Docker runs software inside a container - an isolated, packaged process that carries everything it needs to run: the code, the runtime, the libraries, and the system tools. You hand Docker a package, and it starts that software the same way on your laptop, on a teammate's machine, and on a production server.
You have almost certainly heard, or said, "it works on my machine". An app runs fine on the developer's laptop and then falls over in production because the server has a different Python version, a missing library, or a config file in the wrong place. Chasing those differences by hand wastes hours.
A container removes the guesswork. Everything the app needs is bundled together and shipped as one unit. If it runs in the container on your machine, it runs in the same container in production, because it is literally the same package.
A virtual machine boots a whole guest operating system - its own kernel, its own memory, gigabytes of disk - on top of your real one. It is heavy and slow to start.
A container skips all that. It shares the host machine's Linux kernel and isolates only what the app sees: its own filesystem, its own process list, its own network. The result starts in well under a second and is small enough to run many of them on one machine.
Think of it this way. A VM is a separate house with its own foundation and plumbing. A container is a locked apartment in a shared building - private space inside, but using the building's structure.
Docker shows up at almost every stage of modern delivery. Developers package an app once and run it anywhere. CI pipelines build and test inside clean, throwaway containers. Production platforms like Kubernetes schedule containers across fleets of servers. Learning Docker is the entry point to all of it.
This skill builds from the ground up. You will run containers, then build your own images, then wire several containers together into a working application.