Build an EASY Multiclass Classification Model in PyTorch

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut... Dive into the world of deep learning with our latest tutorial! In this video, we guide you through the process of building a robust multiclass classification model using PyTorch, a powerful open-source deep learning library. 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: ryannolandata@gmail.com 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT PyTorch for Beginners Playlist:    • PyTorch for Beginners (Deep Learning)   PyTorch Linear Regression:    • Build Your First PyTorch Model (Linear Reg...   Want to Learn PyTorch? Start here:    • Want to learn PyTorch? Start here   PyTorch Logistic Regression:    • Mastering Logistic Regression in PyTorch: ...   In this video, I walk you through building a neural network to solve the classic iris multi-class classification problem using PyTorch. We start by loading the iris dataset directly from scikit-learn and perform a complete end-to-end deep learning workflow, including data preprocessing, model architecture design, training, and evaluation. I explain how to properly split and scale your data, convert numpy arrays into PyTorch tensors, and set up your model to run on either CPU or GPU. We build a three-layer neural network from scratch using nn.Module, and I break down each component including the forward pass, loss calculation using cross-entropy loss, and optimization with SGD. Throughout the tutorial, I code everything line by line in Google Colab so you can follow along easily, explaining key concepts like training loops, epochs, accuracy calculation, and the difference between train and eval modes. By the end of this tutorial, you'll understand how to implement a complete PyTorch classification pipeline and achieve impressive results—we hit 100% accuracy on the test set with a loss of 0.065. This is part of my ongoing PyTorch and deep learning series, perfect for anyone looking to build practical machine learning skills with Python. Whether you're new to PyTorch or looking to strengthen your understanding of neural networks for classification problems, this tutorial covers all the fundamentals you need. TIMESTAMPS 00:00 Introduction & Overview 01:42 Importing Libraries & Setup 03:02 Loading the Iris Dataset 05:17 Visualizing the Data with Pandas 07:32 Creating Feature Combination Charts 10:30 Understanding the Data Structure 13:02 Train-Test Split & Data Scaling 15:00 Setting Up Device (CPU/GPU) 16:00 Creating Tensors from Data 19:00 Building the Neural Network Class 23:00 Defining Network Layers 26:00 Setting Input Features & Classes 28:00 Configuring Loss Function & Optimizer 30:00 Creating the Training Loop 33:00 Calculating Accuracy 35:40 Running the Training Process 38:00 Evaluating on Test Data 41:00 Final Results & Conclusion OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.