February 16, 2022
Author : Julia Miles

Machine learning is a feature of artificial intelligence applications that enables learning new features based on user activity and preferences. In layman language, it is a form of artificial intelligence that many software are built with to ensure the software's high user-friendly rate and personalisation. It helps the software predict the user's choices and provide suggestions to enhance their experience. Machine learning is a useful attribute because it occurs with experience and as the users spend time using the software without explicitly programming it. Various software, applications, and even the OTT platforms we are addicted to using machine learning to give suggestions for users to increase user interaction time on their platform.

Is Machine Learning Useful?

It is indeed very useful for the marketing perspective as it allows enterprises to learn the ongoing and latest trends among their customers. Machine learning increases user interaction on a website or platform and helps maintain it easily by learning the user preference and activity pattern. Also, it allows the user to receive the most accurate results and better services based on their likes and dislikes. Many popular websites and platforms such as Facebook, Instagram, Netflix and even Google use machine learning to enhance user experience and maintain traffic on their website. These websites are popular in the first place; it is useful to stay updated with the latest trends.

Netflix uses a machine-learning algorithm called the
'recommendation engine' to show movies, shows and
original content for users to access. It uses the user's
watch history, preferred genres, the cast and crew of
the movie/show that the user watched, and the watch
history of other users with similar preferences to
generate the best suggestions for the viewers.

which is better AI vs ML

What Are the Different Machine Learning Methods?

Supervised Learning

The user inputs or supplies the required information for supervised machine learning to train the software or application algorithm to get the desired output. The input information is well labelled, with each variable defined meticulously to get specific information and results. The supervised learning method is time-consuming as it requires computation time for each problem and can be inefficient for big data.

Face detection in tech devices is a prominent
example of supervised machine learning.Electronic
devices can detect, capture, save, and analyse a
person's facial identity or features to unlock the
device with this technology. The user must input
their data in the algorithm to let the device recognise

Unsupervised Learning

Unsupervised machine learning is functional without labelled data to train the algorithm. The learning algorithm in this machine learning method allows access to data to seek connections and patterns in the user experience. The machine can develop patterns and suggestions without prior knowledge of the user's behaviour and preferences. It happens over time, and the machine can identify hidden connections in the user experience and offer insights.

Youtube uses unsupervised machine learning, user activity, search history, watch history, trends and interactions to curate suggestions for enhancing the user experience. It shows suggestions in search results, video recommendations, trends, channels, and notifications. Many users find it personalised and easy to access as their interests are on their main timeline. Such algorithms can also be deceiving as many user hours on youtube unintentionally.

Semi-Supervised Learning

Both labelled and unlabelled data is fed into the machine to train the AI to detect the user's behaviour patterns and connect. Semi-supervised machine learning is useful for maintaining learning accuracy. When the labelled data input in the system requires supervision or user input to elaborate variables in the algorithm, then semi-supervised learning is preferable.

The grammar and spell-check app Grammarly is the
most prominent example of semi-supervised
machine learning as it detects errors and suggests
improvements based on the labelled data in the cloud
algorithm. It shows many improvement suggestions,
but user input is required to make changes to the
document. The users train the algorithm and work
with the machine suggestions to curate the best

Reinforcement Learning

Such a form of learning is especially useful for data scientists. Scientists train the algorithm to complete multi-step tasks abiding by a specific set of rules. The user inputs positive and negative outcomes of various steps and trains the algorithm to use the labelled data for each step and achieve the desired result.

Self-driving cars still in the trial phase use reinforcement
machine learning as the basic algorithm. It helps improve
trajectory improvement, motion sensors, dynamics planning,
and situational awareness for autonomous cars. Such cars
are still in the testing phase, but it might be the next big
innovation depending on the algorithm learning.

different machine learning methods

AI vs Machine Learning- Which is better?

Artificial Intelligence

  • It is very useful and time-efficient for completing length and repetitive tasks.
  • AI can assimilate huge amounts of data seamlessly and is useful for big-sized enterprises.
  • The most noteworthy feature of AI is imitating human intelligence and similar attributes. It collects necessary data, which is used for decision making and executing tasks.
  • Al has revolutionised the astronomy industry as it has revealed more and more hidden computational and hypothetical data about the universe that has helped many space missions.
  • Also, the AI-based healthcare software has made patient data processing very quick and time-efficient. Whether complex operations or small disease detection, AI helps detect the causes and cures for many diseases.

Machine Learning

  • Machine learning is useful for developing suggestions, patterns and connections for previously mentioned data.
  • Various business applications use machine learning for creating business models based on the nature and requirements of the business. It allows the user to train the software according to the requirements or let the machine curate possible connections based on the previously mentioned data.
  • The easy reconfigurability of ML enables the user to monitor labelled and unlabelled data to curate the most accurate desired results.
  • Various phone AI's like Siri and Google assistant use ML to detect, save, and provide personalised suggestions.
  • Social media applications or websites like Facebook and Instagram use similar machine learning features to learn the required data about users and offer suggestions to increase user engagement.

You can avail our IT Assignment Help in UK to get a deeper understanding of these two concepts and write your assessment answers. Be it Machine Learning or Artificial Intelligence; we are surrounded by devices that help ease our daily lives and enhance our user experience. Be it Amazon Echo or Apple's Siri; we must've felt a surge of happiness using such devices that know us, deliver the task on our command and show the best search results on the go. Humans thrive on easing their lives, and having such devices on the palm of our hands sure makes us feel powerful. But, the query that arises here is, is it safe to allow such algorithms to access and save our data? What about data breaches?

Over the years, we've all witnessed it through movies or real-life incidents (Facebook shut down their AI as their AI bots created a language of their own unknown to humans). The future of innovations involves AI on a deeper level, and as useful as the technology is, it is impossible to get rid of it now. But is there a way to make it safer? Can we control the data synchronisation in AI algorithms? The answer to that is machine learning; it can help us regulate the data that the machine can access and know. The security permissions on our devices are also of great help to avoid privacy and security issues. As the saying goes, anything in extremes can be dangerous; moderation is the key. So you need to find the moderation between ease of such a feature and depending too much on it.

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About the Author

Julia Miles

Julia Miles has been working with international students who are studying in United Kingdom since last 8 years after her post graduation. Being an international student herself, she decided to offer academic support and assistance to other students coming to United Kingdom universities. Her hold of the concepts and theories in the domain of Information Technology give her a broad base to approach the same topics. With My Assignment Services, she utilises her knowledge by providing personalised mentoring and assignment writing help. She also holds successful completion of CCNA, a certification course offered by Cisco in information technology.


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