This course is the first part of a two-part series on PyTorch fundamentals. You'll learn to implement classic machine learning algorithms, specifically model creation and optimization. The course starts with tensors and operations, covering automatic differentiation, integrating Pandas and NumPy, and building datasets and transformations. You'll then train linear regression models, learn about loss, cost, and gradient descent, and make predictions using PyTorch's linear class and custom modules.
🆓 Free to Audit
🕒 Approx. 5 Weeks
📈 Intermediate Level
🧾 Paid Certificate Available Upon Completion
🎓 Offered by IBM via edX
