Denetimli Makine Öğrenimi Python'da

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.

In this course you will learn the details of linear classifiers like logistic regression and SVM.

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.