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Building a Stock Price Predictor using LSTM in Keras
LSTM Stock Price Prediction - Time Series Forecasting, Deep Learning, Data Preprocessing, and Google Colab Deployment
What you'll learn
Understand the fundamentals of time series forecasting with LSTM (Long Short-Term Memory) models
Collect and visualize stock price data using Yahoo Finance and Matplotlib
Preprocess financial data and apply feature scaling techniques
Create sequence datasets suitable for LSTM networks
Build and train an LSTM-based neural network using TensorFlow/Keras
Apply model checkpointing and early stopping for optimal performance
Make future predictions and rolling forecasts of stock prices
Visualize model performance and export predictions to CSV
Save trained models and scalers to Google Drive for future use
Evaluate model performance using RMSE and MAE metrics
Requirements
Basic understanding of Python programming
A Google account to run and save files
Description
In this hands-on course, you'll learn how to build a complete Stock Price Prediction System using LSTM (Long Short-Term Memory) networks in Python - one of the most powerful deep learning architectures for time series data. Designed for learners with basic programming knowledge, this course walks you through real-world financial forecasting using historical stock market data.You will begin with data collection from Yahoo Finance using yfinance, and learn how to preprocess and visualize stock price data with pandas, NumPy, and matplotlib. You'll then dive deep into sequence modeling using LSTM from TensorFlow/Keras - a powerful neural network for capturing patterns in sequential data like stock prices. We will cover model architecture design, training strategies using early stopping and checkpointing, and advanced features such as rolling window forecasting and future prediction.Additionally, you'll learn how to deploy your project on Google Colab with GPU acceleration, and save models, scalers, metrics, and results directly to your Google Drive for seamless storage and access.By the end of this course, you'll be equipped to develop your own time series forecasting tools - a valuable skill in finance, AI applications, and predictive analytics. Whether you're a student, developer, or aspiring data scientist, this project-based approach ensures you can apply your knowledge in the real world.
Who this course is for
Data science and AI enthusiasts interested in time-series forecasting
Beginners and intermediate learners looking for a practical deep learning project
Finance professionals who want to understand stock prediction using neural networks
Students building academic or industry-ready projects
Anyone curious to learn how to forecast stock prices using real-world data and LSTM
Homepage:
Download link
rapidgator.net:
nitroflare.com:
Published 4/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 9m | Size: 241 MB
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 9m | Size: 241 MB
LSTM Stock Price Prediction - Time Series Forecasting, Deep Learning, Data Preprocessing, and Google Colab Deployment
What you'll learn
Understand the fundamentals of time series forecasting with LSTM (Long Short-Term Memory) models
Collect and visualize stock price data using Yahoo Finance and Matplotlib
Preprocess financial data and apply feature scaling techniques
Create sequence datasets suitable for LSTM networks
Build and train an LSTM-based neural network using TensorFlow/Keras
Apply model checkpointing and early stopping for optimal performance
Make future predictions and rolling forecasts of stock prices
Visualize model performance and export predictions to CSV
Save trained models and scalers to Google Drive for future use
Evaluate model performance using RMSE and MAE metrics
Requirements
Basic understanding of Python programming
A Google account to run and save files
Description
In this hands-on course, you'll learn how to build a complete Stock Price Prediction System using LSTM (Long Short-Term Memory) networks in Python - one of the most powerful deep learning architectures for time series data. Designed for learners with basic programming knowledge, this course walks you through real-world financial forecasting using historical stock market data.You will begin with data collection from Yahoo Finance using yfinance, and learn how to preprocess and visualize stock price data with pandas, NumPy, and matplotlib. You'll then dive deep into sequence modeling using LSTM from TensorFlow/Keras - a powerful neural network for capturing patterns in sequential data like stock prices. We will cover model architecture design, training strategies using early stopping and checkpointing, and advanced features such as rolling window forecasting and future prediction.Additionally, you'll learn how to deploy your project on Google Colab with GPU acceleration, and save models, scalers, metrics, and results directly to your Google Drive for seamless storage and access.By the end of this course, you'll be equipped to develop your own time series forecasting tools - a valuable skill in finance, AI applications, and predictive analytics. Whether you're a student, developer, or aspiring data scientist, this project-based approach ensures you can apply your knowledge in the real world.
Who this course is for
Data science and AI enthusiasts interested in time-series forecasting
Beginners and intermediate learners looking for a practical deep learning project
Finance professionals who want to understand stock prediction using neural networks
Students building academic or industry-ready projects
Anyone curious to learn how to forecast stock prices using real-world data and LSTM
Homepage:
Screenshots
Download link
rapidgator.net:
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