Welcome to the Machine Learning Course Repository! This repository contains Jupyter Notebook files accompanying the lessons in our machine learning course. Each notebook focuses on a specific topic related to machine learning, providing explanations, code examples, and exercises to help you understand and apply the concepts.

01-How_Regression_Works.ipynb

• Introduction to regression and how it works.

02-Regression_Cost_Function.ipynb

• Exploring cost functions in regression analysis.

03_Gradient_Descent.ipynb

• Understanding gradient descent optimization algorithm.

04_Linear_Regression_scipy.ipynb

• Implementing linear regression using SciPy.

05_Linear_Regression_scikit-learn.ipynb

• Implementing linear regression using scikit-learn.

06_Polynomial_Regression.ipynb

• Exploring polynomial regression techniques.

07_Local_regression.ipynb

• Understanding and implementing local regression.

08_Logistic_Regression.ipynb

• Introduction to logistic regression.

09_Logistic_Regression_Multiclass.ipynb

• Implementing logistic regression for multiclass classification.

10_Logistic_Regression_NonLinear.ipynb

• Applying logistic regression to non-linear data.

11_Generative_Models_for_Classification.ipynb

• Exploring generative models for classification tasks.

Your feedback is valuable to us! If you have any suggestions, find any errors, or want to contribute improvements, feel free to open an issue or submit a pull request.

Happy learning! 🚀