Stock Price Forecasting with LSTM and Bayesian Models in Python

Predicting market trends requires a solid understanding of both historical data patterns and statistical uncertainty. This text-based course guides you through the foundational concepts of financial data analysis and predictive modeling using modern Python tools.\n\nYou will transition from understanding basic financial terminology to building sophisticated Long Short-Term Memory (LSTM) neural networks and Bayesian models. By working through clear explanations and structured code snippets, you will learn how to clean messy market data, handle missing values, and construct robust forecasting pipelines.\n\nWhat you'll learn:\n- Understand core financial forecasting concepts, market data structures, and the mathematical foundations of time-series modeling.\n- Clean and preprocess financial datasets, handling outliers and missing values using modern Python data libraries.\n- Build and train LSTM recurrent neural networks to capture sequential patterns in stock price history.\n- Implement Bayesian models to quantify uncertainty and make probabilistic predictions about future market movements.\n- Apply modern Python development practices, including virtual environments, type hints, and clean code principles to your data pipelines.\n- Evaluate model performance using standard financial metrics to understand the strengths and limitations of your forecasts.\n\nThe course begins with essential financial terminology and data preparation techniques before guiding you step-by-step through neural network architecture and probabilistic modeling. You will progress naturally from data cleaning to advanced model evaluation through clear written explanations and practical coding exercises.\n\nThis course is designed for aspiring quantitative analysts, data scientists, and programming beginners who want to explore financial modeling. No prior experience with deep learning or advanced statistics is required.\n\nStart reading today to build your first stock forecasting model.