Once training is done, how do you know a model is any good? A look at model evaluation, what overfitting is, and the metrics used for classification and regression tasks.

Third in our beginner's ML series: what training a model really means, how parameters and the loss function relate, plus hyperparameters, libraries, and model types.

Data preparation eats up roughly 80% of a machine learning project's time. Learn its four stages: collection, inspection, summary statistics, visualization.

Part 1 of a 5-step ML workflow: how to define a problem clearly, and tell supervised learning (regression, classification) apart from unsupervised clustering.

A beginner-friendly walkthrough of Kaggle's CIFAR-10 competition in PyTorch, comparing a plain CNN, Dropout, BatchNorm, and a transfer-learned DenseNet.

Learn the key difference between a Python Module and a Package. This beginner-friendly guide explains how to use packages and the __init__.py file to organize your code effectively, with a simple, step-by-step example.

Learn all about Python modules in this beginner's guide. Discover how to create your own modules for reusable code and master the 4 different ways to "import" them into your projects for cleaner, more efficient programming.