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Artificial intelligence is the broad field of creating computer systems that perform tasks associated with human intelligence. Machine learning is a subset of AI in which systems learn patterns from data. Deep learning is a subset of machine learning based on multilayer artificial neural networks.
Generative AI overlaps these areas because modern generative models commonly use deep learning to create new text, images, audio, code and other content.
Understanding these terms makes it much easier to choose AI training and follow discussions about modern tools without treating every form of AI as the same technology.
AI vs machine learning vs deep learning at a glance
| Term | Meaning | Example |
|---|---|---|
| Artificial Intelligence | Broad field of creating intelligent computer behaviour | Expert systems, planning, vision, language systems |
| Machine Learning | AI techniques that learn patterns from data | Fraud detection, demand forecasting |
| Deep Learning | Machine learning using multilayer neural networks | Image recognition, large language models |
| Generative AI | AI designed to generate new content | ChatGPT, image generation, coding assistants |
What is artificial intelligence?
Artificial intelligence, or AI, is the broadest term.
AI systems can perform tasks such as:
- reasoning over information
- recognising images
- understanding language
- planning
- making recommendations
- predicting outcomes
- generating content
- controlling autonomous systems
Not every AI system learns from data in the same way. Historically, AI has included rule-based and symbolic techniques as well as machine learning.
What is machine learning?
Machine learning is a subset of AI that uses data and algorithms to learn patterns that support predictions or decisions.
Instead of a developer writing every rule explicitly, the system learns from examples.
For example, a fraud-detection model can be trained on previous transactions and outcomes. It learns patterns associated with suspicious activity and uses those patterns to assess new transactions.
What are the main types of machine learning?
Supervised learning
Supervised learning trains a model using labelled examples.
Examples include:
- predicting house prices from historical sales
- classifying emails as spam or legitimate
- predicting whether a customer is likely to leave
Unsupervised learning
Unsupervised learning looks for patterns in data without predefined outcome labels.
Clustering customers into groups based on behaviour is a common example.
Reinforcement learning
Reinforcement learning trains an agent through interaction with an environment, using rewards or penalties to improve behaviour.
It is used in areas such as robotics, optimisation and some game-playing systems.
What is deep learning?
Deep learning is a form of machine learning based on artificial neural networks with multiple processing layers.
Deep learning has become particularly important for complex and unstructured data such as:
- images
- speech
- video
- natural language
Microsoft describes deep learning as a subset of machine learning based on artificial neural networks whose multiple layers transform input data into increasingly useful representations.
What is a neural network?
An artificial neural network is a computational structure containing connected processing units organised into layers.
A simple network may have:
- an input layer
- one or more hidden layers
- an output layer
During training, the network adjusts internal parameters so its outputs become more useful for the task.
Modern large models can contain enormous numbers of parameters and many layers.
Why is deep learning called “deep”?
The word refers to the multiple layers used within the neural network.
Earlier machine-learning approaches often relied more heavily on humans designing which features should be extracted from data.
Deep learning can learn useful representations from raw or less structured data, which has contributed to major improvements in image recognition, speech and language processing.
Where does generative AI fit?
Generative AI is part of the wider AI field and modern generative systems commonly rely on deep learning.
Large language models are deep-learning models trained on large volumes of language data.
Image-generation models use other deep-learning architectures to generate or transform visual content.
See What Is Generative AI? for more detail.
AI vs machine learning
AI is the goal or broader field. Machine learning is one major collection of techniques used to build AI systems.
It is therefore correct to say:
Machine learning is AI, but not all AI is machine learning.
Rule-based expert systems are a classic example of AI approaches that do not learn patterns through modern machine learning.
Machine learning vs deep learning
Deep learning is a specialised form of machine learning.
Traditional machine-learning algorithms can perform extremely well on structured business data and may be simpler to train and explain.
Deep learning becomes particularly valuable where the data is complex or unstructured and very large datasets and computing resources are available.
Examples of traditional machine learning
- sales forecasting
- credit-risk prediction
- customer churn prediction
- fraud detection
- predictive maintenance
- customer segmentation
- demand forecasting
Examples of deep learning
- image recognition
- speech recognition
- language translation
- large language models
- advanced computer vision
- audio generation
- autonomous perception systems
Does machine learning always need huge amounts of data?
No. The amount of data required depends on the problem, algorithm and desired accuracy.
Some traditional machine-learning problems can work with relatively modest datasets if the data is representative and the task is well defined.
Large deep-learning systems often benefit from much larger training datasets and substantial computing power.
What is training data?
Training data is the information used to fit or train a machine-learning model.
Data quality has a major influence on model quality. Problems can arise from:
- incorrect labels
- missing information
- bias
- unrepresentative samples
- data leakage
- outdated patterns
AI projects therefore depend heavily on data preparation, governance and evaluation.
What is a model?
A model is the learned mathematical representation produced through training.
After training, the model can be used for inference, which means applying what it has learned to new input.
For example, a trained classification model can assess a new customer application and output a predicted category or probability.
What is inference?
Inference is the process of using a trained model to produce a result.
Training may require substantial computing resources, while inference uses the trained model to process new requests.
When you send a prompt to a deployed generative AI model, the model is performing inference.
What is computer vision?
Computer vision is an AI field focused on interpreting images and video.
Applications include:
- object detection
- medical imaging support
- quality inspection
- document processing
- facial analysis
Many modern computer-vision systems use deep learning.
What is natural language processing?
Natural language processing, or NLP, focuses on systems that work with human language.
Applications include:
- sentiment analysis
- translation
- information extraction
- question answering
- summarisation
- chatbots
Modern generative language models have significantly expanded NLP capabilities.
What is narrow AI?
Most AI used today is narrow or task-focused AI.
A system may perform one category of work extremely well without possessing general human-like intelligence across every domain.
Even flexible generative models operate within technical limitations and should not be assumed to have human understanding, judgement or accountability.
Do you need maths to learn AI?
It depends on the level.
Business users can learn AI concepts, prompting, governance and practical use without advanced mathematics.
Machine-learning engineers and data scientists need stronger foundations in statistics, probability, linear algebra and optimisation as they progress into model development.
Do you need Python?
Again, it depends on the role.
You do not need Python simply to understand AI or use business AI tools.
Python is widely used in data science, machine learning and AI engineering, so it becomes increasingly important for technical practitioners building and training models.
Which should you learn first?
If you are new to the subject, start with AI fundamentals.
A useful progression is:
- Understand AI, machine learning, deep learning and generative AI terminology.
- Explore common business use cases and limitations.
- Learn responsible AI and governance.
- Choose a role-based path.
- Business users can focus on Copilot and generative AI.
- Data professionals can progress into machine learning and analytics.
- Developers can learn AI application development and agents.
- AI engineers can deepen knowledge of models, RAG, evaluation and cloud AI services.
Frequently asked questions
Is ChatGPT machine learning?
ChatGPT is a generative AI application built using large language models, which are developed using deep-learning techniques. Deep learning is itself a form of machine learning.
Is all machine learning AI?
Machine learning is generally considered a subset of artificial intelligence.
Is deep learning better than machine learning?
Deep learning is not automatically better. Simpler machine-learning methods may be more suitable for structured datasets, limited data, interpretability requirements or lower computing cost.
Is generative AI the same as deep learning?
No. Generative AI describes systems designed to generate content. Modern generative AI commonly uses deep learning, but the terms refer to different concepts.
What should a business manager learn about AI?
Managers should understand capabilities, limitations, data risks, responsible use, governance and how to identify realistic business use cases. They do not necessarily need to build models themselves.
Which AI course is best for a beginner?
A fundamentals-level course is usually the best starting point before moving into generative AI, machine learning or platform-specific development.
Develop your AI skills with ExperTrain
ExperTrain offers instructor-led Artificial Intelligence training covering AI fundamentals, generative AI, machine learning, Copilot, cloud AI and responsible adoption.
Microsoft Azure AI Fundamentals (AI-900) provides an accessible introduction to machine learning, generative AI and other AI workloads.
Those wanting a broader vendor-neutral foundation can explore EXIN BCS Artificial Intelligence Foundation.
You can also explore the AI Certification Pathways page and the ExperTrain Glossary Library.
Further reading
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