Build PyTorch Models for Linear Regression with `nn.Module`

Unlock the core concepts of PyTorch to implement your first machine learning models. This course will guide you through the process of building, training, and evaluating linear regression models using PyTorch's fundamental `nn.Module` class, providing a solid base for more complex neural networks. By the end, you will have a clear understanding of how PyTorch organizes model components and training workflows. What you'll learn: * Understand PyTorch tensors and their role in numerical computation. * Learn how automatic differentiation works with PyTorch's Autograd engine. * Build custom linear regression models using the `torch.nn.Module` class. * Implement forward passes, manage model parameters, and apply activation functions. * Configure training loops with optimizers and loss functions for model training. * Practice preparing and loading data efficiently using `Dataset` and `DataLoader`. * Apply basic model saving and loading techniques for persistence. The course begins with foundational PyTorch concepts, then progressively introduces model construction, training, and evaluation for linear regression, incorporating practical data handling techniques. This course is designed for absolute beginners with basic programming knowledge who want to learn machine learning fundamentals with PyTorch. No prior experience with PyTorch or deep learning is required. Start your journey into PyTorch and machine learning today.