What’s the difference between deep learning and machine learning and how can it affect your career?
| 8 Min Read
Machine learning and deep learning are closely related areas of artificial intelligence, with deep learning forming a more specialised branch of machine learning to handle more complex tasks. Understanding how the two are connected and where each technology is used can help professionals build the skills needed for a career in AI.
In this article, we’ll be taking a look at how machine learning and deep learning differ and how these differences can influence career paths in AI.
What is deep learning?
Deep learning uses artificial neural networks inspired by the way the human brain processes information.
A neural network consists of connected layers of artificial neurons.
- The input layer, which receives data
- The hidden layers, which analyse patterns and relationships
- The output layer, which produces the final prediction or decision
As more hidden layers are added, the model becomes capable of recognising increasingly complex patterns. This layered learning process is what makes deep learning especially powerful for tasks involving images and speech, including contextually interpreting and understanding natural language.
How neural networks enable deep learning
Deep learning models can learn from large amounts of data and automatically identify complex patterns. This makes it a useful option for AI tasks that may be too complex for many traditional machine learning models.
How neural networks work
A simple neural network may contain only one hidden layer for basic prediction tasks. Deep learning models contain many hidden layers that progressively extract more detailed information from the data. As data passes through each layer, the model builds a better understanding of what it’s analysing. This makes deep neural networks highly effective for applications such as machine translation or natural language understanding.
These systems improve as they process larger datasets and are able to utilise more computing power.
What’s the difference between deep learning and machine learning?
Machine learning enables computers to learn from data and improve their performance without being explicitly programmed for every task. On the other hand, deep learning uses neural networks with multiple layers to identify patterns that would be difficult for traditional machine learning models to recognise.
What separates deep learning from machine learning?
Both approaches are part of AI, but they’re designed for different types of problems and datasets. This table gives you a quick look at the differences between the two technologies:
| Machine learning | Deep learning |
|---|---|
| Uses algorithms that learn from structured data | Uses multi-layered neural networks |
| Performs well with smaller datasets | Typically requires large amounts of data |
| Easier to interpret the decision-making process | Multiple layers of calculations to understand the decision-making process |
| Faster to train on simpler tasks | Requires greater computing power |
| Suitable for predictions and classification | Excels at images, speech, and language tasks |
These differences become easier to understand by looking at how deep learning developed from machine learning.
How deep learning builds on machine learning
Machine learning includes many different techniques, such as decision trees, support vector machines, and regression models. These methods often need a person to select the important features in the data before training starts. Deep learning uses multi-layered neural networks to learn features directly from raw data. This reduces the need for humans to manually identify and select features before training, although human input is still required for tasks like data preparation.
This is why deep learning is considered a subset of machine learning and not just a separate field. Every deep learning model is a machine learning model, but not every machine learning model is a deep learning model.
Where are deep learning and machine learning used?
Machine learning is used for structured data and predictive tasks, while deep learning is useful when working with large amounts of complex or unstructured data.
Machine learning applications
Machine learning is commonly used for:
- Fraud detection: Identifying unusual transaction patterns that may indicate fraud
- Recommendation systems: Suggesting products, films, or content based on user behaviour
- Customer analytics: Predicting customer behaviour and identifying trends
- Demand forecasting: Using historical data to predict future sales or demand
- Risk assessment: Analysing data to assess credit or business risks
Deep learning applications
Deep learning is particularly useful for tasks involving images, speech, and language, including:
- Computer vision: Recognising objects, faces, and patterns in images and video
- Natural language processing: Understanding and generating human language
- Speech recognition: Converting spoken language into text and responding to voice commands
- Generative AI: Producing text, images, audio, video, and code
- Autonomous systems: Helping vehicles and robots interpret their surroundings and make decisions
Understanding the differences between machine learning applications and deep learning applications can help you identify and develop the skills most relevant to your career goals.
Career paths available for machine learning and deep learning
The growing adoption of AI has increased demand for professionals with expertise in both machine learning and deep learning. As each industry and organisation aims to utilise these models in their own unique ways, each specialism has its own roles and responsibilities.
What employers look for in machine learning and deep learning professionals
Many employers look for professionals with skills in both machine learning and deep learning, as this combined knowledge allows for strong flexibility; machine learning is used across many industries, while deep learning is becoming important in areas such as computer vision and generative AI. Understanding AI and machine learning in-depth gives professionals more career options as AI and its applications continue to grow.
Specialised roles for machine learning and deep learning professionals
Machine learning professionals typically develop predictive models and improve business decision-making.
Common roles include:
- Machine Learning Engineer
- Data Scientist
- AI Analyst
- Data Engineer
Deep learning specialists usually work on more advanced AI systems involving computer vision and natural language processing.
Typical roles include:
- Deep Learning Engineer
- Computer Vision Engineer
- NLP Engineer
- AI Research Scientist
Both career paths require strong programming skills, with Python being the most commonly used language. Deep learning roles also often require experience with tools such as TensorFlow and PyTorch, as well as a good understanding of neural networks.
The next section explores how a postgraduate degree can help you pursue a career in machine learning.
How an MSc in AI and machine learning builds both skill sets
An MSc in AI and Machine Learning (online) at Goldsmiths provides structured learning that connects theoretical knowledge with practical application, helping students build expertise across both machine learning and deep learning.
MSc outcomes at Goldsmiths
The Goldsmiths’ AI and Machine Learning MSc helps students build practical expertise in machine learning, deep learning, data science, and AI system development. Through hands-on projects using industry-standard tools, graduates gain the knowledge and experience needed to pursue careers across the growing AI sector.
Graduates leave with skills that support a wide range of AI and machine learning careers, along with the flexibility to specialise in advanced areas such as deep learning over time.
Build your AI career foundation with an MSc
Understanding the difference between deep learning and machine learning is more than an academic exercise. It can help professionals focus their learning on the skills and technologies most relevant to their career goals.
For those looking to build expertise across both fields, the AI and Machine Learning MSc at Goldsmiths provides a strong foundation in machine learning, deep learning, and modern AI techniques, preparing graduates for a wide range of careers in the rapidly evolving AI industry.
Frequently asked questions
1. What is artificial intelligence?
Artificial Intelligence refers to the development of intelligent systems capable of analysing data, recognising patterns, and solving complex real-world problems. At Goldsmiths, you’ll explore AI across three distinct MSc pathways. New for 2026, the MSc in AI and Data Analytics combines technical grounding in AI with applied data analysis skills. The MSc in AI and Machine Learning focuses on building and deploying machine learning solutions. The MSc in AI and Creative Practice teaches the application of AI as a creative medium across film, music, design, and culture.
2. Is machine learning artificial intelligence?
Machine learning is a core branch of AI and is the method by which intelligent systems learn from data to make predictions and decisions without being explicitly programmed for each task. Goldsmiths’ AI and Machine Learning MSc covers this in depth, spanning machine learning, deep learning, natural language processing, and computer vision.
3. What are the types of artificial intelligence?
There are three main types of AI:
- Rule-based systems that follow fixed logic written by programmers
- Machine learning that learns patterns from data instead of following pre-set rules
- Deep learning that uses layered neural networks to handle complex patterns
At Goldsmiths, you’ll engage with these types of AI across modules in applied machine learning, MLOps, and computer vision, while the Creative Practice MSc adds generative AI systems and creative coding as distinct areas of study.
4. Is there a demand for these skills?
Graduates of Goldsmiths’ AI and Machine Learning MSc enter an industry with exceptional market demand, with opportunities across industries such as finance, healthcare, transportation, and entertainment, with roles such as AI Engineer and Machine Learning. The Creative AI pathway similarly prepares graduates on how to effectively utilise AI across high-growth roles in animation and VFX with opportunities to also branch out this understanding to music and advertising.
5. Is an artificial intelligence degree worth it?
Goldsmiths’ MSc in AI and Machine Learning offers full-stack AI capabilities and a prestigious qualification that acts as a competitive differentiator in the job market. The MSc in AI and Data Analytics combines AI with advanced data analytics skills, preparing graduates to work with data and intelligent systems to solve business and organisational challenges. The Creative Practice MSc equips graduates with a strong portfolio of AI-driven projects, positioning them for roles at generative AI start-ups and established media organisations.