By the end of this course, learners will be able to identify the foundations of deep learning, analyze stock price datasets, apply preprocessing and feature scaling techniques, develop an RNN with LSTM layers, and evaluate predictions using real-world financial data.

This hands-on course takes learners through the complete journey of building a stock price forecasting model with Python. Starting with environment setup and dataset exploration, participants will learn how to preprocess data, perform exploratory data analysis, and apply transformations that prepare inputs for deep learning models. The course then dives into constructing and training a Recurrent Neural Network, leveraging LSTM layers to capture sequential dependencies in stock prices. Learners will test predictions on unseen data and visualize results to interpret model accuracy. What makes this course unique is its practical project-based approach—instead of abstract theory, every step is tied to real-world stock price data from Apple. Whether you are a data science beginner or looking to specialize in time-series forecasting, this course equips you with skills to confidently apply deep learning models to financial predictions and beyond.