Time Series Forecasting with LSTM Neural Networks and TensorFlow

Predicting the future from historical data is one of the most valuable skills in modern data science. This text-based course guides you through the fundamentals of Long Short-Term Memory (LSTM) networks, showing you how to model sequential patterns and forecast trends with confidence. You will transition from understanding basic neural network concepts to building, training, and evaluating your own LSTM models. By working through clear written explanations and practical Python code snippets, you will learn how to prepare sequential data, structure recurrent layers, and apply modern deep learning workflows to real-world time series problems. What you'll learn: - Understand the foundational concepts of recurrent neural networks and how LSTM cells manage long-term dependencies - Prepare and clean sequential datasets using modern dataframe libraries for optimal model training - Build and configure LSTM architectures using TensorFlow and Keras APIs - Implement proper data scaling, train-test splitting, and sliding window techniques for time series - Evaluate forecasting performance using standard metrics and analyze predictions through structured code outputs - Apply basic model saving and tracking concepts to ensure your forecasting pipeline is reproducible The course begins with essential terminology and the mathematical intuition behind sequential data before moving into hands-on data preparation. You will then progress through step-by-step code implementations, learning how to tune hyperparameters and evaluate your models effectively. This course is designed for aspiring data scientists, analysts, and programmers who are new to deep learning for time series. A basic familiarity with Python is helpful, but no prior experience with neural networks is required. Start reading today to unlock the power of deep learning for time series forecasting.