This complete PyTorch course is designed for beginners who want to learn deep learning and machine learning using one of the most popular AI frameworks in Python. The course starts with the fundamentals of deep learning, neural networks, and machine learning workflows before moving into practical PyTorch implementation.

Students will learn how tensors work, how to manipulate data with PyTorch, and how to use GPUs for faster model training. The course explains key concepts such as training loops, loss functions, optimizers, model evaluation, and reproducibility in machine learning projects.

The tutorial also covers neural network classification, regression models, and multiclass classification problems. Learners build and train models step by step while understanding the intuition behind deep learning systems.

A major focus of the course is computer vision using convolutional neural networks (CNNs). Students work with image datasets, DataLoaders, TorchVision, data augmentation, and custom datasets while training deep learning models for image classification tasks.

Advanced sections include custom dataset creation, overfitting and underfitting analysis, model evaluation with confusion matrices, and prediction workflows. By the end of the course, learners will have practical experience building end-to-end deep learning applications with PyTorch.