Welcome to the Machine Learning Series project! This repository contains Jupyter notebooks and resources for learning and practicing machine learning concepts, with a focus on linear regression and related topics.
linear-regression.ipynb: Step-by-step walkthrough of linear regression, error metrics, and model evaluation.regression-walkthrough-1.ipynb: Additional regression examples and exercises.venv.ipynb: (Optional) Notebook for managing your Python environment.requirements.txt: List of required Python packages.
- Clone this repository.
- Install dependencies:
conda create -n ml-env --file requirements.txt -c conda-forge -y
- Open the notebooks in Google Colab, Jupyter or VS Code and follow along!
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- Pattern Recognition and Machine Learning by Christopher Bishop
- Introduction to Statistical Learning
- Coursera: Machine Learning by Andrew Ng
- fast.ai Practical Deep Learning
- StatQuest with Josh Starmer (YouTube)
- scikit-learn Documentation
- Pandas Documentation
- Matplotlib Documentation
- Kaggle: Datasets and Competitions
Pull requests and suggestions are welcome! Please open an issue to discuss any changes or ideas.
Happy Learning!