Machine Learning vs Deep Learning: Which to Learn First
Elexa AI Team · Updated 30 October 2026 · 6-minute read
Short answer
Deep learning is a type of machine learning that uses neural networks with many layers. Machine learning works well on tables of data and smaller datasets, while deep learning shines on images, audio and text when there is a lot of data. Learn machine learning first: it teaches you how models learn and how to test them, which you need for deep learning too.
What machine learning is
Machine learning is a way to build programs that learn patterns from examples instead of following rules written by hand. You give the program many examples with the right answers, for instance past house sales with their prices. It finds the pattern and uses it to predict the price of a house it has never seen.
Most machine learning in business works on tables: rows of customers, orders or transactions with columns such as age, amount or location. Popular methods include linear regression, decision trees, random forests and gradient boosting, usually built in Python with scikit-learn.
What deep learning adds
Deep learning uses neural networks: layers of simple calculations that pass information to each other. With many layers, a network can learn its own features straight from raw data. You do not have to tell it which parts of a photo matter; it works that out from thousands of examples.
That is why deep learning powers image recognition, speech assistants, translation and today's large language models. The cost is that it usually needs more data, more computing power and more care to train well.
Side by side
| Machine learning | Deep learning | |
|---|---|---|
| Best for | Tables of data: sales, customers, transactions | Images, audio, text and other complex data |
| Data needed | Hundreds to thousands of examples can work | Usually thousands to millions of examples |
| Computing power | A normal laptop | A GPU helps; free Google Colab is enough to learn |
| Explaining results | Often easy to see which inputs mattered | Harder to explain why a prediction was made |
| Typical tools | pandas, scikit-learn | PyTorch, TensorFlow, Hugging Face |
| Time to learn the basics | 3 to 5 weeks at an hour a day | 4 to 6 weeks after machine learning |
When to use which
Use machine learning when your data fits in a table, you have a modest amount of it, or you need to explain the result, for example to a manager or a regulator. Predicting customer churn, estimating delivery times and scoring loan risk usually fall here.
Use deep learning when the data is unstructured, such as photos, recordings or long text, and you have plenty of examples. Recognising products in images, transcribing calls and classifying documents are typical cases. For many text tasks today, you can also start from a pre-trained model instead of training your own.
Why learn machine learning first
Deep learning uses the same core ideas as machine learning: training and test data, loss, overfitting and evaluation. If you learn them first on simple models, they are easy to see and fix. If you start with deep learning, the same problems are hidden inside a large network and much harder to understand.
People who learn in this order usually find deep learning faster to pick up, because they only need to learn what is new: the networks themselves.
Next step
Start with Machine Learning with Python to learn the foundations with real projects, then move on to Deep Learning with PyTorch. The AI Learning Path shows where both fit in the full plan.