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所在平台: Udemy |
课程主页: https://www.udemy.com/course/full-stack-machine-learning-django-rest-framework-react/
课程评论：没有评论
课程名称：全栈机器学习 Django REST Framework 和 React 课程概述：这不是一般的课程，而是一个实践项目，您将使用 Django REST Framework、React.js 和机器学习构建一个完整的股票预测门户网站。课程流程如下： 首先，您将学习 Django REST Framework 的基础知识，包括什么是 REST API 以及如何创建它们。如果您已经熟悉 Django REST Framework，可以跳过这一部分。 接下来，我们将深入学习 React.js 的基础知识，以便构建应用程序的前端。 然后，我们将把 Django REST Framework 与 React.js 连接起来，构建门户网站。这将包括实现用户认证系统和应用程序所需的其他基本功能。 一旦门户结构准备好，就可以深入学习机器学习部分。本课程并不是一个机器学习训练营，因此不会详细覆盖每个 ML 概念，而是专注于构建一个股票预测门户作为实际用例的实践方法。 机器学习部分包括： - 机器学习的基本概念及其不同类型。 - 如何为特定问题选择正确的 ML 方法。 - 何时以及为什么使用深度学习，以及神经网络的工作原理。 - 为什么神经网络是此股票预测用例的最佳选择。 您将使用 Jupyter Notebook 构建一个 LSTM 模型，以分析股票价格数据并进行预测。模型准备好后，您将创建一个 API，将其与门户整合，并显示结果。 本课程让您全面体验构建一个实际的股票预测门户网站的过程，结合了 Django REST Framework、React.js 和机器学习的全栈项目。 您还将学习的额外技能包括： - 使用 Pandas 和 NumPy 进行数据处理。 - 使用 Matplotlib 进行数据可视化。 在课程结束时，您将完成一个完整的项目，同时获得网络开发和机器学习的实践经验。 重要声明：此预测模型不得用于实际股票市场交易，仅用于教育目的，以帮助您理解机器学习和股票市场数据的原理。依赖此模型进行实际投资可能会导致重大财务风险。
Not just another course, this is a hands-on program where you'll build a complete, stock prediction portal using Django REST Framework, React.js, and Machine Learning. Course Flow:First, you'll learn the fundamentals of Django REST Framework, including what REST APIs are and how to create them. If you're already familiar with Django REST Framework, you can skip this section.Next, we'll dive into the fundamentals of React.js to build the front-end of our application.After that, we'll connect Django REST Framework with React.js to build the portal. This will include implementing a user authentication system and other essential features needed for a functional application.Once the portal structure is ready, it's time to dive into machine learning. This course is not a Machine Learning Bootcamp, so it won't cover every ML concept in detail. Instead, it takes a practical approach focused on building a stock prediction portal as a real-world use case.Machine Learning Section:The basics of machine learning and its different types.How to choose the right ML approach for a specific problem.When and why to use deep learning and how neural networks work.Why a neural network is the best choice for this stock prediction use case.You'll build an LSTM model in Jupyter Notebook to analyze stock price data and make predictions. Once the model is ready, you'll create an API to integrate it with the portal and display the results.This course gives you the full experience of building a real-world stock prediction portal-a full-stack project combining Django REST Framework, React.js, and machine learning.Additional Skills You'll Learn:Data manipulation using Pandas and NumPy.Data visualization using Matplotlib.By the end of this course, you'll have built a complete project while gaining hands-on experience in both web development and machine learning.Important Disclaimer: This prediction model should NOT be implemented in real stock market trading. It is developed purely for educational purposes to help you understand the principles of machine learning and stock market data. Relying on this model for actual investments can lead to significant financial risks.
