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Recent developments in AI (Artificial Intelligence) technology have led to many breakthroughs and exponential growth for machines. The extent to which the entire world now relies on machines knows no bounds. In fact, at this point, AI solutions are not just a key investment opportunity for large corporations but also a major contributor towards addressing countless day-to-day problems in our lives.

A key subset of AI is machine learning, often simply known as ML. It is only due to the invaluable work that researchers and scientists put into the foundations of ML that we are now capable of harvesting maximum performance from highly competent AI-based technologies.

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In this article, we will talk about how, over the years, humans have made machines capable of intelligence, i.e., the ability to mimic the human thought process and make decisions based on experiences.

Before we talk about the different methodologies using which humans teach machines to behave like humans, let us go over the basic definition of machine learning.

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Machine learning is the method via which humans teach machines to learn from a set of historical data and enable them to perform certain actions in the future based on their past learning. Machine learning is a combination of many things, from computer algorithms and data analytics to mathematics and statistics. It is the technology that the construction of artificially intelligent systems heavily relies on.

The process of making machines learn from historical data is known as training.

The science of machine learning revolves around teaching the machine by using datasets of different sizes composed of useful or random facts and/or figures and feeding them to the machine. The essence of this activity is to help the machine observe the data, establish meaningful connections between the different pieces of the supplied information, and prepare to make decisions about incoming data by incorporating these pre-established connections, also known as rules.

Machine learning models often follow one or more of the following primary training methods.

For the initial training, we use a dataset where the input and/or expected output may or may not be clearly defined. The process of training utilizes training data. Once the machine has been trained, it is fed test data to find out whether the machine has learned from the training dataset or not.

Let us go over each of these training methods in a tad more detail and explore how they are used to make machines smarter.

This type of machine learning algorithm makes use of a dataset that contains labeled data. It means you tell the machine what each item is. This way, we can theoretically pre-define the rules and all that the machine has to do is study the existing mappings and learn these rules.

We can further split supervised learning algorithms into two sub-types, classification, and regression.

Classification: This method is employed when the machine has to be trained to answer in binary terms, such as yes-no, good-bad, or true-false. The training data consists of items that have already been classified into various categories. For each category, the machine studies each item closely and identifies characteristics that are common for all the items within that category. This allows the machine to build relationships between items and their respective categories. It uses these rules to identify items in the test data and correctly classify them.

Regression: The regression model is employed when you need predictions in terms of numeric values, such as housing prices or temperatures. The training dataset contains multiple variables along with outputs that may or may not be dependent on said variables. The machine studies the input variables and figures out how, if at all, each variable affects the value of the output, leading to pattern recognition or the development of rules. For the test data, the machine uses these rules to calculate an estimate or a predicted value for the output.

The key difference between supervised and unsupervised learning is that the items are not labeled in the dataset used for the latter. Let us use an example to demonstrate this in a better manner.

Let us say that you want a machine to be able to classify the items in a dataset containing images of different types of gardening tools, such as trowels, shovels, rakes, and spades.

Under supervised learning, your training data would contain images along with their identifiers. For example, if you are inputting the image of a spade, you will tell the machine that it is a spade. The machine will then study all the spades and their common features to learn how to identify a spade in the future.

However, if you use the unsupervised learning model, you would input pictures of all sorts of gardening tools without labeling them. For example, if you input a picture of a spade, you will not tell the machine that it is a spade. The machine will have to figure out on its own how each image may (or may not) be related to the ones before it, and then put similar images into one category. Thus, the machine learns to form categories on its own without being explicitly told what the categories are. This type of training model works well for datasets where structures or patterns might not be apparent to the average human.

The third prominent method is based on the concept of reinforcement, which some of you might be familiar with if you have ever taken a Psychology 101 course. If you have ever tried to teach your dog some cool tricks by motivating it with treats, you have made use of the reward system.

Unlike the first two methods, this model relies greatly on feedback. For each decision made by the machine, you tell the machine the correct output so that it can figure out whether it made a good or bad prediction. Through repeated trial-and-error, the machine becomes increasingly accurate.

A simple real-world example of reinforcement learning can be seen in the display of online ads. The machine can determine which ads are more successful and worth showing based on how many people click on it. If the machine gets more clicks (higher reward) on a certain ad from a particular target group, it will know that the decision to display that ad to that group was a good one.

While some people seem determined on trying to settle the humans vs machines debate once and for all, others believe that this type of comparison is futile. The fact remains that the human being came first, and the machine followed. As long as our passion for growth and our need for perfection is alive, machine learning algorithms will continue to improve and become increasingly accurate, helping us achieve seemingly impossible success and accuracy rates.

Ralf Llanasas is a digital marketing expert and freelance writer. Has graduated with abachelor's degree in Information Technology, he mostly writes topics related to marketing, technology, and SaaS trends. His writingcan be seen in several publications aimed at the IT industry. He is also into photography and loves taking pictures when he is free.

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