"Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed."
The characterization of machine learning coined by Arthur Samuel is often quoted and is genius in its broadness, but it leaves you with the question of how the computer learns. To achieve machine learning, experts develop general-purpose algorithms that can be used on large classes of learning problems. When you want to solve a specific task you only need to feed the algorithm more specific data. In a way, you're programming by example. In most cases a computer will use data as its source of information and compare its output to a desired output and then correct for it. The more data or "Experience" the computer gets, the better it becomes at its designated job, like a human does.
"Machine learning is the process by which a computer can work more accurately as it collects and learns given."
For example, as a user writes more text messages on a phone, the phone learns more about the messages' common vocabulary and can predict (auto complete) their words faster and more accurately.
Although machine learning is mainly linked to the data-modelling step of the data science process, it can be used at almost every step.
The data modelling phase can't start until you have qualitative raw data you can understand. But prior to that, the data preparation phase can benefit from the use of machine learning. An example would be cleansing a list of text strings; machine learning can group similar strings together so it becomes easier to correct spelling errors. Machine learning is also useful when exploring data. Algorithms can root out underlying patterns in the data where they'd be difficult to find with only charts. Given that machine learning is useful throughout the data science process, it shouldn't come as a surprise that a considerable number of Python libraries were developed to make your life a bit easier.
Broadly speaking, we can divide the different approaches to machine learning by the amount of human effort that's required to coordinate them and how they use labelled data—data with a category or a real-value number assigned to it that represents the outcome of previous observations.