This comprehensive course on Long Short-Term Memory (LSTM) equips you with the skills to build advanced sequence models for time series forecasting and natural language processing. Begin by understanding the fundamentals of Recurrent Neural Networks (RNNs) and how LSTM addresses vanishing gradient issues. Dive into the LSTM architecture—learn the functions of forget, input, and output gates and how they manage memory over time. Progress to practical applications across industries including finance, healthcare, and AI-driven chat systems. Gain hands-on experience through guided demos that walk you through real-world LSTM implementations.

To be successful in this course, you should have a basic understanding of Python, machine learning fundamentals, and neural network architectures. By the end of this course, you will be able to: - Explain the core concepts and architecture of LSTM networks - Identify practical use cases in NLP and time series modeling - Build and train LSTM models using Python-based tools - Apply LSTM to solve real-world sequence prediction problems Ideal for data scientists, ML practitioners, and AI engineers.