Deep Learning RNN & LSTM: Stock Price Prediction

Overview

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.

Syllabus

Foundations of Deep Learning with RNN

This module introduces learners to the foundational concepts and practical setup required for building a Recurrent Neural Network (RNN) for stock price prediction. Learners will explore dataset preparation, preprocessing, exploratory analysis, and feature scaling techniques to create a strong data pipeline essential for deep learning models.

Building & Deploying the RNN Model

This module guides learners through the construction, training, and evaluation of an RNN model using LSTM layers for stock price forecasting. Learners will gain practical skills in neural network architecture, training optimization, prediction analysis, and visualization of final results to assess model performance.