Machine Learning: Definition, Types, Advantages & More

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When talking about artificial intelligence, it is inevitable to mention machine learning, one of its most essential branches. 

This article is an introduction to the field of machine learning. We’ll cover what machine learning is, types, advantages, and many other interesting facts. 

What Exactly Is Machine Learning?

The pioneer of AI Arthur Samuel defined machine learning as “the field of study that gives computers the ability to learn without explicitly being programmed.”

Machine learning (ML) is a discipline of artificial intelligence (AI) and helps machines imitate intelligent human behavior. It teaches computers to learn from data, identify patterns, and make predictions on their own.

Machine learning models are used to solve complex problems by examining data in a way that  human would and they do it with ever-increasing accuracy. 

Video source: YouTube/Simplilearn

Machine Learning vs. Artificial Intelligence

Artificial intelligence refers to the general ability of computers to imitate human behavior and perform tasks while machine learning refers to the algorithms and technologies that enable systems to analyze data and make predictions.

To sum up, AI is the broader concept of creating intelligent machines while machine learning refers to the application of AI that helps computers learn from data without being programmed. 

Machine Learning vs. Deep Learning

Deep learning is a subset of machine learning. Like machine machine, it also involves the ability of machines to learn from data but uses artificial neural networks to imitate the learning process of a human brain

A neural network refers to a computer system modeled after the human brain and biological neural networks. In deep learning, algorithms are created exactly like machine learning but have many more layers of algorithms collectively called neural networks. 

Why Is Machine Learning Important?

Machine learning is used by companies to support various business operations. Due to its ability to predict customer behavior and, therefore, a better user experience, it facilitates the development and offering of new products.

There are two essential reasons for machine learning’s importance:

  1. It enables processing a vast amount of data quickly without using human resources – the machine performs the entire operation, recognizes patterns, and draws conclusions.
  2. Machine learning algorithms update without human intervention whenever their predictions are incorrect, leading to continuous refinement and improvement.

How Machine Learning Works

Machine learning allows computers learn to program themselves through experience

The process starts by gathering data, whether it’s numbers, images or text. This is the so-called training data and the more data is gathered, the better the program will be.

The next step is choosing a machine learning model and supplying the data.  The computer model will then learn to identify patterns and make predictions. 

The operation of a machine learning system can be divided into three main phases:

  1. The decision-making process: The algorithms b try to find a pattern based on which they will perform a classification or predict a potential outcome.
  2. Error function. The algorithms checks prediction accuracy by comparing them with similar examples.
  3. Optimization or updating process. In the third stage, the algorithm analyzes and evaluates the error level, then updates the decision-making process to make the error minor in subsequent cases.

Think of music apps. 

Music apps recommend music you might like based on your previous selections. The songs you’ve listened to, artists, and genres are input data aka parameters that the algorithm gives weight to, and based on it, evaluates what new music to suggest to you. 

If it suggests tracks you like, the weight of each parameter remains the same, because they led to the correct prediction of the outcome. If it offers the music you don’t like, the parameters are changed to make the following prediction more accurate.

The ML algorithm updates itself every time it makes a mistake and, thus, without human intervention, it becomes more analytically accurate.

Types of Machine Learning with Examples

Machine learning is categorized based on how the algorithm learns to become more accurate in its predictions. This classification divides machine learning into four categories, and the type of data that needs to be predicted determines which category scientists will apply:

Supervised Learning 

Supervised learning uses pre-labeled datasets to train an algorithm to classify data or predict results. After entering the input data, the algorithm assigns them a value, which it then adjusts according to the results achieved by trial and error method.

An example of supervised learning is the classification of spam mail that goes into a separate folder where it doesn’t bother the users.

Unsupervised Learning 

Unsupervised learning uses unlabeled datasets, and the algorithm scans, identifies and classifies them without human intervention.

The unsupervised learning method successfully reveals similarities and differences between data sets, which makes it helpful in data analysis, customer targeting, image and pattern recognition, and many other fields.

Semi-Supervised Learning 

Semi-supervised learning combines supervised and unsupervised learning since it contains both types of datasets: labeled and unlabeled.

During the training, semi-supervised learning uses a repeating pattern in the small labeled dataset to classify bigger unlabeled data. This type of ML helps with data labeling.

Reinforcement Learning

works on a slightly different principle than the previous three. It is used to solve a task that consists of several steps, for the solution of which there are clearly defined rules.

The algorithm is programmed to solve the task, but it takes the appropriate steps, while the data scientists guide it with positive and negative reviews on each step. IBM Watson, which won the Jeopardy competition, is an excellent example of reinforcement learning.

The Advantages of Machine Learning 

The wide application of ML speaks in favor of its clear advantages:

  • ML is efficient in pattern-finding. Machine learning, in a short time, notices patterns and repetitions in massive data sets, which would take humans significantly more time, and even then, it is not sure that they would notice the patterns.
  • ML work is automated and does not require human intervention. Thanks to ML, you no longer have to supervise your project at every stage. Giving computers the ability to learn enables them to make predictions and enhance algorithms. 
  • ML algorithms are ever-evolving. Machine learning algorithms become more accurate as they gain experience. They can consequently make wiser selections. Algorithms become faster at making more accurate predictions as the data set expands.
  • ML can work with different types of data – labeled or unlabeled, visual or textual, ML algorithms can handle them.
  • Wide application. Various industries can use ML algorithms in multiple sectors: marketing, eCommerce, finance, healthcare, agriculture, and many others. Regardless of the industry in which they are applied, ML algorithms enable better targeting and recommendations, which improves the entire user experience.
Video source: YouTube/Infinia ML

The Disadvantages of Machine Learning 

Machine learning is an advanced technology, however, it has certain disadvantages:

  • ML is limited by the amount of information obtained: Machine learning is not based on human knowledge, which is inexhaustible, but on the data it receives from data scientists. Therefore, ML is still far from mimicking human intelligence.
  • ML algorithms are difficult to train: the process is time-consuming, expensive, and requires large databases. Only 4% of data training scientists report that they didn’t have difficulties during this process. Problems occur due to data errors, unusable data formats, and the inability to label data.
  • ML exact working methods are difficult to identify: Machine learning systems work independently, making decisions, and if you notice irregular work, there is no way to know why it happened. All you can do is start the process from the beginning, training the algorithms with a new set of data.

Machine learning is a complex process, prone to errors due to a number of factors. One of them is it requires a large amount of training data to notice patterns and differences. If data is lacking, the results will be inaccurate or biased.

Additionally, idata can be too simple or too complex for ML algorithms. This problem can be solved, but doing so will take a lot of effort and time as scientists must classify valid and unuseful data.

Despite these challenges, ML generally provides high-accuracy results, which is why this technology is valued, sought after, and represented in all business spheres. However, the implementation of data is time-consuming and requires constant monitoring to ensure that the output is relevant and of high quality.

Examples of Machine Learning Applications

Generally, machine learning is included in most AI applications. For instance, lets consider the following, which you probably use every day without realizing that this cutting-edge technology benefits you:

  • Speech recognition: Voice assistants, such as iPhone Siri and Amazons Alexa, use speech recognition enabled by machine learning algorithms. ML uses natural language processing (NLP) to transform human speech into a written text.
  • Computer vision: This technology allows computers to understand visual content, such as photos, infographics, and videos. 
  • Customer services: Chatbots answer FAQs and guide customers through a website’s service for an unbothered user experience. Chatbots as customer support are used across businesses and industries such eCommerce, healthcare, and banking.
  • Recommendations algorithms: ML learns from data patterns, making it ideal for product recommendations in the eCommerce industry.
  • Fraud detection and prevention: ML is highly efficient in analyzing datasets and finding irregularities, which is helpful in finance industries as it helps detect fraud. While scanning data, ML algorithms can detect suspicious activities and inform the persons in charge to take necessary action.
Video source: YouTube/TECH IN 5 MINUTES

Machine Learning: Key Takeaways

Machine learning is a branch of artificial intelligence that enables machines to imitate intelligent human behavior. 

There are four types of machine learning: 

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning

Although machine learning algorithms have existed for decades, they got the spotlight they deserve with the popularization of artificial intelligence. Their advantages outweigh their disadvantages, which is why ML has been and will remain an essential part of AI. 

Continuous development of the machine learning technology will lead to overcoming its challenges and further increase its representation in the future. 

With so many possibilities machine learning already offers, businesses of all sizes can benefit from it. 

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.