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What is machine learning (ML)? A complete guide

Machine learning is a form of artificial intelligence that allows computers to learn and make decisions on their own by analyzing data, much like humans do. It imitates human learning by making predictions that can be used as inputs for other tasks. Language translation, search engines, and even self-driving cars are all powered by machine learning. But that’s not all — machine learning has much more to offer.

What is machine learning (ML)? A complete guide

Table of Contents

Table of Contents

What is machine learning?

Machine learning definition

Machine learning (ML) is a type of artificial intelligence (AI) that mimics human learning. It enables software to learn independently and make more accurate predictions without needing explicit programming for each task. Machine learning improves its predictions by using all the data it has ever received.

Machine learning is used a lot in daily life, for example, to recommend TV shows based on what you liked before, fix your typos, or find the best route to work based on your and other people’s traffic data.

While ML is a subset of AI, deep learning (DL) is a further specialized subset of ML. Now, let’s explore the differences and connections between machine learning, deep learning, and AI.

Machine learning vs. deep learning vs. AI

While the terms machine learning, deep learning, and AI are often used interchangeably, they’re not synonyms. AI is the broad concept of machines carrying out tasks in a way that we consider smart. ML is a subset of AI that uses algorithms and statistical models to enable machines to improve at tasks with experience. DL is a further subset of ML that mimics the neural networks in the human brain, analyzing complex patterns in large amounts of data and creating sophisticated systems that can make decisions on their own.

Machine learning needs human intervention (think the augmented intelligence approach), whereas deep learning can evaluate the results independently and decide if they’re satisfactory. While it might sound like science fiction, futurists’ predictions for 2025 say that we can go even further than that.

Machine learning (ML)Artificial intelligence (AI)Deep learning (DL)
DefinitionA subset of AI that enables systems to learn from data.A broad field of computer science focused on creating intelligent systems. A subset of ML that uses neural networks for learning.
FunctionalityUses algorithms to learn and make predictions.Mimics human intelligence to perform tasks.Uses multi-layered neural networks for complex tasks.
ExamplesRecommendations for TV shows to watch, typo correction, route planning.Chatbots, autonomous vehicles, and game-playing AIs.Image and speech recognition, advanced natural language processing.
ComplexityGenerally more complex than basic AI algorithms.Varies from simple to highly complex systems.The most complex systems, requiring significant computational power.
Data dependencyHighly data-dependent for training models.Can function with rule-based or data-driven approaches.Requires large amounts of data for training.
Learning approachPrimarily statistical and data-driven learning.Can include rule-based, symbolic, or statistical learning.Deep neural networks with multiple layers.
Use casesSpecific tasks such as predictions and classifications.Broad applications including decision-making systems.Specialized tasks like image and voice recognition.
Development requirementRequires knowledge of data science and statistics.Requires significant expertise in AI principles.Requires expertise in neural networks and high computational resources.
Machine learning vs AI vs Deep learning

Why is machine learning important?

Machine learning is important because we’re already used to its benefits, such as using search engines that provide relevant results instantly. But ML keeps developing and affecting even more areas of our lives and jobs:

  • Automation of tasks. Customer service chatbots can handle routine inquiries, so human agents can focus on more complex issues.
  • Accurate predictions. Weather forecasting systems use ML to predict weather conditions more accurately by analyzing historical weather data.
  • Personalization. Streaming services recommend movies and TV shows based on your viewing history and preferences.
  • Efficiency and speed. Financial trading algorithms analyze market data and execute trades within milliseconds, much faster than a human could.
  • Scalability. E-commerce platforms use ML to manage inventory and logistics for thousands of products and millions of customers.
  • Continuous improvement. Voice assistants become better at understanding accents and speech patterns as they interact with more users over time.
  • Stronger security. Banks use ML to monitor transactions and detect fraudulent activities because ML tools are great at recognizing unusual patterns.
  • Driving innovation. Automated investment advisors provide personalized investment advice by analyzing financial data and market trends, while computer vision tools assist in diagnosing diseases by analyzing medical images and patient records. Machine learning is also used in self-driving cars to analyze sensor data and make real-time decisions for safe navigation and obstacle avoidance.

How does machine learning work?

Understanding how machine learning works helps you get a better idea of how everyday technologies, like recommendation systems and voice assistants, make decisions and improve over time.

Basically, machine learning works by using algorithms to analyze and learn from data. Here’s a breakdown of the process:

  • Data collection. The first step is for a data scientist or engineer to gather relevant training data that comes from various sources like sensors, databases, or user interactions.
  • Data preparation. Then, engineers clean and organize the training data, removing any errors or inconsistencies to make it suitable for analysis.
  • Choosing a model. Specialists then select a type of machine learning model (e.g., linear regression, decision trees, neural networks, etc.) based on the problem at hand.
  • Training the model. Engineers train the model using a portion of the data. During training, the model learns patterns and relationships within the data.
  • Evaluating the model. Specialists test the model on new data to evaluate its performance and accuracy.
  • Tuning. They fine-tune and adjust the model to improve its accuracy and performance based on the evaluation results.
  • Deployment. Once the machine learning model is performing well, the data scientist deploys it to make predictions or decisions on new, unseen data.
  • Monitoring and updating. Scientists continuously monitor the performance of their machine learning models and update them as new data becomes available.
Machine learning workflow

Machine learning models use algorithms to learn from data and make predictions or decisions. These algorithms define the rules and processes for how the machine learning models interpret and analyze data.

Machine learning algorithms

Machine learning algorithms are the methods or procedures that enable computers to learn from and make predictions or decisions based on data. Each machine learning algorithm is designed for specific types of tasks and data. Among the numerous machine learning algorithms, these are the main ones:

  • Linear regression predicts a continuous value (like house prices) based on input variables. It finds the best-fit line through the data points. Real estate companies use linear regression to predict the selling price of a house based on factors such as square footage, number of bedrooms, location, and age of the property. The algorithm finds the best-fit line through the data points to make predictions about house prices.
  • Logistic regression predicts a binary outcome (yes/no, true/false). It uses a logistic function to model the probability of the outcome. In healthcare, logistic regression can be used to predict the likelihood of a patient having a certain disease, such as diabetes, based on input variables like age, weight, blood sugar levels, and family history. The model comes up with a probability that can be interpreted as a binary outcome — disease or no disease.
  • Clustering algorithms can identify patterns in data so that it can be grouped. They can identify differences between data items that humans have overlooked. Retail companies use clustering algorithms to group customers based on their purchasing behavior, demographic information, and browsing patterns. This way they can identify distinct customer segments and adapt their targeted marketing strategies.
  • Decision trees classify data into different categories. They split the data into branches based on the values of input variables until they reach a decision. Banks and financial institutions use decision trees to classify loan applicants into different risk categories based on factors such as credit score, income, employment history, and debt-to-income ratio. The tree splits data into branches, assessing credit risk, until it reaches a decision.
  • Random forests improve the accuracy of decision trees by using multiple trees — they build many decision trees and combine their results. Financial institutions use random forests to detect fraudulent transactions. By building multiple decision trees and combining their results, the algorithm identifies suspicious activities based on transaction history, amount, location, and user behavior.
  • K-nearest neighbors (KNN) classify data points based on their proximity to other data points. Online streaming services like Netflix use KNN to recommend movies or TV shows to users. The algorithm looks at the closest users with similar viewing histories and suggests content those users have enjoyed.
  • Support vector machines (SVMs) classify data into different categories by finding the best boundary that separates them. In computer vision, SVMs can classify images into different categories. For example, an SVM can be used to identify and separate images of cats and dogs by finding the best boundary that separates the two categories based on features like edges, shapes, and colors.
  • Naive Bayes classifies data based on probabilities. It uses Bayes’ theorem to predict the category of a data point. Email services use Naive Bayes classifiers to filter spam emails. The algorithm uses Bayes’ theorem to predict the probability that an email is spam based on features such as the presence of certain words, the sender’s address, and the email’s structure.
  • K-means clustering groups data into clusters based on similarity. It assigns data points to ‘k’ clusters by minimizing the distance between points and cluster centers. Marketing businesses use K-means clustering to group consumers into clusters based on purchasing behavior and preferences. This helps them to develop tailored marketing campaigns for each cluster.
  • Principal component analysis (PCA) reduces the number of variables in the data while preserving as much information as possible. It transforms the data into a new set of variables (principal components) that are uncorrelated. PCA is used in image processing to reduce the number of variables (pixels) in an image while keeping as much information as possible, so it’s useful for compressing and storing images.
  • Artificial neural networks process audio and visual data through layers of interconnected nodes (neurons) to recognize and interpret spoken language and images. For example, virtual assistants like Siri and Alexa use artificial neural networks for speech recognition.

Types of machine learning

Machine learning can be categorized into several types, including supervised, unsupervised, semi-supervised, and reinforcement learning. They each offer different ways for analyzing and interpreting data.

Machine learning types

Supervised machine learning

Supervised machine learning involves presenting an algorithm with both input data and desired output data so it can be trained to make predictions. The algorithm analyzes the data, discovers patterns, and gradually learns how to correlate input data with output data. Once trained, it can work independently and fulfill its intended purpose.

The learning process for the algorithm doesn’t stop here. The algorithm continues to discover new patterns while analyzing incoming data.

Supervised machine learning is widely used in spam detection tools because it effectively classifies emails as spam or not spam. When training these ML tools, data scientists feed them with labeled data, where each email is tagged as “spam” or “not spam.” The features of the training data might also include the continent of the email, subject line, sender’s email address, and frequency of certain keywords.

Unsupervised machine learning

Unsupervised machine learning algorithms don’t need human intervention because they can find patterns in data themselves. This ability allows them to perform more complex and versatile tasks than supervised learning. However, unsupervised learning algorithms are less accurate.

Unsupervised learning is used in customer segmentation. Businesses use these ML algorithms to group customers based on their purchasing behavior for targeted marketing purposes. The data the algorithm works on includes purchase history, browsing patterns, demographic information, and consumer interactions with products.

Semi-supervised machine learning

As the name suggests, semi-supervised machine learning combines elements of both supervised and unsupervised learning. It uses a mix of labeled and unlabeled data so a model can learn and make predictions for new data entries.

Semi-supervised learning is often used when there isn’t enough labeled data for a purely supervised learning approach. However, the limited amount of labeled data can result in less trustworthy outcomes.

Semi-supervised machine learning is commonly used for speech recognition. The algorithm is presented with a small set of labeled audio recordings with corresponding transcriptions and a large set of unlabeled audio recordings. The labeled data helps train the ML model, while the unlabeled data helps improve its accuracy and generalization.

Reinforcement learning

Reinforcement learning is a type of machine learning where a model gets rewards or penalties based on its actions. It’s up to a model to figure out how to get more rewards and fulfill its tasks. Reinforcement learning algorithms solve complex problems and are not used for simple tasks. By trying different methods to solve a problem, the model eventually finds one that maximizes its reward.

Reinforcement learning is widely applied in the field of autonomous driving to train self-driving vehicles to navigate and make decisions on the road. The ML model is trained by applying an algorithm to a dataset that includes information from cameras, LiDAR, radar, and GPS. The autonomous car receives rewards or penalties based on its actions, such as staying in the lane, avoiding collisions, and following traffic signals.

What is machine learning (ML) used for in real life?

Machine learning is behind most services we use today. Businesses adopt machine learning because it can find hidden patterns in data and improve their services without requiring changes in their existing code.

Take Facebook ads as an example. If you’re an avid hiker interested in camping gear and the latest GPS tracking gadgets, you will most definitely receive outdoor-related ads. Machine learning analyzes your browsing history, the websites you visit, and the people you follow on Facebook to provide you with relevant ads. This analysis of your behavior highly increases the chances that you will make a purchase.

As soon as you adopt a new hobby or search for something online you haven’t searched for before, machine learning immediately starts to target you with different ads. It constantly analyzes changes in your behavior, attempting to provide you with the ads you’re most likely to click on and driving revenue to services.

Here are some well-known examples of machine learning applications in day-to-day life:

  • Recommended TV shows on streaming platforms. Streaming services analyze what you watch and suggest similar shows you might like.
  • The auto-correct feature. Many different applications and devices offer the auto-correct function, which works by analyzing what you’re typing and suggesting corrections.
  • Fraud detection in online banking. Machine learning can detect fraud and prevent identity theft. When a suspicious activity is detected, your bank may freeze your account. This might include you logging in to your account from a remote country or making purchases the bank has flagged as seeming suspicious.
  • Virtual personal assistants. Virtual assistants like Siri or Alexa analyze the information users provide them, compare it with previous data, and perform various tasks.
  • Chatbots and virtual friends. ML-based tools can generate human-like text responses and engage users in natural conversations.
  • Traffic prediction apps. Google Maps, Waze, and other similar apps analyze traffic data and suggest the fastest route.
  • Suggested friends on social networks. The algorithm analyzes your location, friends, interests, age, and workplace and presents you with a list of people you might know.
  • Spam filters. Artificial intelligence in cybersecurity is also widely applied. For instance, this technology can support email phishing detection. When machine learning is provided with examples of spam or phishing emails, it can prevent similar emails from reaching your inbox.
  • Sports watches. These types of gadgets track your activities, monitor your heart rate, and notify you about such factors as your progress or rest time.
  • Facial recognition. Machine learning can analyze biometric data and identify people by comparing their faces in a database.
  • Recommended songs on Spotify. Machine learning analyzes the music genres and artists you listen to and provides you with a weekly playlist of music you might like.

Machine learning: How to get started

Starting with machine learning technology can feel overwhelming, but with the right steps, you can dive into it and benefit from it. Here’s a simple guide to get you on the right track:

Understand the basics

Before jumping into machine learning programs, make sure you understand foundational topics like statistics and linear algebra. Learning a traditional programming language like Python is also very beneficial. Python is a great choice due to its simplicity and the numerous libraries available. Consider taking online courses or reading reputable books in these areas to build a strong foundation.

Choose the right tools

It’s important to choose the right tools. Python, along with libraries such as NumPy, Pandas, and Scikit-learn, is easy to use and popular in the machine learning community. You can learn to use these tools through online tutorials and courses on platforms like DataCamp.

Learn machine learning algorithms

Once you have a handle on the basics, start learning about different machine learning algorithms. Begin with simple ones like linear regression and decision trees, then gradually move to more complex algorithms like neural networks.

Work on projects

Start applying what you’ve learned by taking on some projects. Begin with simple projects like predicting real estate prices or classifying rose species. Once you feel ready, take on more complex projects. You can find project ideas online that match your skill level.

Stay up-to-date

The field of machine learning is developing fast, so keep yourself updated by following relevant blogs, joining online communities, or attending conferences. Train your machine learning skills regularly and listen to podcasts, webinars, and live training sessions to keep yourself informed about the latest trends and developments.

By following these steps, you’ll build a solid foundation in machine learning and prepare yourself to build machine learning models in the future.

Ethical challenges of machine learning

Machine learning tools offer incredible potential, but they also come with ethical challenges you should be aware of.


Using machine learning systems for human surveillance is a controversial topic. Many cities use facial recognition software to monitor public spaces and identify criminals. However, privacy activists have raised concerns about its accuracy and whether it’s ethical to monitor people in this way.

Lack of transparency

AI still lacks decent regulation and international laws. We can’t be sure how AI technologies, including machine learning tools, are used and who’s collecting our private data. It can even serve malicious purposes and benefit various threat actors.

Uneven power distribution

Big companies have more resources to adopt AI and push their competitors out of the market. Computing experts agree that those who own AI technology are a few steps ahead of everyone else.

The potential spread of inaccurate information

Limiting the resources that AI can learn from might result in AI hallucinations or false information presented as facts. Without double-checking its accuracy, people might continue spreading misleading information.


Businesses collect a lot of real-world data about us, from browsing habits to location (just take a look at what Google knows about you). While they claim this information is needed to provide users with the best possible experience, we can’t be sure how our data is stored and who can access it. Since data breaches happen every day, data collection makes us all vulnerable.

Final thoughts: Machine learning and cybersecurity

Machine learning is a form of artificial intelligence that enables computers to learn from data and make decisions independently, mimicking human learning. It powers various applications such as language translation, search engines, and self-driving cars. From healthcare and finance to transportation and entertainment, machine learning algorithms are everywhere you look.

Machine learning is not just a buzzword — it’s a powerful tool that’s impacting our daily lives and work. However, it also presents ethical challenges, such as privacy concerns and cybersecurity issues, including the potential misuse of personal data and vulnerabilities to data breaches. We should all understand these risks as we continue to use machine learning in more aspects of our lives.