What is Machine Learning? Learn About It in Simple Terms

In this paper, we’ll answer the question: What is machine learning? An aspect of artificial intelligence (ML) is solving problems in today’s world. From face and voice recognition to DNA sequencing, you can feel the impact. We’ll take you right from the history of machine learning to what the future holds.

A Brief History of Machine Learning (ML)

1949: Donald Hebb created a model of brain cell interaction. This model became the basis for machine learning.

1952: Arthur Samuel coined the term (ML). He also developed the first computer program for playing checkers

1957: Frank Rosenblatt created “The Perceptron” at Cornell Aeronautical Laboratory. Perceptron was the first artificial neural network.

1967: Conception of the nearest neighbor algorithm. This algorithm was for pattern recognition.

The late 1970s and Early 1980s: Machine learning separated from Artificial Intelligence

Nothing much took place in the late 1980s and 1990s. However, in 1990, the concept of boosting was introduced, which helped in the evolution of (ML). Boosting algorithms is a technique that converts weak learners to strong learners. Thereby, reduces bias in supervised learning.

21st Century

  • Speech Recognition

2007: Long Short-Term Memory is a deep learning algorithm which helps in speech recognition training. In 2007, it outshone more traditional speech learning programs.

2015: Google’s speech-recognition program had a significant leap.

  • Face Recognition

2006: Face Recognition Grand Challenge tested different facial recognition algorithms, including iris images, 3D face scans. Some of the algorithms recognized more faces than humans.

2012: Google’s X Lab developed an (ML) algorithm that could scan and find cats’ videos.

2014: Introduction of Facebook’s DeepFace. It can recognize faces in photographs with the same precision as humans. Some of the significant developments in the industry are a result of (ML). For instance, (ML) algorithms are helping in these ways:

  • Product recommendations
  • Natural language processing
  • Computational finance
  • Flexible pricing system
  • Mobile experience personalization

What is Machine Learning (ML)?

Firstly, (ML) is a subset of artificial intelligence. It refers to how a system analyzes data and performs tasks automatically through prediction. Stanford University describes (ML) as “the science of getting computers to act without being explicitly programmed.”(ML) enables computer systems to find insights from data and experience. In other words, as you feed the computer with data, it learns from it and performs better the next time.

Furthermore, we can answer the question, what is machine learning by defining its characteristics. So, we can say (ML) is a system that uses pattern recognition to solve problems. When you present an (ML) application with new data, it relearns and improves. If you can answer What is Machine Learning? You are ready for the next part.

How Does Machine Learning Work?

The machine learning process starts by making a model, which predicts the (ML) system. After setting the model, an initial input is provided to the system to learn from it. The data provided to the learning system is called training data. The training data input could be known or unknown. The learning process starts with inputting training data into a selected algorithm.

After that, a new set of data is provided to the system to test if the algorithm works. The result is checked with the prediction. You repeat this process is multiple times until you get the desired output. The following section of What is machine learning discusses the two different types. That will help you further understand how machine learning works.Machine-Learning

What are the Main Types of Machine Learning (ML)?

There are two main types of machine learning: supervised and unsupervised. Supervised (ML): This type teaches the machine with a large amount of labeled or known data. During training, you expose the system to millions of data for mastery. This system is vast due to the number of exposed datasets. Hence, supervised (ML) makes predictions based on evidence.

There are different applications of supervised learning. For example, speech recognition, spam detection, object recognition. Unsupervised Learning: In contrast to supervised learning, the training data here is unknown. Unsupervised learning finds hidden patterns and relationships in a dataset by creating clusters. Then puts them in similar groups and categories. For instance, when you present different fruits to the algorithm. It looks for similarities among the fruits, then put them in groups, without singling them out. So, you may have groups of apple, orange, banana, etc. The next section explains why machine learning is important.

Why is Machine Learning Important?

Machine learning models can analyze complex data and give accurate details. By using algorithms to work on a large amount of data, many industries are making better decisions.

Some of these industries are:

  • Finance 

(ML) helps bank and financial sectors in different ways, like fraud prevention and risk management. There’s also algorithmic trading for fast trading decisions. Another advantage of (ML) in the industry is portfolio management.

  • Health 

What is machine learning doing for health care? To start with, medical experts can analyze the data of their patients and make a correct diagnosis. Also, medical image analysis is better with (ML). The outbreak of epidemics and several infectious diseases is possible with (ML). Furthermore, you can see the production of wearable devices that help analyze a person’s health.

  • Ecommerce 

What is machine learning doing for the retail industry? From your previous searches and purchased items, websites can recommend products for you. Business owners use (ML) to personalize their customer’s shopping experiences or optimize prices. Also, it serves as a guide for marketing campaigns, ad-targeting, etc. Other industries using (ML) include manufacturing, travel and hospitability, energy, etc. Next, let’s look at the differences between machine learning and artificial intelligence.

What is the Difference Between Artificial Intelligence and Machine Learning?

In the first part of this article, where we answered, What is machine learning? States the difference. (ML) is only a subfield of artificial intelligence (AI). In other words, one of the methods of achieving artificial intelligence is machine learning. The origin of AI goes back to the 1950s. AI refers to the ability of machines to simulate human intelligence.

Artificial intelligence is wide-ranged. Also, Some of the traits in AI systems are reasoning, planning, perception, problem-solving, manipulation. Besides (ML), some other branches of AI are expert systems, robotics, and evolutionary computation. We have experts working in different fields of AI. For example, (ML) engineers.

Another area where people get confused is deep learning vs. machine learning. Deep Learning is a subfield of (ML), as (ML) is a subfield of artificial intelligence. Here you will be capable of learning extra about artificial intelligence.

Uses of Machine Learning

We see different machine learning uses in our day to day life. Besides, Some (ML) applications include Email spam filtering, ads targeting, newsfeed personalization, stock valuation, face, and speech recognition. These instances and more are applications of (ML).

Also, (ML) helps analyze large data sets faster and efficiently. Before (ML), data analysis was tedious and impossible. But, by developing the right algorithm, you will get accurate results regardless of data size. Business models are changing too. Predictive analysis is helping organizations serve their customers better.

For example, product and service recommendations are getting more personalized. Your recommended videos on Youtube will be different from your friend’s. This difference is a result of data analysis from your most-watched videos. Data mining, big data, and all are more popular than ever, thanks to (ML).


The Future of (ML)

As artificial intelligence progresses and becomes more prominent, machine learning—deep learning power, most of the advanced AI applications. Big companies like Google, IBM, Amazon are using machine-learning embedded applications and services.

Other sectors, including the government, are also interested in data collection and model building. So, we can say it only goes forward from here. The future of machine learning would involve a larger-scale application of data. We will see more plausible developments and even more competition for data.

Advantages and Disadvantages of Machine Learning

The different use cases stated in this article highlights the advantages of machine learning. Nevertheless, it has its disadvantages. Some of the advantages we see range from improved security, product developments to personalized user experiences. However, Companies use (ML) in marketing spending, customer service, and increased productivity. Thus, (ML) spans across different industries.

However, it also comes with disadvantages. Firstly, (ML) consumes resources. You need to pay high salaries to data scientists who oversee the project. The software in use is also expensive. (ML) trains on large data sets. Often, these data are biased. When this happens, you may end up with errors. Or even still, run into regulatory problems.

The Bottom Line

Machine learning aims to have computer systems that work without human assistance. To build systems that can learn from their experiences and make necessary adjustments. You can appreciate how the tech industry is achieving this aim. We saw the model of brain cell interaction become a building block for today’s (ML).

Many industries are adopting (ML) to work on large data sets. This adoption results in an improved experience for humans in different ways, from health to finance fields.

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Phyllis Olson

Upon completing her university education at the University of Cambridge, Phyllis acquires a job at Microsoft to work in an innovation department. It took her merely a year before she quits the job and starts working as a writer. Her work did not majorly revolve around writing but she delivers innovative apps that help people in real life. Phyllis Olson is among the chief consultant in the online networks on the innovations about the app and devises developments. She reviews the latest apps and highlights everything to know before subscribing to an app.

In 2015, Phyllis Olson started delivering the apps' news. Her writings are dependable if you want timely reports about apps development. She also delves into prudent upgrades you should look out for in the old apps. Interestingly, Phyllis believes she is at the epitome of her career, while many graduates like her will have enjoyed working in famous companies. She answers a curious journalist that writing is something that many people will overlook, not realizing how it impacts those people!

Phyllis hopes to upgrade her work by increasing the volume of innovations and writings about the apps. She believes, reviewing the apps from other innovators is the only way to improve her abilities and knowledge.

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