Monday 12 February 2024

What is machine learning and why we should care about


 “Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed.”
                                                 —Arthur Samuel, 19591


The definition 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.

When machine learning is seen as a process, the following definition is insightful:

“Machine learning is the process by which a computer can work more accurately as it collects and learns from the data it is given.”
                                                —Mike Roberts2

For example, as a user writes more text messages on a phone, the phone learns more about the messages’ common vocabulary and can predict (autocomplete) their words faster and more accurately.

In the broader field of science, machine learning is a sub field of artificial intelligence and is closely related to applied mathematics and statistics. All this might sound a bit abstract, but machine learning has many applications in everyday life.

Here are the important points from the above discussion 

  1. Defined by Arthur Samuel as a field of study that enables computers to learn without explicit programming.Experts develop general-purpose algorithms for large learning problems.
  2. For specific tasks, more specific data is fed to the algorithm.
  3. Machines use data as their source of information and compare their output to desired outputs.
  4. As a process, machine learning improves accuracy by collecting and learning from given data.
  5. Examples include a phone learning about common vocabulary and predicting user's words faster.
  6. Machine learning is a sub field of artificial intelligence, closely related to applied mathematics and statistics.


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