regression and predict flower types using nearest neighbors
- How to understand metrics and scoring, and apply manifold learning and data decomposition
- How to work with random classification datasets, make multilabel classifications, and reduce high-dimensional data
- How to use the Scikit-Learn LibSVM GUI, vector quantization, and hierarchical clustering
- How to transform prediction targets, perform feature agglomeration, and model species distribution
- How to extract features, compare feature selection techniques, and scale and transform data
- How to perform curve fitting with Bayesian ridge regression, use Lasso and Elastic Net, and work with logistic regression
- How to perform joint feature selection with multi-task Lasso, use SGD penalties, and apply Theil-Sen regression
- How to reconstruct images using compressive sensing
ð Achievements
After completing this course, you will be able to:
- Confidently apply a wide range of scikit-learn techniques to solve real-world problems
- Understand the strengths and limitations of different machine learning algorithms
- Effectively preprocess and transform data to improve model performance
- Interpret model results and evaluate the quality of your predictions
- Continuously expand your knowledge and skills in the field of machine learning
