This intermediate course path is designed for learners who have experience with Python and basic machine learning concepts and want to develop practical deep learning skills using PyTorch. You will begin with tensors, the core data structure used to build and train neural networks, then move into model construction and evaluation. The path covers how to build simple neural networks, train models for binary and multi-class classification, and prepare datasets for use in PyTorch workflows. You will also work through an applied project using the Wine dataset, including preprocessing, training, evaluation, and model saving. Later courses introduce techniques for improving model performance and training stability, including dropout, regularization, batch normalization, learning rate scheduling, and checkpointing. By the end, you will be prepared to create, evaluate, optimize, and save PyTorch models for practical machine learning tasks.
