In machine learning and statistics, logistic regression is a tool used frequently to create models to summarize the probability of a certain class or event existing. This could be win or lose, pass or fail and many more. Logistic regression is often confused with linear regression, however, they have distinct differences. Linear regression is used to predict trends in data and create models through a “line of best fit”, also calculating sources of errors within the predictions.
I published Machine Learning article with video. Read the article here: https://medium.com/mlearning-ai/using-logistic-regression-in-machine-learning-with-python-66af0dd0135c
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Linear Regression is a machine learning concept which is a form of supervised learning that uses labelled input and output data. Through linear regression, we are able to construct a model to create predictions based on a dataset and calculate sources of error in our prediction. Machine learning concepts covered in this video include the mathematics of linear regression, machine learning and regression using python and its libraries, and analyzation of datasets with python. I published Machine Learning article with video. Read the article here: https://python.plainenglish.io/machine-learning-for-beginners-78c818f74ba8 |
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