{"id":11768,"date":"2026-08-10T10:36:39","date_gmt":"2026-08-10T10:36:39","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-linear-regression-and-logistic-regression-in-python-2022-11\/"},"modified":"2026-08-10T10:36:39","modified_gmt":"2026-08-10T10:36:39","slug":"udemy-linear-regression-and-logistic-regression-in-python-2022-11","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-linear-regression-and-logistic-regression-in-python-2022-11\/","title":{"rendered":"Udemy \u2013 Linear Regression and Logistic Regression in Python 2022-11"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Linear Regression and Logistic Regression in Python Course. This comprehensive course covers all the steps involved in building Linear Regression and Logistic Regression models in Python. Upon completion, you will be able to identify business problems that can be solved with these techniques, implement linear and logistic regression models in Python, and analyze the results. The course begins with the basics of statistics, including data types, graphical representations, and measures of center and dispersion. It then covers Python basics, setting up the Jupyter environment, and working with libraries such as Numpy, Pandas, and Seaborn. The Machine Learning section covers basic definitions, terminology, and steps for building models. Data preprocessing includes data exploration, univariate and bivariate analyses, handling outliers, imputation of missing values, and correlation. The Regression section begins with Simple Linear Regression and expands to Multiple Linear Regression, with an emphasis on theory without mathematical details. Also, model accuracy, F-statistics, interpretation of categorical variables, and the method of least squares are examined. By the end, you will have the ability to interpret results to solve business problems and build predictive models with high confidence.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n