Deep learning vs Machine learning: the guide

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Machine Learning and Deep Learning are different from each other in various ways. Read this to learn more about these two concepts and their applications.

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Erika L.
Apr 14, 23 · 8 min read
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Are you planning to learn and make a career in Machine Learning or Deep Learning? If so, this article is for you. Read on to learn about the similarities, differences, applications and use cases of these two data concepts.

Artificial Intelligence, Machine Learning and Deep Learning

Even if you aren't a data scientist, you will have read about these concepts. However, many people use the terms Deep Learning, Machine Learning and Artificial Intelligence interchangeably and this causes confusion.

Oxford Languages defines Artificial Intelligence or AI as the “theory and development of computer systems that perform tasks which normally require human intelligence.” Put simply, Deep Learning (DL) is a part of Machine Learning (ML), while the latter is a subset of Artificial Intelligence. You can think of these three concepts as concentric circles: Artificial Intelligence is the biggest circle in this data science universe, while Deep Learning is the smallest and Machine Learning fits in between.


Machine Learning versus Deep Learning

Model building is a key pillar of Artificial Intelligence. When you create a data model, you'll need to build data sets for production, training and testing purposes. Within AI, Machine Learning is a form of data analysis used to automate analytical model building. ML is that part of data science that works with the least amount of human interference as, once set up, every ML model keeps training itself. One of the key use cases you can employ Machine Learning for is image recognition.

Deep Learning is that part of ML that copies the human brain by using artificial neural networks. Every DL model uses interconnected nodes and layers when processing large sets of data. These neural networks mimic your human nervous system network. In fact, DL neural networks attempt to copy your human brain through a combination of bias, data inputs and feedback loops.

You will find that layers are the topmost building blocks in any Deep Learning model. There are several kinds of layers, and each layer generates results in a particular order. The types of neural networks in DL include:

  1. Multi-Layer Perception
  2. Convolutional Neural Network
  3. Recurrent Network

An example of a DL model that you are likely to come across is identifying cancerous cells by analysing medical data.

Differences between Deep Learning and Machine Learning

You will find that these technologies differ in some significant ways.

  1. ML uses algorithms to divide data. Later, Machine Learning learns from this parsed data to build models and make informed decisions based on those models. DL structures these algorithms into layers of data and thus creates an artificial neural network.
  2. While ML uses thousands of data points, Deep Learning models are based on millions of data points.
  3. ML requires more human intervention to set up. 
  4. In contrast, Deep Learning, while costly to implement, requires less manual intervention.
  5. You can use Machine Learning via a central processing unit. However, in DL, you'd need graphical processing units or GPUs to learn important concepts.
  6. While you can quickly set up and run Machine Learning models, results take time to come. In contrast, Deep Learning algorithms provide results almost immediately. However, the quality of these results improves over time.
  7. Any Machine Learning model takes less time for training because of limited data size. The training time in Deep Learning models is considerably longer.
  8. Machine Learning models can be linear as well as complex. However, this doesn't apply to any Deep Learning model because it's based on artificial neural networks.
  9. Machine Learning requires high-quality data but doesn't need large data sets. In contrast, Deep Learning uses large amounts of data and can include some low-quality data to create neural networks, but must improve over time.

Use cases of ML and Deep Learning

ML and DL are used by enterprises in several ways. You see some of these use cases daily. Here are some examples.


Machine Learning use cases
  1. Image recognition - Companies use this technology for image recognition, face detection and pattern recognition.
  2. Speech recognition - ML software is also used for speech recognition by Amazon's Alexa and the Google Maps network.
  3. Predicting traffic patterns - Whenever you input your current location into Google Maps, its ML software collects and interprets upcoming traffic data and tells you the best route.
  4. E-Commerce - Companies like Amazon and Best Buy use ML algorithms to study your shopping habits and recommend specific products and services for you at the checkout stage.
  5. Virtual assistants - Machine Language software is also used to provide you with virtual assistance through chatbots. When you put in a query, the chatbot replies using natural language processing and speech recognition technologies.

Organisations use Machine Learning in other areas also, such as catching malware and email spam.


Deep Learning use cases
  1. Image recognition - DL is used by IBM, Facebook and other companies for image and face recognition.
  2. Natural language processing - DL uses natural language processing when converting your words into text.
  • Translation - when you use Google Translate, Deep Learning algorithms are used to translate one language into another.

How to learn Machine Learning?

First, decide what you want to derive from this technology and set yourself a goal. Since ML is a fast-evolving discipline, you must update yourself frequently.
HowTo step image

1. Learning the basics first:

Walk, don't run. If you're new to data science, don't jump to advanced concepts like Deep Learning or Data Mining. Learn the basics first and then proceed to Natural Language Processing and other areas.

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