Model Development Lifecycle

"Building the future of technology, one algorithm at a time."

Introduction

Welcome to the Model Development Lifecycle repository, where we focus on building and deploying machine learning models with a hands-on approach.

This project currently walks you through various regression models (more content 🔜), from simple Linear Regression to more advanced methods like Random Forest Regression and Support Vector Regression.

Here, you'll find practical examples and exercises to help you understand how to train, evaluate, and deploy regression models in Python. Whether you're a beginner or looking to sharpen your skills, this repo will guide you through the most commonly used techniques in machine learning.

🚀 What's Inside:

• Practical, hands-on examples
• Clear explanations of the implementation, models an algorithms

🧠 Key Topics

Tasks

• 📊 Regression Models for Machine Learning

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Credits

This project is brought to you by 🏴‍☠️ Dead Code (a.k.a Fernando de la Madrid)
Check out my LinkedIn profile.

Bury the past, code the future ⚰️