The difference between Deep Learning vs Machine Learning is often missed due to the similarities of both concepts. So, what do they two have in common? What makes them different? And which one is better to choose for your business case? 

In fact, Deep Learning is a subset of Machine Learning techniques that rely on artificial neural networks to learn from data. What differentiates it from ML is its capabilities and the business use cases. Now, let’s look a bit closer at these two notions.

What is Machine Learning, and when is it used?

Machine Learning is the science of getting computers to act by learning from experience without being explicitly programmed. It is used in cases where it is difficult or infeasible to develop a conventional algorithm for effectively completing the task.

Machine Learning development powers solutions across various industries, from technology and finance to healthcare and retail, where it drives automation, enables data-driven decisions and fosters continuous improvement. Whether you work in marketing, finance, HR, customer operations, or another department, ML can help you optimize a large share of tasks, providing speed and accuracy.

How does Machine Learning work?

Data scientists train Machine Learning models with existing datasets, test them, fine-tune them, and then apply well-trained models to real-life situations. The more data you feed when training the model — the better and the more accurate results it will produce.

The model runs as a background process and provides results automatically based on how it was trained. Data scientists can retrain the models as frequently as required to keep them up-to-date and make the results more effective. That’s why Machine Learning models improve their results progressively.

Based on how the models are trained, they fall into 3 key categories.

3 types of Machine Learning

Supervised learning — when the model is getting trained on a labeled dataset.
Example: analyzing customer data (age, gender, hobbies, etc.) and historical data to predict if a person clicks on an ad or not. 

Unsupervised learning - when the model is trained on neither classified nor labeled data, and the model must find the dependencies in the data independently. 
Example: market segmentation, in which the model clusters customers based on similarities in purchasing behavior, demographic data, or other relevant factors to adapt marketing strategies to specific customer segments. 

Reinforcement learning - is a Machine Learning technique that allows a model to learn in an interactive environment by trial and error using feedback from its own experiences. In short, the ultimate goal is to find the most suitable sequence of actions to get the reward - e.g., winning a game. 
Examples: driverless cars and games.

Deep Learning vs Machine Learning table of comparison

What is the difference between Deep Learning vs Machine Learning?

When comparing Deep Learning vs Machine Learning, it's evident that Machine Learning models depend more on human guidance and adjustments than Deep Learning. Indeed, ML can make insights without being explicitly programmed and improve their results progressively. However, Deep Learning can improve results independently by relying solely on neural networks. Its algorithms emulate the human brain's operation, recognizing inherent relationships within datasets. Additionally, Deep Learning models can adapt to evolving inputs seamlessly.

That means that Deep Learning developers can create a model in which an algorithm determines if a prediction or a result is accurate through its neural network. This algorithm can adjust the parameters autonomously if needed to improve the results. However, the downside of it is that they are more opaque and less explainable than conventional Machine Learning models.

Another important difference is that Deep Learning models help to solve the most complex tasks and drive insights from an ongoing stream of unstructured data (videos, texts, sensor data, images, etc.). Deep Learning models and neural networks enable self-driving cars, speech recognition, image recognition, natural language processing, precision medicine, and more.

Many big businesses across a wide range of industries that use Machine Learning are, in fact, powered by Deep Learning, such as: 

  • Tesla, which powers its autopilot system in self-driving cars; 
  • Apple — with its face recognition, starting from iOS 10;
  • Google, Apple, and Amazon for their virtual voice assistants;
  • Netflix - for their advanced recommendation system;
  • JP Morgan Chase — for insider trading detection and government regulatory compliance;
  • Pinterest —  for discovering visually similar objects, colors, patterns, and more;
  • Baker Hughes — for seismic modeling, automated well planning, predicting machinery failure, optimizing supply chains, and many more;
  • Via — uses Deep Learning to optimize ride-sharing routes.

How to choose between Deep Learning vs Machine Learning?

Regarding Deep Learning vs Machine Learning, the former is a more advanced technology as it helps solve more complex tasks. However, it is not a good fit in all business cases.

To ensure Deep Learning models are effective, you must train them on enormous amounts of data, like terabytes and petabytes. That’s why Deep Learning is typically a good option for businesses in the financial sector and banking, ecommerce, telecom, social media, oil and gas, customer support, and other domains dealing with an ongoing big data stream. 

If you don’t have very large datasets for training, Deep Learning models will produce highly inaccurate and sometimes even absurd results. Besides, the more data a trained Deep Learning model will deal with daily, the more advanced results and performance it will progressively achieve. Here is an image to illustrate.

Deep Learning data reliance graph

Unlike Deep Learning, ML models are generally more adaptable to different scenarios and are a better choice under certain circumstances. ML is an excellent option for businesses across diverse industries, particularly when the available datasets are not as extensive as those required for Deep Learning.

ML models can often achieve meaningful results with smaller datasets, making them accessible to a broader range of businesses. Industries such as retail, healthcare, manufacturing, and marketing benefit significantly from ML applications. For instance, ML is used in retail for demand forecasting, inventory optimization, and personalized recommendations. ML leverages patient data in healthcare to aid disease diagnosis, patient outcome prediction, and drug discovery.

Moreover, ML methods like decision trees, random forests, and support vector machines are easy to understand and explain. This makes them ideal for industries like finance and healthcare, where meeting regulatory standards is crucial. 


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What do you need for an effective Machine Learning/Deep Learning project?

At N-iX, we have identified seven common traits of a successful enterprise R&D project in Machine Learning/Deep Learning. Here they are.

  1. A clear objective. Before collecting the data, you need at least some general understanding of the results you want to achieve with AI and Machine Learning. At the early stages of the project, data scientists will help you turn that idea into actual KPIs.
  2. Robust architecture design of the Machine Learning solution. You need an experienced software architect to execute this task.
  3. An appropriate Big Data engineering ecosystem (based on Apache Hadoop or Spark) is necessary if your business deals with Big Data. It allows us to collect, integrate, store, and process huge amounts of data from numerous siloed data sources. Big Data architects and engineers are responsible for constructing the ecosystem.
  4. Running ETL procedures (extract, transform, and load) on the newly created ecosystem. A Big Data architect or a Machine Learning engineer performs this task.
  5. The final data preparation. Besides data transformation and technical clean-up, data scientists may need to refine the data further to make it suitable for a specific business case.
  6. Applying appropriate algorithms, creating models based on these algorithms, fine-tuning models, and retraining models with new data. Data scientists and Machine Learning engineers perform these tasks. TensorFlow, PyTorch, Keras, and Caffe are some of the popular tools widely used to carry out Deep Learning algorithms. TensorFlow is one of the best Deep Learning frameworks.
  7. Lucid visualization of the insights. Business intelligence specialists are responsible for that. Besides, you may need front-end developers to create dashboards with easy-to-use UI.


The correct choice between Deep Learning vs Machine Learning may help your company leverage better insights from vast amounts of data, enable intelligent automation and predictive analytics, optimize operations, reduce risks, and grow profits.

Deep Learning is typically used for solving more complex tasks and deriving insights from vast amounts of unstructured data (texts, videos, images, sensor data). It powers such techniques of Machine Learning as computer vision, speech recognition, natural language processing, and more. And it is worth using if your business generates an ongoing stream of huge amounts of data.

Launch your Deep Learning/Machine Learning project today