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Deep Learning RNN & LSTM: Stock Price Prediction

EDUCBA via Coursera

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Overview

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Discover how deep learning can be applied to stock price prediction by building a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) layers in Python. This hands-on course guides you through the complete workflow, from setting up your development environment and preparing financial datasets to training, evaluating, and visualising a deep learning model for time-series forecasting. You will learn how to analyse stock price data, perform exploratory data analysis, preprocess datasets, apply feature scaling and consistent data transformations, and construct an RNN that captures sequential patterns in financial data. Using real-world Apple stock price data, you will train an LSTM-based model, generate predictions on unseen data, and evaluate forecasting performance through visual comparison with actual stock prices. Designed for beginners in data science as well as learners who want to strengthen their deep learning and time-series forecasting skills, this course emphasises practical implementation rather than isolated concepts. By connecting data preparation, neural network development, prediction, and performance evaluation into a single project, you will gain the confidence to build and assess RNN models for stock price forecasting using real-world financial data.

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.