Udemy

Full Stack Machine Learning | Django REST Framework, React

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  • 995 名學生
  • 更新於 9/2025
4.7
(192 個評分)
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課程資料

報名日期
全年招生
課程級別
學習模式
修業期
18 小時 35 分鐘
教學語言
英語
授課導師
Rathan Kumar
評分
4.7
(192 個評分)
6次瀏覽

課程簡介

Full Stack Machine Learning | Django REST Framework, React

Learn to Build full-fledged Stock Prediction Portal using Python, Django REST Framework, React.js and Machine Learning

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.

課程章節

  • 10 個章節
  • 187 堂課
  • 第 1 章 Introduction
  • 第 2 章 Getting Started
  • 第 3 章 Django REST Framework
  • 第 4 章 Class Based Views
  • 第 5 章 Mixins
  • 第 6 章 Generics
  • 第 7 章 Viewsets
  • 第 8 章 Nested Serializers
  • 第 9 章 DRF Pagination, Filtering, Search and Ordering
  • 第 10 章 React.js Introduction & Installation

課程內容

  • REST API Development
  • Backend Development with Django and Frontend with React JS
  • Machine Learning with Neural Networks
  • Deep Learning with LSTM Models
  • Data Analysis, Data Manipulation and Data Visualization
  • How to decide which type of machine learning to use for specific problems.
  • Where deep learning comes in and how neural networks work.
  • Why a neural network is the best choice for this specific stock prediction use case.
  • Integration of Machine Learning Models with Web Applications

評價

  • O
    Ogbonna Emmanuel Ndubuisi
    5.0

    It is engaging tutorial thank you very much

  • G
    Galang Piliang
    5.0

    Need to add an explanation about how the business side works for this project, so that I can easily present this to the future employer/client, also it will be huge thanks if you can also add the series where we can deploy this to the production environtment

  • P
    Prashant
    5.0

    Fabulous course to understand machine learning and Django rest framework together. It is all complete package course. Love it....

  • M
    Mehdi
    5.0

    The course instructor is breaking down the concepts very well which makes it easier to understand. The support for the Q&A section is responding fast and are very helpful.

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