Machine Learning Course Notebooks

This repository contains Google Colab notebooks from a machine learning course, covering essential topics in regression, evaluation, and classification. Each notebook is designed to help you understand and implement fundamental concepts and techniques in machine learning.

Table of Contents

1. Optimization for Regression

• Understanding the Dataset: An introduction to the dataset used for regression tasks.
• Closed Form Solution: Analytical solution to linear regression using the normal equation.
• Batch Gradient Descent: Iterative optimization technique for minimizing the cost function.
• Polynomial Regression: Extending linear regression to capture non-linear relationships.
• Mini-Batch Gradient Descent: A variant of gradient descent that balances batch and stochastic methods.
• Step Sizes: Understanding the impact of learning rates on gradient descent convergence.

2. Evaluating Predictors

• Visualizing Different Errors: Analyzing and interpreting prediction errors.
• Picking the Best Polynomial Predictor: Model selection using validation techniques.
• Regularization: Techniques like L1 and L2 regularization to prevent overfitting.

3. Classification

• Understanding the Dataset: Overview of datasets used for binary and multiclass classification tasks.
• Binary Classification: Implementing and evaluating models for two-class problems.
• Multiclass Classification: Extending binary classification methods to handle multiple classes.

How to Use

1. Clone the repository:

   https://github.com/Usaidahmed10/Machine-Learning-Course-Notebooks.git

2. Open the desired notebook in Google Colab or any Jupyter notebook environment.
3. Follow the instructions and execute the cells sequentially.

Prerequisites

• Python 3.x
• Google Colab or Jupyter Notebook
• Basic knowledge of Python programming and machine learning

Contributing

Contributions to improve these notebooks are welcome. Feel free to submit a pull request or raise an issue if you find a bug or have suggestions.

License

This repository is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

Special thanks to the machine learning community and the creators of the datasets used in this course.

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Happy Learning!