This course aims to help build a solid foundation in Machine Learning using various tools such as Linear Regression, Logistic Regression and K-Means Clustering in Python.
This repository exhibits the projects completed throughout the "Beginner Machine Learning in Python + ChatGPT Bonus" course, which can be found here: [2023]https://www.udemy.com/course/machine-learning-python-level-1/
This course has 3 main sections:
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Regression Learned to predict continuous variables and covered foundational concepts like Simple and Multiple Linear Regression, Ordinary Least Squares, Testing your Model, R-Squared and Adjusted R-Squared.
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Classification Learned Logistic Regression, which is by far the most popular model for Classification. Also learned about Maximum Likelihood, Feature Scaling, The Confusion Matrix, Accuracy Ratios.
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Clustering Investigated the concepts of unsupervised learning and practiced using K-Means Clustering to discover previously unseen patterns in the data.