Have you ever wondered how the “People you may know” feature on Facebook always provides you with an accurate list of people you know in real life and can connect with on Facebook? How does Facebook find out about this, and how does it provide you with these recommendations? Machine learning is the answer to this question. But what is machine learning?
What is machine learning?
Machine learning, which is a subset of artificial intelligence and a field of computer science, focuses on the analysis and interpretation of data patterns and structures. Simply, machine learning is a field of computer science that allows a machine to learn independently without being explicitly programmed.
So basically, what happens is that instead of the code being written for a new problem each time, the algorithm is fed with data. Then it analyzes the data and provides recommendations and decisions based only on the input data without human intervention.
The thing to note here is that machine learning algorithms can learn from their past experiences just like humans! When new data is fed to them, these algorithms learn, change, and grow without ever having to change the code. The results obtained may not be very accurate at first, but the machine learning algorithm can use its output data to improve the results in the future.
A brief history of machine learning
You may think that machine learning is a relatively new topic, but the concept of machine learning was introduced in 1950; When Alan Turing (the main character of The Imitation Game ) wrote an essay answering the question, “Can machines think?”
In 1957, Frank Rosenblatt designed the first neural network for computers, now known as the perceptron model. In 1959, Bernard Vidro and Marcian Hoff developed two neural network models, ADALINE, which could recognize binary patterns, and MADALINE, and remove echoes on telephone lines.
In 1967, the Nearest Neighbor algorithm was written, allowing computers to use rudimentary pattern recognition. In 1981, Gerald de Jong proposed the concept of explanation-based learning, in which a computer analyzes data and creates a general rule for discarding irrelevant information.
During the 1990s, work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began to create programs for computers to analyze large amounts of data, draw conclusions, and learn from the results. Finally, over time and after numerous improvements, machine learning was presented in its present form.
The importance of machine learning
It is very difficult to imagine any industrial activity without the use of machine learning or artificial intelligence. Machine learning is important because of its wide range of applications and its incredible ability to adapt and provide fast, effective, and optimal solutions to complex problems.
Machine learning has enabled the rapid and automated generation of models that can analyze larger and more complex data and provide faster and more accurate results, even on a very large scale. By creating accurate models, there is a greater chance of identifying profitable opportunities or avoiding unknown risks.
How do machine learning algorithms work?
Machine learning algorithms use different techniques to manage large volumes of complex data to make decisions. These algorithms do the job of learning from data with specific inputs given to the machine. Understanding how these algorithms and machine learning systems work is very important to know how to use them in the future.
It all starts with training the algorithm using the training data set to create a model. The algorithm then provides a prediction for the new input data. Predictions and results are evaluated for accuracy. If the prediction is not as expected, the algorithm is trained repeatedly until the desired output is obtained. This enables the machine learning algorithm to learn independently and produce an optimal response that gradually increases in accuracy over time. After achieving the desired level of accuracy, the machine learning algorithm is applied.
Let’s check how machine learning works with a simple example:
When you search for “Lion images” on Google, Google will provide you with relevant results. But how does Google do it?
- Google first receives a large number of image samples (datasets) labeled “Lion.”
- The algorithm then looks for pixel patterns and color patterns that help it predict whether the image is a “Lion” or not.
- At first, Google’s computers randomly guess what patterns are suitable for recognizing the “Lion” image.
- If the algorithm makes a mistake, a series of changes are made to get it right.
- Finally, a large computer system modeled after the human brain learns from this set of patterns. After training, this system can correctly identify “Lion” images and provide accurate results.
If you are responsible for building a machine-learning algorithm for image recognition between images of lions and tigers, how do you do this?
The first step, as explained above, is to collect a large number of photos labeled “Lion” for lions and “Tiger” for tigers. The computer must then be trained to look for patterns in images to identify images of lions and tigers. Once the machine learning model is trained, we can feed it different images to see if it can correctly distinguish between images of lions and tigers. A trained machine learning model can correctly recognize such requests.
“What is machine learning, and how it works?” is now more familiar to us. So, let’s dig a little deeper and explore the different types of machine learning.
Types of machine learning
Machine learning is often classified based on how the algorithm learns. There are four main approaches: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each of these methods has a specific purpose. The type of algorithm that data scientists choose depends on the type of data they want to predict.
Supervised learning
In supervised learning, data scientists train the algorithm with labeled data and the variables that they want the algorithm to evaluate. Once the model is trained with a set of known (labeled) data, the model is fed unknown (unlabeled) data to get a new response.
Supervised machine learning requires data scientists to train the algorithm with both labeled inputs and desired outputs.
Supervised learning algorithms are suitable for the following tasks:
- Binary classification: dividing data into two categories
- Multiclass classification: choosing between more than two classes
- Regression Modeling: predicting continuous values
- Ensemble classification: combining predictions from multiple machine learning models to produce an accurate prediction
Unsupervised learning
This type of machine learning involves algorithms that learn using unlabeled data. Unsupervised machine learning algorithms examine unlabeled data in search of patterns that can be used to group data points into subsets.
One thing to note is that unsupervised learning cannot add labels to the data. For example, it can’t tell if it’s a group of lemons or strawberries, but it separates all lemons from strawberries.
Unsupervised learning algorithms are suitable for the following tasks:
- Clustering: dividing data sets into groups based on similarity
- Anomaly detection: identifying unusual data points in a data set
- Dependency exploration: Identifying sets of items in a data set that often occur together.
- Dimensionality reduction: reducing the number of variables in a data set
Semi-supervised learning
This approach is a combination of supervised learning (with labeled training data) and unsupervised learning (without labeled training data). In semi-supervised learning, only a small number of input data are labeled.
In semi-supervised machine learning, the model is first trained using labeled data. Unlabeled data is then fed to the model. The model labels unlabeled data with some accuracy. These data are called pseudo-labeled data. Finally, the combination of pseudo-labeled data and original labeled data is used to improve the accuracy of the model.
Today, there is a huge amount of data in various industries. Many large companies have already collected millions of terabytes of data and are still collecting it. But tagging the collected data requires a lot of manpower and resources and is therefore very expensive. So many real databases fall into this category.
Some of the fields in which semi-supervised learning is used are:
- Machine translation: teaching language translation algorithms
- Fraud detection: identifying fraud cases when there are only a few positive samples
- Data Labeling: algorithms trained on small data sets can learn to apply data labels to larger data sets automatically
Reinforcement learning
Data scientists typically use reinforcement machine learning to perform a multi-step process with specific rules. Data scientists program the algorithm to do a task and provide it with positive or negative signals while it is trying to do it. When the model predicts or produces an outcome, it is penalized if the prediction is wrong and rewarded if it is correct and trains its model accordingly.
Some of the fields in which reinforcement learning is used are:
- Robotics: robots can learn to perform real-world tasks using this technique.
- Video games: reinforcement learning has been used to train robots to play many video games.
- Resource management: given limited resources and specific goals, reinforcement learning can help companies plan how to allocate resources
The importance of interpretability of ML
Explaining how a complex machine-learning model works can be very challenging. In some industries, data scientists must use simple models; Because explaining how decisions are made for that business is essential. Sophisticated models can provide accurate predictions, but explaining how the output is determined is difficult for a layperson.
Machine learning applications
Machine learning is everywhere. You may use it many times in your daily life and not even know about it. Here are some examples of machine learning applications that you may encounter every day:
- Image recognition: Image recognition is one of the most common applications of machine learning. This technology enables computers and systems to derive meaningful information from visual input.
- Automatic speech recognition: Automatic speech recognition is used to convert speech to text. Its applications are in authenticating users based on their voice and performing tasks based on human voice input.
- Recommender Engines: Machine learning is widely used by various e-commerce and entertainment companies to recommend products to the user. Machine learning algorithms understand the user’s interest by using the data of the user’s past consumption behavior and suggest products to them accordingly.
- Customer relationship management: Customer relationship management software can use machine learning models to analyze email and advise sales team members to respond to the most important messages first. More advanced systems can even recommend effective responses.
- HR Information Systems: HR systems can use machine learning models to filter job applications and identify the best candidates for a job position.
- Fraud detection: Machine learning algorithms are excellent at detecting fraud and money laundering activities by monitoring each user’s activities and evaluating whether an action is typical for that user.
- Email Spam Filter: Email Spam Filter uses a supervised machine learning model to filter spam emails from your mailbox.
- Automated stock trading: Since there is always a risk of stocks going up and down in the stock market, machine learning is widely used in stock market trading to predict stock market patterns. Machine learning enables trading platforms to execute thousands or even millions of trades per day without human intervention.
- Diagnosis of diseases: machine learning has become a crucial part of the health and treatment field. ML algorithms can be used to diagnose diseases, determine the best course of treatment, help with more accurate diagnoses, etc. It is also capable of creating three-dimensional models in order to determine the location of lesions in the brain accurately.
The future of ML
Machine learning solutions continue to make significant changes to core business processes and become more common in our daily lives. Currently, many companies have started using machine learning because of its high potential for more accurate predictions and business decisions. The global machine-learning market is expected to grow from USD 8.43 billion in 2019 to USD 117.19 billion by 2027.
As new technologies continue to develop, more efficient machine learning algorithms can be used. For example, with the development of GPUs in the future, it will be possible to calculate larger data at a faster rate.
Machine learning also has great research potential. Currently, one of the hot topics in research articles in the field of computer science is machine learning and it is used in various industries and fields. With this speed and increased market penetration, machine learning will have a bright future.
