What is Machine Learning: concept, types, distinction with Artificial Intelligence (AI) and Deep Learning (DL)

It's hard to imagine our daily lives without artificial intelligence. It does everything: it works as your voice assistant, creates content, manages your social media feed, controls your home vacuum cleaner, and even makes sure that your data is not fraudulently used. Most people are aware of the existence of artificial intelligence and use it almost every day, but not everyone is familiar with how it works and the distinctions between its subfields. One of the most important subfields of artificial intelligence is machine learning (ML), a technology that allows computers to learn from their own experience and make choices without direct programming or human intervention.

In this blog post, we will talk about the specifics of ML, its types, application in various fields, and differences from other AI modules.

AI vs ML vs. Deep Learning

Artificial intelligence (AI) has become a buzzword in recent years, literally becoming a word for any type of intelligent machine. In fact, there are two more subtypes of AI - machine learning and deep learning - which are different terms with subtle differences. 

Artificial intelligence is a broad area of computer science to create machines with the ability to perform tasks that require human intelligence. A few such tasks include problem solving, decision making, natural language processing, and pattern recognition. AI encompasses no one paradigm - it includes anything from rule-based automation to sophisticated self-improving algorithms. Essentially, AI can be divided into two types:

• Narrow AI is a specialized system for performing a specific task, such as face detection or spam detection.

• General AI is a hypothetical system with human intelligence that can solve problems and make decisions in an integrated manner over a broad field of applications.

AI as such is a very wide category encompassing machine learning and deep learning, both being different levels of automation and complexity.

If you recall school logic, it can be described as follows: 

All deep learning is machine learning, and all machine learning is artificial intelligence, but not all artificial intelligence is machine learning.

Machine learning, deep learning, and artificial intelligence are associated but distinct areas. Artificial intelligence is the most broad term for intelligent systems. Machine learning is a subdomain of AI which allows computers to learn from information, while deep learning is an advanced form of AI that utilizes neural networks in order to scrutinize complex patterns.

Gaining insight into the differences assists in understanding how the technologies based on AI operate and where they should be implemented across fields like health care, money management, and autonomous systems. As technology becomes more advanced, these sectors will further mold innovation and automation to come.

Machine learning

Machine learning (ML) is a branch of artificial intelligence (AI) that empowers computers and machines to mimic human learning. It enables them to perform tasks autonomously and enhances their performance and accuracy by learning from experience and gaining exposure to more data.

Machine learning is revolutionizing almost every industry. It helps logistics companies optimize delivery routes, enables retailers to customize the shopping environment, mechanizes work in factories, and even improves organizational security around the world. Even when you enter a voice command into Google on your smartphone, ML executes it, deciphering your command and providing useful answers. It finds its application in most processes, from small home-based ones to global ones in giant corporations.

The amount of data created every day is growing at an unprecedented rate. Without machine learning, it would be virtually impossible to process and make sense of this data, as processing such massive amounts of data is beyond human capacity. ML opens up new opportunities for individuals and businesses by solving such tasks as:

• Fraud detection and cybersecurity

• Personalized recommendations and customer service automation

• Sophisticated data analysis for smarter decision making

• Speech recognition, transcription and translation

• Advanced technologies such as self-driving cars, drones, and virtual reality

How does machine learning work?

Simply put, machine learning allows computers to learn and act like humans, improving on their own experience. Machine learning starts with data - whether it's numbers, images, text, or other forms of information such as bank transactions, reporting logs, sensor readings, or sales statistics. This data is collected together and prepared to serve as training material for a machine learning model. Generally, the more data available, the better the model's performance.

Once the data is ready, developers select a machine learning model and provide it with information that allows the system to detect patterns and make predictions. Over time, programmers can improve the model by changing its parameters, increasing its accuracy and efficiency.

Types of Machine Learning

Machine learning is a tool that allows computers to learn from data and predict new information. However, the algorithms are not the same, the approaches to ML are different, each designed to solve problems in a unique way. The main difference between them lies in the results they produce, as well as in the ways they verify and calibrate these results.

The main types of ML are supervised, unsupervised, and reinforcement learning:

Supervised learning

Supervised learning is the process of training an algorithm with a dataset that has labeled examples. Supervised learning associates each input data with an output label related to it, so the model learns to map the input to the output. Once trained, the model applies the learned pattern to forecast new and unseen data.

For example, suppose you have a small zoo of animals that you want the machine to identify. The machine first looks at the features of each animal, its shape, color, and features. It then compares them to the ones it has seen previously. If the features of a new animal look like those of a monkey, the machine predicts that it is a monkey.

For example, let's say you teach the machine by showing it different animals one by one:

• If the animal has a long tail, agile limbs, and looks like humans, it is labeled as a monkey.

• If the animal has furry ears, a round face, and gray hair, it is labeled koala.

• If the animal has a spotted eye, black and white, a round body, and chews bamboo, then it is a panda.

Now, after training, you introduce a new animal - for example, a koala — and ask the machine to categorize it.

As it has already learned from previous examples, it examines the characteristics of the animal - round face, fluffy ears, and gray fur — before it calls it a koala and places it in the Koala category with certainty. This shows how a machine learns from training data (a dataset of known animals) and generalizes the learned knowledge to new and unseen examples.

This learning is applied intensively to classification issues, in which the goal is to divide the input data into previously defined categories, and to regression issues, in which the goal is to predict continuous numerical values. The effectiveness of supervised learning heavily depends on the quantity and quality of labeled data used for training.

This approach is used in applications such as spam detection, where the model learns from previous labeled examples to distinguish between spam and non-spam. It is also very important in medical diagnosis, where patient information is used to predict disease. Supervised learning is also used in banks in assessing credit risk by predicting the likelihood of a borrower defaulting on a loan.

Key features: 

• Labeled data: the training data consists of input-output pairs where the right answers (labels) are already provided.

• Direct control: the algorithm receives feedback during learning by comparing predictions with the right outputs.

• Task-based learning: primarily used for classification (predicting categories) and regression (predicting continuous variables).

Unsupervised learning

As opposed to supervised learning, unsupervised learning works with raw, unstructured data that has no pre-existing labels. The algorithm finds this data on its own, looking for intrinsic patterns, relationships, and structures without explicit guidance. This makes it particularly useful in uncovering latent insights, clustering similar data points, and identifying anomalies.

For example, imagine you're presented with a machine learning model that has been trained on a large dataset of unsigned images that contain monkeys, koalas, and pandas and so on. The model has no pre-exposure to these animals and has no labels or categories in advance. The task of the system is to use unsupervised learning to identify and classify these animals in a new image that's never seen before.

Let us consider the case where the model is given a photo of monkeys, koalas, and pandas but without any idea of their characteristics. Since it has no idea what a monkey, koala, or panda is, it is not possible for it to directly classify them. However, from the patterns, similarities, and differences, the model is able to put the images in various clusters - a cluster of monkeys, a cluster of koalas, and a cluster of pandas.

Clustering and association are two primary techniques within unsupervised learning. Clustering organizes data into groups based on similarities, making it useful for market research and behavioral analysis. Association rule learning identifies relationships between different elements in a dataset, such as detecting products frequently bought together in retail. 

Here, no labeled data or categories are used to train the model. It clusters the images based on the patterns that have been seen, illustrating how unsupervised learning reveals hidden structure in unlabeled data.

A common application of unsupervised learning is customer segmentation, through which organizations divide customers into groups based on their buying patterns without prior knowledge of previously established categories. Another important application is anomaly detection and is commonly implemented in fraud management systems to identify transactions that lie outside the typical pattern. Unsupervised learning is also implemented in recommendation systems such as Netflix and Spotify with a view to analyzing user preferences and delivering relevant output.

Key features:

• Unlabeled data: the model learns from raw and unstructured data without knowing the correct outcome.

• Self-recognition: the model identifies patterns, associations, and clusters in the data.

• Exploratory analysis is widely used for segmentation, pattern recognition, and identifying anomalies.

Reinforcement learning

Reinforcement learning is contrasted with supervised and unsupervised learning because it is based on interaction with the environment rather than a known set of data. Here, the agent learns by acting in the environment and receiving feedback in the form of reward or punishment. The goal is to improve the decision-making process over time and cumulatively maximize the reward by repeating experiments.

For example, imagine that an agent is trained to recognize animals in photos using reinforcement learning. But initially, the model doesn't know what a monkey is — it learns by trial and error, guessing and receiving feedback.

AI is given images of monkeys without any references or labels. It generates random answers. If it first calls a panda, it is incorrect, and the agent receives incorrect feedback and discards this result. The second attempt is a koala, and again, it's wrong. When the agent gives the monkey, it gets approval and the correct algorithm.

Every time it makes a wrong prediction, it learns from it and adjusts its approach. With a few images and a few attempts, it starts to learn monkey characteristics such as body shape, facial structure, and fur type until it starts to correctly identify monkeys and no longer makes random guesses.

This approach is mostly used in industries that require adaptive learning and frequent decision-making. For example, self-driving cars use reinforcement learning to navigate roads by constantly learning the environment. In the gaming industry, AI algorithms have mastered complex games, such as chess, with improved strategy through repeated play. Robotics also uses reinforcement learning to teach a machine to move objects, balance, and perform various functions through trial and error.

The learning process within reinforcement learning can be divided into two types: model-free and model-based. Model-free methods allow an agent to learn solely from experience without having a defined model of the environment. Model-based methods, on the other hand, allow the agent to build an internal model of the environment, which leads to more efficient decision-making.

Key features:

• Interaction-based learning: the agent continues to interact with the environment rather than learning from fixed data.

• Reward and punishment: the agent learns its strategy by finding actions that maximize rewards.

• Consistent decision-making: RL is best suited for areas where actions affect subsequent steps.

Thus, supervised learning involves the use of labeled data and is used for prediction-based tasks, unsupervised learning finds hidden patterns in unlabeled data, and reinforcement learning improves the decision-making process through trial and error. Understanding these types of learning helps you choose the right approach to a particular problem, utilizing the strengths of each method to build efficient and effective machine learning models.

Deep learning

Deep learning is one particular kind of ML that employs artificial neural networks to process information and make complex judgments. The networks are designed to mirror the structure of the human brain, which consists of numerous layers of interconnected neurons that improve their understanding as they receive greater amounts of information.

Deep learning is best optimized for processing high amounts of unstructured data, e.g., images, audio, text. Some of the well-known forms of deep learning models are:

• Feed-forward neural networks (FNNs) are the most basic type of neural network where the data passes through only in a single direction among neuron layers.

• Recurrent neural networks (RNNs) are used for sequential data, such as speech and time series, as they remember past inputs.

• Long Short-Term Memory (LSTM) is a more advanced form of RNN that focuses on longer sequences being stored in memory, used for tasks such as language translation.

• Convolutional Neural Networks (CNNs) are designed for image recognition, CNNs break down images into smaller patterns before rebuilding them.

• Generative Adversarial Networks (GANs) are two competing networks, GANs are used to create realistic images, videos, and even music.

Deep learning is more computationally intensive than traditional ML methods and requires large data sets for training. It allows learning features automatically, reducing the need for human involvement in setting the parameters of the data set.

Use cases across industries

Machine learning technology is improving various industries by using data to improve decision-making, productivity, and customer satisfaction. It is transforming business processes and customer experience in healthcare, finance, marketing, autonomous vehicles, retail, and energy:

1. Machine learning in healthcare

Machine learning in healthcare assists healthcare professionals in treating patients and interpreting clinical data, improving decision-making and reducing risks. For example, it assists in the early detection of diseases by analyzing patient information and medical images, improves the accuracy of tumor detection in X-rays and mammograms, helps in drug development by selecting promising candidates, and automates patient interactions with virtual assistants who schedule appointments and provide initial medical recommendations.

1. Machine learning in finance

In the financial sector, ML turns data into action, using it to detect scams by monitoring transaction history for potential fraud. It speeds up loan origination by assessing creditworthiness based on borrower information, facilitates algorithmic trading using sophisticated market trend analysis, and offers customized investment recommendations using AI-powered robo-advisors.

1. Machine learning in marketing and advertising

In marketing and advertising, machine learning automates digital marketing processes and improves decision-making. Machine learning allows targeted advertising with personalized ads based on user interaction, recommending content on platforms such as Netflix and Spotify based on user likes and dislikes, adjusting product prices in real time based on market demand, and providing real-time customer support with chatbots that handle product and information queries.

1. Machine learning in retail

In business, machine learning improves operations and enhances customer service. It predicts product demand for efficient inventory optimization, dynamically sets prices based on market conditions, selects specific customer segments for certain marketing efforts, and optimizes checkouts with self-service kiosks equipped with artificial intelligence and computer vision.

1. Machine learning in the energy sector

Finally, in the energy sector, machine learning maximizes resource utilization and efficiency by predicting renewable energy production, performing predictive maintenance on equipment to prevent breakdowns, improving smart grids by monitoring electricity consumption in real time, and maximizing building energy efficiency by tracking occupancy and building usage patterns.

1. Machine learning in everyday life 

Machine learning enriches everyday life - from facial recognition, which verifies faces and tags photos, to product recommendations to regular users when it learns customer purchases to predict purchases. Machine learning provides email automation and spam blocking, identifying patterns to reduce the number of unwanted emails. Voice-to-text and text-to-speech capabilities improve communication by getting used to voice features, identifying language, and adapting sentences, improving the fluidity of interaction and making the user's life easier.

Сonclusion

Machine learning (ML), artificial intelligence (AI), and deep learning (DL) are transforming industries by increasing efficiency, automating high-level tasks, and making data-driven decisions. While AI is a general term for machines that emulate human intelligence, ML refines it by enabling systems to learn and improve from data without the need for direct instruction. Deep learning, an extension of ML, goes one step further, using neural networks to process huge amounts of unstructured data and transform industries such as healthcare, finance, and autonomous vehicles. Each of these technologies has a distinct purpose, but together they are driving innovation in many areas.