{"id":5345,"date":"2026-08-10T09:22:03","date_gmt":"2026-08-10T09:22:03","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/datacamp-supervised-machine-learning-in-python-2024-8\/"},"modified":"2026-08-10T09:22:03","modified_gmt":"2026-08-10T09:22:03","slug":"datacamp-supervised-machine-learning-in-python-2024-8","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/datacamp-supervised-machine-learning-in-python-2024-8\/","title":{"rendered":"Datacamp \u2013 Supervised Machine Learning in Python 2024-8"},"content":{"rendered":"
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Descriptions<\/h2>\n

Supervised Machine Learning in Python, Master the fundamentals of supervised machine learning and discover how to make predictions using labeled data. Join the ML revolution today! If you\u2019re new to machine learning, or want to specialize in supervised machine learning, this is an ideal place to start. You\u2019ll start by learning about and implementing core supervised learning models, such as K-Nearest Neighbors (KNN), Logistic Regression, Linear Regression, Support Vector Machines (SVMs), and tree-based models with the popular scikit-learn library. You\u2019ll also discover how to use state-of-the-art algorithms like XGBoost to efficiently boost modelling performance on tabular datasets. To get the most out of your models, you\u2019ll learn about different hyperparameter tuning techniques and how to decide which technique to use for your use case. You\u2019ll finish the track by bringing your knowledge of these diverse models together to learn about ensemble learning, where different models are combined to improve performance and solve more complex problems. By the time you\u2019re finished, you\u2019ll have mastered the essential supervised machine learning concepts and be able to apply them in Python.<\/p>\n

What you\u2019ll learn<\/h3>\n