Machine Learning
Machine
learning is everywhere around us, most people encounter it every day, there is
a huge number of real-world machine learning use cases for example:
· Voice
assistants such as Apple
Siri, Google assistant which are inside every one’s phones.
· Product
recommendations which
works by collecting data from our previous purchases and previous internet
activity to recommend the product most interesting to the aimed person
· Self-driving
cars, they constantly
identify surrounding objects and guide the car
· Fraud
detection, used by banks
to keep consumers safe.
and there
are many more use cases as the machine learning field is huge and it keeps
growing exponentially with each day.
Machine
learning uses two different techniques to work: supervised learning, which uses
labeled data to predict future outputs, and unsupervised learning, which digests
huge number of unlabeled data to find meaningful features and hidden patterns
(MathWorks, 2021). Additionally, the techniques themselves which machine
learning uses to work, are made up of different techniques such as classification
techniques and regression techniques for supervised learning, and clustering
for unsupervised learning (IBM, 2020).
References:
IBM, 2021.
What is Machine Learning?. [online] Ibm.com. Available at:
<https://www.ibm.com/cloud/learn/machine-learning> [Accessed 16 April
2021].
MathWorks,
2021. What Is Machine Learning? | How It Works, Techniques & Applications.
[online] Mathworks.com. Available at:
<https://www.mathworks.com/discovery/machine-learning.html> [Accessed 16
April 2021].
Computerizer, 2021. AI. [image] Available at: <https://pixabay.com/photos/robot-mech-machine-technology-2301646/> [Accessed 20 April 2021].
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