Intro to Machine Learning with PyTorch
by Udacity Team · Udacity
Our Verdict
Worth it — with caveatsDespite the catalog label "Intro to Machine Learning with PyTorch," the linked course (ud188) is actually Udacity's free "Intro to Deep Learning with PyTorch," originally sponsored by Facebook (now Meta) and co-taught by PyTorch co-creator Soumith Chintala. It is a genuinely free, self-paced, 9-lesson video course that walks beginners from neural-network basics through building, training, and deploying networks in PyTorch (CNNs for image classification, style transfer, RNNs for sentiment analysis). It is well-rated as a gentle, hands-on entry point (4.5/5 from 41 reviews on Class Central) but is deliberately theory-light and, despite the official "0 prerequisites" claim, in practice expects working Python plus NumPy/Matplotlib. It does NOT issue a certificate, and it is a different product from Udacity's paid "Introduction to Machine Learning with PyTorch" Nanodegree (nd229), which adds supervised/unsupervised ML and graded projects. For a free first taste of PyTorch it is a reasonable pick; for structured classical ML or a credential, look elsewhere.
Free, beginner-friendly, and taught partly by PyTorch's own co-creator, so it is a strong no-cost first exposure to deep learning in PyTorch. But it is conditional because the course is theory-light (students report it teaches you to stitch code together more than to design your own models), it quietly assumes Python fluency the official page denies, it offers no certificate, and it is mislabeled in this catalog as a machine-learning course when it is really a deep-learning intro distinct from the paid nd229 Nanodegree.
Best for: Beginners who already know basic Python (and ideally NumPy/Matplotlib) and want a free, guided, hands-on introduction to deep learning specifically in PyTorch; people who learn well from short video lessons plus runnable notebooks; and learners who want to hear PyTorch explained by its co-creator Soumith Chintala before moving on to official tutorials or fast.ai.
Skip if: Complete programming novices (multiple students got stuck early due to insufficient Python), learners who want classical machine-learning breadth (linear/logistic regression, SVMs, decision trees, clustering) rather than neural nets, people who need a verifiable certificate or credential, and those wanting deep mathematical theory or the ability to architect novel models from scratch.
About This Course
Learn foundational machine learning algorithms with a focus on PyTorch, covering supervised and unsupervised methods.
What You'll Learn
Curriculum
Orientation, setup, and how the course/notebooks are structured.
Foundations: perceptrons, activation functions, gradient descent, and backpropagation before touching PyTorch.
Interview-style lesson with PyTorch co-creator Soumith Chintala on the library's design and philosophy.
Tensors, autograd, building and training networks, transfer learning, and basic image classification in PyTorch.
CNN architecture and training for image-classification tasks.
Applying the style of one image to another using features from a pretrained CNN.
RNN/LSTM fundamentals for sequential data and text generation.
Building an RNN to classify text sentiment.
Converting and deploying trained models (TorchScript) for inference.
Prerequisites
- Working knowledge of Python (the official page says 0 prerequisites, but student reviews and other listings strongly recommend solid Python)
- Familiarity with NumPy and Matplotlib is expected for the coding exercises
- Basic calculus and linear algebra are helpful (not strictly required, but assumed for gradient descent / backprop intuition)
- Fluent written and spoken English (course is English-only)
Instructor
Udacity Team
Instructor · Udacity
Pros & Cons
Pros
- Completely free and self-paced, with runnable Jupyter notebooks and a public GitHub repo for every lesson
- Includes a lesson taught by PyTorch co-creator Soumith Chintala, plus respected instructors like Luis Serrano and Cezanne Camacho
- Practical, project-driven coverage (CNN image classification, style transfer, RNN sentiment analysis, model deployment) rather than slides-only
- Beginner-friendly pacing and clear explanations make it an accessible first contact with deep learning in PyTorch
- Well-reviewed for a free course (4.5/5 from 41 reviews on Class Central)
Cons
- Theory-light: students report it teaches you to stitch existing code together and use basic models, but not how to design your own models from scratch
- Mislabeled vs. its actual content and assumptions: it is a deep-learning intro (not broad classical ML), and the official 'no prerequisites' claim is misleading since Python fluency is effectively required (one learner got stuck in Lesson 2)
- No certificate of completion for the free open course
- Content predates recent PyTorch and generative-AI developments; it focuses on CNN/RNN-era topics rather than transformers or LLMs
Alternatives To Consider
Frequently Asked Questions
Is Intro to Machine Learning with PyTorch free?
Yes — Intro to Machine Learning with PyTorch is free to access. Free. There is no program fee for the open ud188 course, and it does not grant a certificate. This is distinct from Udacity's paid 'Introduction to Machine Learning with PyTorch' Nanodegree (nd229), which is subscription-priced and does include certificates and graded projects.
Who is Intro to Machine Learning with PyTorch for?
Beginners who already know basic Python (and ideally NumPy/Matplotlib) and want a free, guided, hands-on introduction to deep learning specifically in PyTorch; people who learn well from short video lessons plus runnable notebooks; and learners who want to hear PyTorch explained by its co-creator Soumith Chintala before moving on to official tutorials or fast.ai.
What will you learn in Intro to Machine Learning with PyTorch?
Core neural-network concepts: perceptrons, activation functions, gradient descent, and backpropagation; How to build, train, and validate neural networks in PyTorch using tensors, autograd, and nn modules; Convolutional neural networks (CNNs) for image classification (e.g., dogs-vs-cats style tasks); Style transfer using features from a pretrained convolutional network.
What are the prerequisites for Intro to Machine Learning with PyTorch?
Working knowledge of Python (the official page says 0 prerequisites, but student reviews and other listings strongly recommend solid Python); Familiarity with NumPy and Matplotlib is expected for the coding exercises; Basic calculus and linear algebra are helpful (not strictly required, but assumed for gradient descent / backprop intuition); Fluent written and spoken English (course is English-only).
Is Intro to Machine Learning with PyTorch worth it?
Free, beginner-friendly, and taught partly by PyTorch's own co-creator, so it is a strong no-cost first exposure to deep learning in PyTorch. But it is conditional because the course is theory-light (students report it teaches you to stitch code together more than to design your own models), it quietly assumes Python fluency the official page denies, it offers no certificate, and it is mislabeled in this catalog as a machine-learning course when it is really a deep-learning intro distinct from the paid nd229 Nanodegree.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Udacity's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official Udacity course page (Intro to Deep Learning with PyTorch, ud188)
- Class Central listing with rating (4.5/5, 41 reviews) and syllabus
- Careers360 details (free, no certificate, prerequisites, syllabus)
- Reddacity aggregation of real Reddit student opinions on ud188
- Udacity paid 'Introduction to Machine Learning with PyTorch' Nanodegree (nd229) for contrast