Introduction to Machine Learning
Introduction to Machine Learning
If I ask you about “Machine learning,” you'll probably imagine a robot or something like the Terminator. In reality t, machine learning is involved not only in robotics but also in many other applications. You can also imagine something like a spam filter as being one of the first applications in machine learning, which helps improve the lives of millions of people. In this chapter, I'll introduce you to what machine learning is, and how it works.
What is machine learning?
Machine learning is the practice of programming computers to learn from data. In the above example, the program will easily be able to determine if the given are important or are “spam”. In machine learning, data referred to as called training sets or examples.
In 1959, while working for IBM, Arthur Samuel, an early American pioneer in the fields of computer gaming and artificial intelligence, coined the term "Machine Learning."He said that "the field of study that gives computers the ability to learn without being explicitly programmed" is what machine learning is. Machine learning, on the other hand, does not have a universally accepted definition. The term is defined differently by different authors. Two more definitions are provided below.
Programming computers with example data or previous experience to optimize a performance criterion is known as machine learning. Learning is the execution of a computer program to optimize the model's parameters using training data or previous experience. We have a model defined up to some parameters. The model could be predictive to make future predictions or descriptive to learn from data.
The question of how to construct computer programs that automatically improve with experience is the focus of the field of study known as machine learning.
Definition of learning: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P , if its performance at tasks T, as measured by P , improves with experience E.
Examples
Handwriting recognition learning problem
Task T : Recognizing and classifying handwritten words within images
Performance P : Percent of words correctly classified
Training experience E : A dataset of handwritten words with given classifications
A robot driving learning problem
Task T : Driving on highways using vision sensors
Performance P : Average distance traveled before an error
Training experience E : A sequence of images and steering commands recorded while observing a human driver
Definition: A computer program that learns from experience is called a machine learning program or simply a learning program.
Machine Learning Classification
Depending on the nature of the learning "signal" or "response" that is available to a learning system, there are four main categories of machine learning implementations:
Supervised Learning
Unsupervised Learning
A type of machine learning algorithm called unsupervised learning is used to draw inferences from datasets with input data but no labeled responses. Classification or categorization is not included in the observations of unsupervised learning algorithms.
Reinforcement learning
The problem of getting an agent to behave in the world in a way that maximizes its rewards is called reinforcement learning.
In most forms of machine learning, a learner isn't told what to do; rather, they have to try different things to figure out which ones are most rewarding. Consider teaching a dog a new trick, for instance: We can't tell it what to do, but we can reward or punish it if it does the right thing or the wrong thing.
When you watch the video, notice how the program starts out clumsy and untrained but gradually gets better with practice until it wins.
For more information on machine learning algorithim, you can read this article :- Common Machine Learning Algorithms
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