What is Machine Learning in Artificial Intelligence and What are it’s applications?

Published by projectsflix on

What is Machine Learning?

Machine Learning is the use of computers to highlight the true meaning of the data, in order to convert the unordered data into useful information. It is a multi-disciplinary subject involving many disciplines such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. Specializing in how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. It is the core of artificial intelligence, and it is the fundamental way to make computers intelligent. Its application spans all fields of artificial intelligence. It mainly uses induction, synthesis rather than deduction.

Machine learning research significance:

Machine learning is a science of artificial intelligence. The main research object in this field is artificial intelligence, especially how to improve the performance of specific algorithms in empirical learning.” “Machine learning is the study of computer algorithms that can be automatically improved through experience” “Machine learning is the use of data or past experience to optimize the performance standards of computer programs. 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 in T, as measured by P, improves with experience E .

Machine learning has been widely used, such as: data mining, computer vision, natural language processing, biometrics, search engines, medical diagnosis, detection of credit card fraud, securities market analysis, DNA sequence sequencing, speech and handwriting recognition, strategy Games and robots.

Example of AI solving a problem with different approaches:

Problem Statement: Identify cats

Different approaches:

  1. Pattern recognition (official standard): People get a conclusion through a lot of experience, so that it is a cat.
  2. Machine learning (data learning): People learn by reading, observing that it will have small eyes, two ears, four legs, one tail, and get a conclusion to judge that it is a cat.
  3. Deep learning (in-depth data): People understand it by knowing it, and it is similar to similar cats and draws conclusions to judge that it is a cat. (Frequent areas of deep learning: speech recognition, image recognition)
  • Pattern recognition: Pattern recognition is the oldest (as a term, it can be said to be very obsolete).
    • We refer to the environment and the object as “patterns”. Recognition is a recognition of patterns, and how to make a computer program do something that looks “smart”.
    • By integrating wisdom and intuition, by building a program, identify something, not a person, for example: Identify numbers.
  • Machine learning: Machine learning is the most basic (one of the hot spots of current startups and research laboratories).
    • In the early 1990s, people began to realize a way to build pattern recognition algorithms more efficiently, which is to replace experts (people with a lot of image knowledge) with data (which can be obtained through cheap labor collection).
    • Machine learning” emphasizes that after inputting some data to a computer program (or machine), it must do something, that is, to learn the data, and the steps of this learning are clear.
    • Machine Learning is a discipline that specializes in how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance.
  • Deep learning: Deep learning is a very new and influential frontier, and we don’t even think about it – the post-deep learning era.
    • Deep learning is a new field in machine learning research. Its motivation is to build and simulate a neural network for human brain analysis and learning. It mimics the mechanism of the human brain to interpret data such as images, sounds and texts.

Uses of Machine Learning:

  • Search Engine: Optimize your next search results based on your search clicks. It is machine learning to help search engines determine which results are more suitable for you (and also determine which ads are right for you).
  • Spam: Automatically filters spam emails into the trash.
  • Supermarket Coupon: You will find that when you buy a child’s diaper, the salesperson will give you a coupon to redeem 6 cans of beer.
  • Post Office Mail: The handwriting software automatically identifies the address where the greeting card is sent.
  • Apply for a loan: Conduct a comprehensive assessment of your recent financial activity information to determine your eligibility.

Next:Supervised learning,Unsupervised learning and Reinforcement learning in Machinelearning


Sarah Leslie · January 21, 2021 at 8:16 pm

Thanks for sharing this resource. Understanding what exactly machine learning is, is crucial to our understanding of the potential for its applications. To apply machine learning to business, and to make impact through machine learning use cases, https://www.explorium.ai/usecases/, including lead scoring, improved fraud detection, anomaly detection for money laundering, and others. This is where the real power of machine learning lies.

What are Machine Learning Prerequisites and Machine Learning Terminologies for Beginners? - projectsflix · January 5, 2021 at 6:46 pm

[…] Also Read:What is Machine Learning in Artificial Intelligence and What are it’s applications? […]

Leave a Reply

Avatar placeholder

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.