Learn how to predict League of Legends match outcomes using logistic regression with PyTorch. An easy-to-follow introduction for beginners exploring practical machine learning concepts with gaming data.
Evaluate your trained machine learning model and use it to make predictions. Understand key metrics like accuracy, precision, and recall in this hands-on guide!
Learn how to build a simple machine learning model in Python using the Iris dataset. In this part, we will step-by-step train our model.
Learn how to build a simple machine learning model in Python using the Iris dataset. Follow this step-by-step guide to train, evaluate, and make predictions with scikit-learn.
You'll understand how backpropagation helps neural networks learn by minimizing errors. We break it down step by step in simple terms!
New to PyTorch? This beginner-friendly guide explains tensors clearly and shows how to create and use them easily, even if you've never coded before!
Set up Python for ML with essential libraries like NumPy, Pandas, Matplotlib, and scikit-learn in a beginner-friendly way.
This article explores supervised vs. unsupervised learning, explaining their key differences, how they work, and real-world applications.
Discover how machine learning enables computers to learn from data. Explore supervised and unsupervised learning to understand how ML finds patterns and makes predictions.
Explore AI concepts, its differences from Machine Learning and Deep Learning, and why AI matters in modern technology and society.