Training data is the fuel that powers your machine-learning model. Without it, your model isn't going anywhere, and it's not just any old fuel, either: it's a whole barrel of racing-grade gasoline. The more training data you can use to teach your model what to look for in the world, the better your model will be at figuring out what's essential and what isn't. If you're working with labeled data (which means that each example has a label), you'll also know how accurate your predictions are and whether they're improving over time. The more data available for training, the better off you'll be! Using training data in machine learning programs is a simple concept, but it is foundational to how these technologies work. The training data is an initial data set to help a understand how to apply technologies like neural networks to learn and produce sophisticated results. It may be complemented by subsequent data sets called validation and testing sets. It's like teaching a child how to play basketball by having them shoot hoops with their friends on the playground. The child will learn how to dribble, pass, and fire, but they'll never know if they can make it into the NBA until they get out there and play against other kids who are good at shooting hoops! The machine learning program is like a child. It needs food to operate. The better the training data, the more accurate and effective the algorithm. If you don't give it enough food, it might not grow into an adult who can do cool stuff. If you give it too much food, it'll get fat and lazy and always stay in its room. That's why training data is so critically and essentially important!