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Quick answer
Take it if you already write Python and want the shortest structured route through modern deep learning. We rate Deep Learning in Python 4.7 / 5: about eighteen hours in which all five required courses are PyTorch courses, running from an introduction through images and text to transformer models. The limitation is compression — five subjects inside eighteen hours arrive as working introductions rather than specialisms, and we publish the track as Intermediate because Python is a prerequisite it never teaches. Skip it if you have never written Python, or if you want the mathematics behind these methods argued out at length.
Where we would start, among the ones that pay us
This review's subject: eighteen hours in which every required course is PyTorch, from the introduction through to transformer models, for a reader who already writes Python. Short enough to finish, and narrow enough that none of it is theory.
Why this course, and its limitations
A compact introduction to deep learning with PyTorch for learners who already know Python. We favour the focused format for practical study. A longer specialization can offer more theoretical depth; shorter does not mean better for every learner.
Learning: 4.7/5. Credential: 3.0/5. These are separate editorial judgments, not learner ratings or job-placement statistics.
The cross-provider alternative for a reader whose limit is patience rather than hours: the same framework at roughly fifty-two hours, bought once instead of subscribed to, and carried on through paper replication and model deployment that the DataCamp track's required list does not cover.
Why this course, and its limitations
A substantial practical PyTorch course including paper replication and deployment. We classify it as Intermediate because of the work involved. It offers more depth than a short introduction but needs sustained practice; length alone does not predict whether you will finish.
Learning: 4.7/5. Credential: 1.5/5. These are separate editorial judgments, not learner ratings or job-placement statistics.
A deep-learning syllabus can go wrong in two directions. It can front-load so much mathematics that nothing is trained for weeks, or it can train something immediately and never say what happened. This track sidesteps both by refusing to be broad: one framework, three kinds of problem, eighteen hours, and an assumption that you brought Python with you. Whether that reads as efficient or as thin depends almost entirely on what you intend to do the week after you finish — which is the question this review keeps coming back to.
What is it?
Deep Learning in Python is a DataCamp skills track: a fixed sequence of required courses, each one a mix of short video segments and coding exercises that execute in the browser against DataCamp's own environment. There is nothing to install and no environment to configure before the first exercise. Five courses are required for completion and the sequence is recorded at 18 hours.
The rest of what we can say about its reception comes from DataCamp's own catalogue data rather than from its website: more than 19,000 learners and an average learner rating of 5.0, both read on 27 August 2026. The attribution matters more than usual here. DataCamp publishes no structured data on its course pages, so no figure of theirs can be re-checked by a script; each one is taken by hand, carries the date it was taken, and is stated on this page in exactly that form.
We publish the track as Intermediate, and the argument sits inside its own required list. Every course in it is a PyTorch course, and the first of them opens on the framework rather than on the language, so Python is something you bring rather than something the track gives you. Intermediate here is a statement about what the first morning expects of you, not a position in a catalogue.
What the required list leaves out is worth naming too. There is no mathematics course in it, no course on classical machine learning, and no course on serving or operating a trained model. This is a track about building and training networks in one framework, and it is honest about being only that.
What you'll actually learn
These five courses are the track's required content, in DataCamp's own titles, with a line on what each is there to do.
- Introduction to Deep Learning with PyTorch — the foundations of the framework, and the course the other four assume you have already done
- Intermediate Deep Learning with PyTorch — the second and last of the courses that are about PyTorch itself rather than about a particular kind of problem
- Deep Learning for Images with PyTorch — the computer-vision side of the field: the problems where the input is a picture
- Deep Learning for Text with PyTorch — the language side: the problems where the input is a sequence of words
- Transformer Models with PyTorch — the architecture the current generation of language models is built on, and the last of the five required courses
Read the order as the argument. Two courses go on the framework itself and three on what it is used for, which is why the hours go further than they look: you are not being introduced to a new library in each section, only to a new kind of problem in the one you already know. It also means the applied half of the track is the larger half, which is not what the title leads you to expect from an eighteen-hour introduction.
The narrowness cuts both ways, and it is the single thing to weigh before enrolling. A track that never leaves PyTorch also never shows you the alternatives, never touches the data-engineering work that precedes real training runs, and never asks you to build anything that outlives the exercise. What you get is a framework you can write in and three problem shapes you have met. What you do not get is a portfolio, and the week after you finish is the right time to fix that.
Not sure this is the right one for you?
Tell the picker about your background and what you want the certificate to do, and it narrows the list to the one or two courses we would start with — from the same vetted list we rank from.
Try the AI Certification Picker →The details: cost, time, prerequisites
Cost. The track is not sold on its own; it comes with a DataCamp Premium subscription, which DataCamp prices by country — about $330 a year at US list price, per its affiliate team on 21 September 2026; from Pakistan we were shown $13 a month billed annually. Read DataCamp's pricing page for your own country before you commit. Holding the subscription for one eighteen-hour track is poor value on its own arithmetic; it pays off if you carry on into the machine-learning or AI-engineering tracks alongside it.
Time. Eighteen hours is content time, not calendar time. An hour each weekday puts you at the end in about three and a half weeks; two focused weekend days a month makes it a two-month project. Because the exercises run in the browser, the stated hours are closer to real hours than they would be for a course that opens by asking you to install CUDA — the setup step that quietly ends a great deal of self-directed study simply is not there.
Prerequisites. Python, and enough of it to read code you did not write. No deep learning is assumed, and no framework experience: the first required course starts at the beginning of PyTorch. Mathematics is not listed anywhere in the required sequence, which is part of why we are careful to call this a practical route rather than a theoretical one.
What you have at the end. A record that you completed the track. Nothing in it is examined, no invigilator is involved, and DataCamp's assessed certifications are a different product sold under their own terms — our guide to DataCamp's certifications separates the two for anyone who wants the assessed route. Treat the completion record as evidence you did the work, and treat what you can now build as the thing that actually carries.
How we checked this. We list the cost as DataCamp Premium subscription, priced by country — about $330 a year at US list price; DataCamp's pricing page shows the price for your country. Source: DataCamp's affiliate team, in writing on 21 September 2026: pricing is dynamic and geolocalised, with a US annual list price of $330. From Pakistan on 24 August 2026 the public pricing page showed $13 a month billed annually or $19 month-to-month, so the figure you are shown depends on where you are — treat any number here as a guide, not a quote. We re-check every price against the provider before each monthly review, and publish no figure we cannot source — where a provider prices regionally, we say so rather than quote a number that is wrong for most readers.
Pros and cons
✓ Pros
- Eighteen hours is short enough to schedule around a job, and finishing beats abandoning something longer
- One framework start to finish: all five required courses are PyTorch, so nothing is re-taught in a second API
- The required sequence runs as far as transformer models rather than stopping at a first neural network
- Images and text each get a course of their own, so you train on more than one shape of data
- Included in a DataCamp Premium subscription rather than sold as a separate purchase
✕ Cons
- Python is assumed from the first exercise and no Python course sits anywhere in the required sequence
- Five subjects inside eighteen hours leaves each one an introduction rather than a depth you could be hired on
- Finishing records completion; nothing in the track is examined, and DataCamp's assessed certifications are a separate product
Who should take it (and who shouldn't)
Take it if you write Python already and deep learning is the gap. Data analysts and software engineers are the clearest fit: the track begins exactly where general programming ability stops being enough, and it does not spend your first three hours re-teaching things you can do. It suits anyone who needs to be conversational about transformers quickly, and anyone aiming at vision work in particular — we name it on our guide to becoming a computer vision engineer for that reason, since the images course is a real part of the sequence rather than an afterthought.
Skip it if Python is still ahead of you rather than behind you; there is no route through this track that works without it. Skip it if what you want is the theory — the derivations, the optimisation arguments, the why rather than the how — because a longer programme does that properly and this one does not try. Our piece on the machine learning and deep learning specializations treats those two as a sequence rather than a choice, and the Deep Learning Specialization review is the place to start if that is the trade you want to make. And skip it if you need a credential someone will assess, because a completion record is not one.
How it compares to the alternatives
Three other courses readers weigh against this track, with the time each asks for. Of the three rows that teach deep learning, this one is the shortest at ~18 hrs, against ~52.22 hrs for the Udemy bootcamp and ~130 hrs for the Specialization; the fourth row is the no-code starting point for a reader who is not ready for any of them, at ~9 hrs and the only Beginner entry here.
| Certification | Provider | Level | Time | Coding | Best for | Enrol |
|---|---|---|---|---|---|---|
| Deep Learning in Python | DataCamp | Intermediate | ~18 hrs | Python | The shortest complete pass through PyTorch | DataCamp → |
| PyTorch for Deep Learning Bootcamp | Udemy | Intermediate | ~52.22 hrs | not recorded | Depth in one purchase, through to deployment | Udemy → |
| Deep Learning Specialization | DeepLearning.AI | Intermediate | ~130 hrs | Python | Theory, derivations and a name recruiters know | Coursera → |
| AI Fundamentals | DataCamp | Beginner | ~9 hrs | none | Understanding AI concepts without writing code | DataCamp → |
The nearest rival is the Udemy bootcamp, and it is the right answer for a reader whose limit is patience rather than hours. PyTorch for Deep Learning Bootcamp covers the same framework at roughly fifty-two hours and carries it further — its syllabus runs through transfer learning, experiment tracking, paper replication and model deployment, none of which the DataCamp track's required list contains. Its learner evidence is solid: over 6,000 ratings averaging 4.6 from more than 49,000 learners, checked in a browser on 14 September 2026, against a syllabus updated in February 2026. We rate PyTorch for Deep Learning Bootcamp 4.2 / 5, below this track, because the extra ground costs roughly thirty-four hours more. Its list price was $59.99 when we last checked, and Udemy discounts often enough that the sensible move is to check the day's price rather than plan around that list figure.
The Specialization is the other direction entirely: roughly seven times the hours for the theory this track leaves alone, and a provider name that carries weight on its own. AI Fundamentals is in the table for the reader who arrived here and recognised themselves in the prerequisites paragraph — nine hours, no code, and the concepts in place before any of this becomes worth attempting. If the platform question rather than the course question is what is open, our comparison of fast.ai and Coursera covers a third way through the same material.
Is it worth it?
Yes, for the reader it was built for, and the score says so: 4.7 / 5. The case is the shape of the thing. Eighteen hours committed to a single framework, with three of the five courses spent on real problem types, produces someone who can open a PyTorch file and know what they are looking at. That is a modest claim stated precisely, which is rarer in this market than an ambitious one stated vaguely.
The case against is the same sentence read the other way. Eighteen hours is eighteen hours; a transformer course inside it is an introduction to transformers, and no amount of good design makes it more than that. If you already know what you want to build, this is a fast route to being able to build a first version of it. If you want to understand why the methods work, this is the wrong purchase and a longer programme is the right one. Our reasoning, and the six factors behind every score here, is on the methodology page; where this track sits against everything else we score is in the 2026 ranking.
Why we score it 4.7 / 5
A compact introduction to deep learning with PyTorch for learners who already know Python. We favour the focused format for practical study. A longer specialization can offer more theoretical depth; shorter does not mean better for every learner.
4.7 / 5 how well it teaches3.0 / 5 what the certificate is worth
Provider facts for this entry were last checked on 2026-08-27.
Ready to start?
Included in a DataCamp subscription rather than bought outright. DataCamp's pricing page shows the plans and the price for your country, and one subscription covers the rest of its catalogue too.
Frequently asked questions
Do you need to know Python before starting Deep Learning in Python?
Yes, and the track's own required list is the evidence: all five courses are PyTorch courses, and not one of them is a Python course. You are expected to arrive able to write and read ordinary Python — functions, loops, lists and dictionaries — because the opening course spends its time on the framework rather than on the language. That is the whole reason we publish the track as Intermediate: the level we publish describes what it asks of you on day one. If Python is still new, learn the language first and come back. Eighteen hours of PyTorch is a poor place to also be working out what a function is.
Is eighteen hours enough to learn deep learning?
It is enough to train networks in PyTorch on images and on text, and to read a modern architecture without it being a wall of unfamiliar words. It is not enough to make you a deep-learning engineer, and the track claims nothing of the sort. Eighteen hours spread across five required courses is a little over three and a half hours each, which buys one worked pass through each subject. Treat what you leave with as fluency in the framework rather than command of the field, and decide what comes next — a project of your own, or a longer programme — before you start rather than after you finish.
Does the track teach PyTorch or TensorFlow?
PyTorch. Every one of the five courses required for completion carries PyTorch in its title, from the introduction through to the transformer course at the end, and no other framework appears anywhere in the required sequence. That single-framework decision is most of what makes eighteen hours go as far as they do: there is no second API waiting halfway through, and no translation step between what a video shows you and what the next exercise asks for. If you are studying for a specific team that has standardised on something else, check that before you enrol, because this track will not teach it to you.
Should you take this or Udemy's PyTorch for Deep Learning Bootcamp?
Take this one if the binding constraint is time and you want the shortest complete pass through PyTorch; take the Udemy course if the constraint is depth and you are ready to keep going for months. Deep Learning in Python is about eighteen hours inside a subscription and stays in the browser. PyTorch for Deep Learning Bootcamp runs to roughly fifty-two hours, is bought once rather than rented, and goes further, through paper replication and model deployment, neither of which appears in the DataCamp track's required list. We rate PyTorch for Deep Learning Bootcamp 4.2 / 5, below this track, because the extra ground costs roughly thirty-four hours more and that cost is real. Both end in a completion record rather than an assessed credential.