Intro To Py Torch And Neural Networks
Build and evaluate PyTorch neural networks with hands-on projects
This course teaches practical development of neural networks using PyTorch, covering model construction, activation and loss functions, optimizer algorithms, and model evaluation. The emphasis is hands-on: you will build, train, and test models and apply them to real-world prediction tasks.
Aimed at intermediate learners with foundational Python and machine learning knowledge, the curriculum uses a guided project (predicting electric vehicle charging loads), quizzes, and AI-assisted coding feedback to make skills immediately usable.
At a Glance
Intro To Py Torch And Neural Networks teaches how to create, train, and test artificial neural networks using PyTorch. It is instructed by Ada Morse and the course team and broadly covers activation and loss functions, optimizer algorithms, and building models for real-world prediction tasks.
| Level | Intermediate |
| Rating | 4.6 out of 5 |
| Duration | 3+ hours |
| Languages | English |
| Learners | 10K+ learners |
| Certificate | Certificate of completion (available with paid plan) |
| Access | Access for as long as your subscription is active |
| Course includes |
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| Price | Subscription-based; some features require a paid plan |
What This Course Teaches
On completion, learners can perform concrete, measurable tasks in PyTorch such as constructing models, implementing core functions, and assessing model performance. These competencies are framed so you can apply, evaluate, and iterate on neural networks for real-world prediction tasks.
How the Course Is Structured
The course is organized into 4 modules that together combine a core lesson, a guided project, an assessment quiz, and a short next-steps unit. The full sequence is compact and intended to be completed in roughly 3+ hours in total.
Curriculum overview
01Intro to Py — Torch and Neural Networks▾
Introduces how to create, train, and test artificial neural networks in PyTorch, and explains common loss functions and optimizer algorithms.
02Predicting Electric Vehicle Charging Loads▾
Guided project that walks through building a neural network in PyTorch to predict electric vehicle charging loads from real-world data.
- Project: Predicting Electric Vehicle Charging Loads
03Intro to Py — Torch and Neural Networks▾
A short assessment to check understanding of the lesson material and applied concepts.
- Quiz: Intro to Py — Torch and Neural Networks (auto-graded)
04Next Steps — Certificate of completion available with Plus or Pro▾
Information about earning the certificate of completion and suggested next courses or resources to continue learning.
Audience & Requirements
This course is aimed at intermediate learners seeking hands-on experience building and evaluating neural networks with PyTorch. It is appropriate for those who have some prior exposure to machine learning concepts and want to apply models to real-world prediction tasks like forecasting electric vehicle charging loads.
- Data practitioners who want to implement neural networks for predictive tasks.
- Learners who have completed an introductory regression or ML course and want practical PyTorch skills.
- Software engineers seeking to add PyTorch model-building to their toolset.
- Analysts aiming to upgrade from traditional models to basic deep learning workflows.
- Recommended prior course: Machine Learning: Introduction with Regression (or equivalent knowledge).
- Working familiarity with Python and basic machine learning concepts.
- A Python development environment with PyTorch available (local install or cloud notebook).
Final Verdict
Recommended for intermediate learners who already know Python and basic machine learning and want a compact, project-driven introduction to building neural networks with PyTorch. The course’s practical project, assessment structure, and positive learner reception make it a pragmatic choice to gain applied PyTorch skills without a large time investment, and the platform certificate and subscription access provide straightforward ways to showcase and continue using the material.
This is not the best starting point for complete beginners; those without prior regression or ML exposure should take an introductory course first to avoid gaps in fundamentals. For learners who meet the prerequisites and prefer hands-on, guided work, this course offers clear, employable competencies at modest time cost and with accessible credentialing options.
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