{"id":2918,"date":"2026-08-10T09:00:21","date_gmt":"2026-08-10T09:00:21","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-machine-learning-with-python-for-everyone-part-3-fundamental-toolbox-2022-8\/"},"modified":"2026-08-10T09:00:21","modified_gmt":"2026-08-10T09:00:21","slug":"oreilly-machine-learning-with-python-for-everyone-part-3-fundamental-toolbox-2022-8","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-machine-learning-with-python-for-everyone-part-3-fundamental-toolbox-2022-8\/","title":{"rendered":"Oreilly \u2013 Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox 2022-8"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Course Machine Learning with Python for Everyone Part 3: Fundamental Toolbox. To code machine learning tasks you need to go beyond following discussions. Machine Learning with Python for Everyone Part 3: The Essential Toolbox shows you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends. You will learn about fundamental classification and regression metrics such as decision tree classifiers and regressions, support vector classifiers and regression, logistic regression, penalized regression, and discriminant analysis. You\u2019ll see techniques for engineering features, including scaling, discretization, and interactions. You will learn how to implement pipelines for more complex processing and nested cross-validation for setting hyperparameters. This course is suitable for anyone who needs to improve their basic understanding of machine learning concepts and become familiar with basic machine learning code. You may be a newer data scientist, a data analyst transitioning to using machine learning models, an R&D scientist looking to add machine learning techniques to your classic statistical training, or a manager who needs Adds Data Science\/Machine Learning to your team.<\/span><\/p>\n

Students should have a basic understanding of programming in Python (variables, basic control flow, simple scripts). They should also have basic familiarity with machine learning vocabulary (dataset, training set, test set, model). They should be running a Python installation that allows them to use scikit-learn, pandas, matplotlib, and seaborn.<\/span><\/p>\n

What you will learn in the course<\/span><\/h3>\n
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