Machine Learning (ML) Is The Study Of Computer Algorithms That Improve Automatically Through Experience. It Is Seen As A Subset Of Artificial Intelligence. Machine Learning Algorithms Build A Mathematical Model Based On Sample Data, Known As "Training Data", In Order To Make Predictions Or Decisions Without Being Explicitly Programmed To Do So. Machine Learning Algorithms Are Used In A Wide Variety Of Applications, Such As Email Filtering And Computer Vision, Where It Is Difficult Or Infeasible To Develop Conventional Algorithms To Perform The Needed Tasks.

Machine Learning Is Closely Related To Computational Statistics, Which Focuses On Making Predictions Using Computers. The Study Of Mathematical Optimization Delivers Methods, Theory And Application Domains To The Field Of Machine Learning. Data Mining Is A Related Field Of Study, Focusing On Exploratory Data Analysis Through Unsupervised Learning. In Its Application Across Business Problems, Machine Learning Is Also Referred To As Predictive Analytics.

Types of Learning in Machine

Machine Learning Is A Large Field Of Study That Overlaps With And Inherits Ideas From Many Related Fields Such As Artificial Intelligence.

The Focus Of The Field Is Learning, That Is, Acquiring Skills Or Knowledge From Experience. Most Commonly, This Means Synthesizing Useful Concepts From Historical Data.

• Learning Problems

First, we will take a closer look at three main types of learning problems in machine                     learning: supervised, unsupervised, and reinforcement learning.

1. 1. Supervised Learning is supervised learning where the training data contains very few labeled examples and a large number of unlabeled examples.
   2. Unsupervised Learning describes a class of problems that involves using a model to describe or extract relationships in data.
   3. Reinforcement Learning describes a class of problems where an agent operates in an environment and must learn to operate using feedback.

• Hybrid Learning Problems

The lines between unsupervised and supervised learning is blurry, and there are many      hybrid approaches that draw from each field of study.

• 1. Semi-Supervised Learning is supervised learning where the training data contains very few labeled examples and a large number of unlabeled examples.
  2. Self-Supervised Learning Self-supervised learning refers to an unsupervised learning problem that is framed as a supervised learning problem in order to apply supervised learning algorithms to solve it.
  3. Multi-Instance Learning is a supervised learning problem where individual examples are unlabeled; instead, bags or groups of samples are labeled.
• Statistical Inference Self-supervised learning refers to an unsupervised learning problem that is framed as a supervised learning problem in order to apply supervised learning algorithms to solve it.
  1. 1.   Inductive Learning is a supervised learning problem where individual examples are unlabeled; instead, bags or groups of samples are labeled.

1. 1. 2.  Deductive Inference Deduction or deductive inference refers to using general rules to determine specific outcomes.

• 1. 3.   Transductive Learning is used in the field of statistical learning theory to refer to predicting specific examples given specific examples from a domain.
• Learning Techniques

     This includes multi-task, active, online, transfer, and ensemble learning.

1. 1. Multi-Task Learning is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks.
   2. Online Learning involves using the data available and updating the model directly before a prediction is required or after the last observation was made.

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A standard editorial manager system is utilized for manuscript submission, review, editorial processing and tracking which can be securely accessed by the authors, reviewers and editors for monitoring and tracking the article processing. Manuscripts can be uploaded online at Editorial Tracking System (https://www.longdom.org/editorial-tracking/publisher.php) or forwarded to the Editorial Office at https://www.longdom.org/swarm-intelligence-evolutionary-computation.html The Journals includes around 150Abstracts and 100 Keynote speakers have given their valuable words. The meet has provided a great scope for interaction of professionals including in addition to clinical experts and top-level pathologists and scientists from around the globe, on a single platform.

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International journal of swarm intelligence and evolutionary computation

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