Linear Regression code Implementation in Python | ML from Scratch скачать в хорошем качестве

Linear Regression code Implementation in Python | ML from Scratch

Building Machine Learning models is not just about theory — it’s about understanding the code behind it. Welcome to Day 7 of our ML journey, where we implement and explain Linear Regression coding step by step using Python and scikit-learn. In this video, we walk through the entire Linear Regression project, explaining each code block, why it is used, and how it contributes to building a regression model — all in a simple, beginner-friendly way. 🔍 What You’ll Learn in This Video In this hands-on session, we cover: ✨ How to load and explore a real dataset ✨ Selecting input features and target values ✨ Creating a Linear Regression model using scikit-learn ✨ Training the model and making predictions ✨ Evaluating performance using R² score ✨ Visualizing actual vs predicted values ✨ Understanding the importance of each line of code No confusing math — just clear code explanation with visuals. 📄 Linear Regression Code & Notes: https://shorturl.at/Lk63r Use this resource to revise quickly and strengthen your coding fundamentals. 🚀 Why This Topic Matters Linear Regression coding is the first real ML project for every beginner. It helps you: Understand how ML models are built in practice Read and write ML code confidently Prepare for advanced topics like Gradient Descent and Multiple Linear Regression Build a strong foundation for Data Science and AI Once you understand this code, ML becomes much easier. 🔔 Continue the Learning Journey Follow the playlist — each day builds on the previous one, helping you master Machine Learning step by step, from scratch. #machinelearning #linearregression #mlcoding #regression #datascience #pythonml #mlfromscratch #mlseries #supervisedlearning #aiml #notebooklm #googleai #mlforbeginners #education #visuallearning