Data Science Forecasting with PyTorch: From Math to Neural Networks

In today's data-driven world, the ability to predict future trends and build intelligent models is one of the most valuable skills in technology. Understanding both the mathematical foundation and the practical programming tools is the key to unlocking true predictive power. This text-based course guides you through the entire pipeline of data forecasting, helping you transition from understanding the essential mathematical concepts of calculus and linear algebra to writing clean, production-ready PyTorch code. What you'll learn: - Understand the foundational mathematics, including calculus and linear algebra, required for machine learning. - Analyze time series data by identifying trends, seasonality, and noise using modern Python libraries. - Build and train neural network architectures from scratch using PyTorch. - Apply forecasting models to predict future values in real-world scenarios. - Implement modern workflow practices, including virtual environments and basic experiment tracking concepts. You will start with core mathematical definitions and basic data manipulation before moving step-by-step into advanced forecasting algorithms and neural network design. The written explanations and code-focused exercises ensure you grasp both the theory and the practical application of every model. This course is designed for absolute beginners to data science and neural networks, with no prior advanced math or deep learning experience required. Start your journey into predictive analytics and deep learning today.