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describe about Machine learning

describe about Machine learning A36Dn
Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today.
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Because
of new computing technologies,
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machine
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learning
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today
is not like
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machine
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learning
of the past. It
was born
from pattern recognition and the theory that computers can learn without
being programmed
to perform specific tasks; researchers interested in artificial intelligence wanted to
This phrase/word (depending on the context) can be potentially replaced with :
see
if computers could learn from data. The iterative aspect of
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machine
You use this word too often, consider replacing it with
learning
is
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important
This phrase/word is too simple and can be replaced with:
because
as models
are exposed
to new data, they are able to
independently
adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new
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–
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but
one that has gained fresh momentum.

While
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many
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machine
You use this word too often, consider replacing it with
learning
algorithms have been around for a long time, the ability to
automatically
apply complex mathematical calculations to
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big
data
Consider using an m-dash if you do not want to join two words.
–
over and over, faster and faster
Consider using an m-dash if you do not want to join two words.
–
is a recent development. Here are a few
widely
publicized examples of
You use this word too often, consider replacing it with
machine
You use this word too often, consider replacing it with
learning
applications you may be familiar with:

The
heavily
hyped, self-driving Google car? The essence of
You use this word too often, consider replacing it with
machine
learning.

Online recommendation offers such as those from Amazon and Netflix?
You use this word too often, consider replacing it with
Machine
You use this word too often, consider replacing it with
learning
applications for everyday life.

Knowing what customers are saying about you on Twitter?
You use this word too often, consider replacing it with
Machine
You use this word too often, consider replacing it with
learning
combined with linguistic
This phrase/word (depending on the context) can be potentially replaced with :
rule
creation.

Fraud detection? One of the more obvious,
This phrase/word is too simple and can be replaced with:
important
This phrase/word (depending on the context) can be potentially replaced with :
uses
in our world
This phrase/word (depending on the context) can be potentially replaced with :
today
.
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IELTS essay describe about Machine learning

Essay
  American English
6 paragraphs
205 words
This writing has been penalized,
text can't be
less than 250 words in Task 2
and less than 150 words in Task 1
5.0
Overall Band Score
Coherence and Cohesion: 5.5
  • Structure your answers in logical paragraphs
  • ?
    One main idea per paragraph
  • Include an introduction and conclusion
  • Support main points with an explanation and then an example
  • Use cohesive linking words accurately and appropriately
  • Vary your linking phrases using synonyms
Lexical Resource: 5.5
  • Try to vary your vocabulary using accurate synonyms
  • Use less common question specific words that accurately convey meaning
  • Check your work for spelling and word formation mistakes
Grammatical Range: 5.5
  • Use a variety of complex and simple sentences
  • Check your writing for errors
Task Achievement: 5.0
  • Answer all parts of the question
  • ?
    Present relevant ideas
  • Fully explain these ideas
  • Support ideas with relevant, specific examples
Labels Descriptions
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    Currently is not available
  • Meet the criteria
  • Doesn't meet the criteria
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