Build a Linear Regression Prediction Model with PyTorch from Scratch - part 1
Build a Linear Regression Prediction Model with PyTorch from Scratch
Want to learn how to build a machine learning model from scratch using PyTorch? In this tutorial, you'll learn every step required to create a Linear Regression Prediction Model with PyTorch from Scratch using Python, Jupyter Notebook, FastAPI, and Uvicorn.
In this video, Build a Linear Regression Prediction Model with PyTorch from Scratch is explained step by step, making it suitable for beginners while also providing practical knowledge for software engineers and AI developers building production-ready AI applications.
On the Applied AI Manager channel, we focus on practical Artificial Intelligence, Machine Learning, Software Engineering, AI Agents, Computer System Validation (CSV), and modern software delivery using AI technologies.
What you'll learn
How linear regression works
How to build a prediction model from scratch
Creating a Linear Regression model in PyTorch
Understanding tensors, gradients, and optimization
Preparing training data
Training and evaluating a machine learning model
Making predictions with the trained model
Running the solution inside Jupyter Notebook
Building a FastAPI REST API for model inference
Serving the AI model using Uvicorn
Organizing a production-ready Python project
Best practices for deploying machine learning models
If you're learning Machine Learning, Deep Learning, Python, or PyTorch, this tutorial will help you understand the complete workflow—from data preparation and model training to exposing your trained prediction model through a modern FastAPI web service.
Build a Linear Regression Prediction Model with PyTorch from Scratch demonstrates how modern AI applications are created using Python tools that are widely used in production environments. You'll see how Jupyter Notebook accelerates experimentation, how PyTorch simplifies model development, and how FastAPI and Uvicorn make deployment straightforward.
This tutorial is ideal for:
AI Engineers
Machine Learning Engineers
Software Developers
Data Scientists
Engineering Managers
Python Developers
Students learning AI
Anyone interested in practical PyTorch projects
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