What is Machine Learning ?
What is Machine Learning ?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Machine Learning is -the field of study whicg privides computers the capability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and use it learn for themselves.
Machibe Learning is one of the most exciting technologies, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used todays in many more places than one would expect.
Machine learning overlaps with and inherits ideas from many related fields such as artificial intelligence. The focus of the field is learning, acquiring skills or knowledge from experience, which means synthesizing useful concepts from historical data.
As such, there are many different types of learning that you may encounter as a practitioner in the field of machine learning: from whole fields of study to specific techniques.
The learning process begins with observations or data, like examples, direct experience or instruction, in order to look for patterns in the data and make the better decisions in the future based on the examples that we provide. The main aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
Given that the focus of the field of machine learning is “learning,” there are many types that you may encounter as a practitioner.
Machine learning algorithms are often categorized as supervised or unsupervised or Semi-supervised machine learning algorithms or Reinforcement Machine Learning.
Some types of learning describes the whole subfields of study comprised of many different types of algorithms like “supervised learning.” And the others describe the powerful techniques that you can use on your projects like “transfer learning.”
The Supervised Machine Learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.
The Unsupervised Machine Learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.
The Semi-supervised Machine Learning algorithms fall somewhere in between the Supervised Machine Learning and Unsupervised Machine Learning, as they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data.
The Reinforcement Machine Learning algorithms is a learning method that interacts with its environment by producing the actions and discovering the errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.
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