This repository contains a collection of machine learning practice codes. The purpose of this repository is to enhance and solidify my machine-learning skills through hands-on coding exercises.
Machine learning is a rapidly evolving field with various algorithms and techniques. This repository serves as a personal practice ground where I explore and implement different machine learning concepts, algorithms, and models. It allows me to sharpen my skills, experiment with new ideas, and gain practical experience in the field of machine learning. Most of these practice codes are gotten from the book "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron"
To use the codes in this repository, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/ML_Practice_Codes.git
cd ML_Practice_Codes
-
Set up your Python environment with the necessary dependencies.
-
Open the code files in your preferred IDE or Jupyter Notebook to study, modify, or run the code.
This repository serves as a personal collection of machine learning practice codes. You can browse through the different code files to explore various machine-learning concepts and techniques. Each code file focuses on a specific topic or algorithm and includes explanations and comments to aid understanding. Feel free to use the code for learning purposes, experimentation, or as a reference for your own projects.
As this repository is for personal practice purposes, contributions are not expected. However, if you have suggestions, improvements, or bug reports, please feel free to open an issue on the repository's GitHub page.
The code in this repository is licensed under the MIT License. You are free to use, modify, and distribute the code for both commercial and non-commercial purposes.