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Decoding the Buzzwords: AI, ML, Data Mining, and Deep Learning Explained


Artificial Intelligence (AI) is one of the most talked about subjects in our world today. While some people are fascinated by its possibilities, others express fear about the potential harm it can be used for. But what exactly is AI, and how does it differ from Machine Learning (ML), Deep Learning, and Data Mining?

Artificial Intelligence (AI)

AI is one of the most talked about subjects in our world today. From the songs and movies that are recommended to us, to the way we shop or browse the internet, AI is shaping our daily experiences. It influences all sectors from healthcare to finance to education and beyond. While some people are fascinated by its possibilities, others express fear about the potential harm it can be used for. Yet, whether you are fascinated or fearful, a clear understanding of AI and its related terms—Machine Learning (ML), Deep Learning, and Data Mining—is crucial for anyone navigating today’s digital landscape.

Here is a simple breakdown:

  1. Thinking Like Humans: This version of AI tries to replicate ways of human thinking. For example, when a computer plays a game of chess, it “thinks” several moves ahead just as humans aim to do.
  2. Act Like Humans: These AI systems are built to mimic human behaviors. An example of this is a customer service chat “bot” that interacts with users in a way that mimics human conversations.
  3. Thinking Rationally: This type of AI aims to use pure logic to make decisions. It processes information rationally and provides a result.
  4. Acting Rationally: An AI system in this category aims to take the best possible action in each situation. An example of such a system is self-driving cars. It does not necessarily drive “like a human,” but rather, they choose the best decision based on logic and data.

Examples of Artificial Intelligence

  • Personal Assistants: Siri, Alexa, and Google Assistant. These digital assistants use AI to understand voice commands and provide relevant responses or actions.
  • Video Games: Modern video games often have AI-driven non-player characters that can interact with players in varied and sophisticated ways, mimicking human-like behavior.
  • Recommendation Systems: When Netflix suggests a movie or when Spotify recommends a song, AI analyzes your behavior and preferences to make these recommendations.

Machine Learning

Machine Learning is a subgroup of AI that focuses on giving computers the ability to learn. This is done by creating, training, testing, and refining ML models. These models are trained using a variety of algorithms including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning to name a few. Each algorithm has its own strengths and weaknesses and is better suited for certain problems or tasks. It is important to note that there is currently no one-size-fits-all type of algorithm (Mahesh, 2020). ML models “learn” by iterating through a dataset. Each iteration provides a slight modification to the model which aims to reduce error and improve accuracy over time. After the model has been trained, it is evaluated using previously unseen data. The model’s performance on this new data will determine whether the model is acceptable or if more training or a different approach is needed. 

Examples of Machine Learning

  • Email Filtering: Services like Gmail use ML to categorize your emails, determining which ones are likely spam and which are important based on your past interactions.
  • Credit Scoring: Financial institutions use ML to predict the probability of a potential borrower defaulting on a loan based on their credit history.
  • Predictive Text: When you type on your smartphone and it suggests the next word, it’s using ML based on patterns of how you and others generally complete certain phrases.

Data Mining

Data Mining is a process that shares similarities with ML. Like ML algorithms, Data Mining aims to find patterns in data. However, the goal of Data Mining is to extract meaningful new information from a dataset, while ML focuses on utilizing its learned patterns from training data to make predictions (Jackson, 2002). Additionally, an ML model learns and refines its performance by iterating through the data, while Data Mining uses specific programming methods and algorithms. To put it simply, think of ML as teaching a computer to make decisions based on data, while Data Mining is about discovering hidden insights within that data which are often unknown previously or not immediately obvious.

Examples of Data Mining

  • Market Basket Analysis: Supermarkets use data mining to understand which products are often bought together. This is why certain products might be placed together in aisles or bundled in sales.
  • Healthcare: Medical researchers might mine data to find patterns in biometric information to predict or diagnose illnesses.
  • Customer Segmentation: Companies analyze purchase histories and customer behaviors to group their clientele into segments for targeted marketing.

Deep Learning

Deep Learning is a subset of ML. It focuses on making accurate data-driven decisions. It does this by finding and extracting patterns from datasets. It is particularly suited for tasks where the dataset consists of a vast amount of complex data such as images or natural language (Kelleher, 2019). Deep Learning uses neural networks with multiple processing layers. The depth of layers is what gives Deep Learning its name. Each layer is made up of interconnected nodes or “neurons.” Each node learns a small, simple function whose output becomes the input for the neurons in the next layer. This process continues until the final output is reached. Through this process, Deep Learning can extract features autonomously, which allows it to handle more complex tasks. This is an improvement from the manual extraction needed with traditional ML. 

Examples of Deep Learning

  • Image Recognition: When you upload a photo to Facebook, it automatically identifies and tags your friends, that’s deep learning in action.
  • Voice Assistants’ Understanding: The capability of Siri or Alexa to understand your voice, with all its nuances and inflections, comes from deep learning algorithms trained on vast datasets of human speech.
  • Medical Imaging: In medicine, deep learning is used to read and interpret complex images like X-rays or MRI scans, sometimes spotting issues faster or more accurately than the human eye.
Diagram depicting the relationship between artificial intelligence, machine learning, deep learning, and data mining.


As we can see, AI, ML, Deep Learning, and Data Mining are all interrelated but different disciplines. Defining AI is not easy, as there are disagreements about what constitutes intelligence. As a result, the current field of AI can be described as a combination of separate disciplines that unite under the AI category (Wang, 2019). AI is the broad umbrella that ML, Deep Learning, and Data Mining are under. However, a common goal for these systems is to convert input into useful output, such as data insights, accurate predictions, or actions taken.


As AI systems increasingly integrate with our daily lives, it becomes increasingly important to understand its components. While AI, ML, Data Mining, and Deep Learning are interconnected, they each have distinct purposes and offer different possibilities. As these technologies continue to advance, we may see more drastic transformations in the way we work, play, and live. By understanding the nuances and potential applications, we become better equipped to harness their benefits and address potential challenges.

Glossary of Terms

  • Artificial Intelligence (AI): A field of computer science that aims to create machines capable of mimicking cognitive functions such as learning, problem-solving, and decision-making.
  • Machine Learning (ML): A subset of AI where algorithms allow computers to learn from and make decisions based on data. Rather than being explicitly programmed to perform a task, the machine uses data and algorithms to derive patterns and make decisions.
  • Deep Learning: A specific subset of ML that utilizes neural networks with many layers (hence “deep”) to analyze various factors of data. It is especially good at processing vast amounts of complex data like images or natural language.
  • Data Mining: The practice of examining large databases to generate new information. It involves exploring data to find consistent patterns or systematic relationships and then validating the findings by applying the detected patterns to new data sets.
  • Neural Network: A computational model inspired by the structure of biological neural networks. It’s designed to recognize patterns in data through training.
  • Supervised Learning: A type of Machine Learning where the algorithm is trained on labeled data, meaning the algorithm is provided with input-output pairs.
  • Unsupervised Learning: A type of Machine Learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.
  • Semi-Supervised Learning: A middle ground between supervised and unsupervised learning. Uses both labeled and unlabeled data for training.
  • Reinforcement Learning: A type of Machine Learning where an agent learns to behave in an environment by performing actions and receiving rewards or penalties.
  • Algorithms: A set of rules or processes used for calculations or problem-solving, especially by a computer.
  • Predictive Text: Technology that suggests words to users while they input text.
  • Recommendation Systems: Algorithms used by online platforms to provide users with content (like movies, music, or products) based on their past behaviors and preferences.


  • Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1-37.
  • Kok, J. N., Boers, E. J., Kosters, W. A., Van der Putten, P., & Poel, M. (2009). Artificial intelligence: definition, trends, techniques, and cases. Artificial intelligence, 1, 270-299.
  • Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9(1), 381-386.
  • Jackson, J. (2002). Data mining; a conceptual overview. Communications of the Association for Information Systems, 8(1), 19.
  • Kelleher, J. D. (2019). Deep learning. MIT Press.

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