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所在平台: Udemy |
课程主页: https://www.udemy.com/course/building-a-stock-price-predictor-using-lstm-in-keras/
课程评论：没有评论
课程名称：使用Keras构建股票价格预测模型 课程概述：在这个实践课程中，您将学习如何使用Python中的LSTM（长短期记忆）网络构建完整的股票价格预测系统。这是处理时间序列数据最强大的深度学习架构之一。该课程针对具有基本编程知识的学习者，带您通过历史股票市场数据进行现实世界的金融预测。您将首先从Yahoo Finance收集数据，学习使用yfinance进行数据采集，并使用pandas、NumPy和matplotlib进行股票价格数据的预处理和可视化。接下来，您将深入探讨使用TensorFlow/Keras进行序列建模的LSTM，这是捕获股票价格序列数据模式的强大神经网络。我们将涵盖模型架构设计、使用早停法和检查点的训练策略，以及滚动窗口预测和未来预测等高级特征。此外，您将学习如何在Google Colab上使用GPU加速部署项目，并将模型、缩放器、指标和结果直接保存到Google Drive，便于存储和访问。课程结束时，您将具备开发自己的时间序列预测工具的能力，这在金融、人工智能应用和预测分析中都是一种重要技能。无论您是学生、开发者还是有志成为数据科学家的人员，这种基于项目的方法确保您能够在现实世界中应用所学知识。
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.
