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Full Stack Machine Learning

Django REST Framework, React

5.0 Filled star Filled star Filled star Filled star Half star
Based on 16 ratings
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Rathan Kumar

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:

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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.

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What's inside

Learning objectives

  • 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

Syllabus

How To Get The Most Out Of This Course
Introduction
Asking Questions & Getting Help
Getting Started
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Offers a practical, hands-on approach to building a stock prediction portal, integrating Django REST Framework, React.js, and machine learning techniques, providing experience in modern web development
Develops skills in data manipulation using Pandas and NumPy, alongside data visualization using Matplotlib, which are essential for data analysis and machine learning projects
Covers the basics of machine learning, including how to choose the right approach for specific problems, and explains when and why to use deep learning with neural networks
Emphasizes the integration of machine learning models with web applications, providing a comprehensive understanding of how to deploy models in a real-world context
Focuses on building an LSTM model in Jupyter Notebook for stock price data analysis, offering practical experience in time series forecasting and neural network implementation
Explicitly states that the prediction model is for educational purposes only and should not be used for real stock market trading, which is an important disclaimer for learners to consider

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Reviews summary

Full stack ml with django and react

According to learners, this course offers a highly practical and hands-on approach to building a full-stack application that integrates Django REST Framework and React.js with Machine Learning. Students particularly appreciate the real-world project of creating a stock prediction portal, finding it a great way to learn by doing and see how all the components fit together. While the instructor's explanations are generally clear, some reviewers note that the ML section is focused on application rather than deep theory, and the course is best suited for those with some prior familiarity with Django or React basics.
Instructor explains concepts well.
"The instructor is clear and easy to follow throughout the lessons."
"Explanations are concise and straight to the point."
"I appreciated how the instructor broke down complex topics."
"Good teaching style made the material accessible."
Teaches how to connect backend, frontend, and ML.
"Learning how to integrate Django REST Framework with React was a game-changer."
"The course does a great job of showing you the full pipeline, including ML integration."
"Finally understood how to connect my backend API to the React frontend."
"The integration part is very practical for building real applications."
Focuses on building a complete, real-world app.
"I loved building the stock prediction portal, it felt like a real project."
"The hands-on project approach is excellent for seeing how everything connects."
"Building the full application from scratch was the most valuable part for me."
"This course is perfect if you want to learn how to build a full project incorporating different technologies."
Helpful to have some prior web dev knowledge.
"This course moves quickly, having prior React or Django experience is highly recommended."
"Some sections assume you are comfortable with basic concepts."
"Not ideal for absolute beginners to all three technologies (DRF, React, ML)."
"Be prepared to do some extra learning on the side if you're new to the stack."
ML section application-focused, not theory-heavy.
"Don't expect a deep dive into machine learning algorithms or theory."
"The ML part is very specific to the stock prediction use case, not a general ML course."
"It's more about integrating an existing ML model than teaching ML from scratch."
"Good for integrating ML, but you'll need other resources for ML fundamentals."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Full Stack Machine Learning | Django REST Framework, React with these activities:
Review REST API Concepts
Solidify your understanding of REST API principles before diving into Django REST Framework. This will make grasping the framework's concepts much easier.
Browse courses on REST APIs
Show steps
  • Read articles and watch videos explaining RESTful architecture.
  • Review common HTTP methods (GET, POST, PUT, DELETE).
  • Understand the concept of statelessness in REST APIs.
Review REST API Concepts
Solidify your understanding of REST API principles before diving into Django REST Framework. This will make grasping the framework's concepts much easier.
Browse courses on REST APIs
Show steps
  • Read articles and watch videos explaining RESTful architecture.
  • Review common HTTP methods (GET, POST, PUT, DELETE).
  • Understand the concept of statelessness in REST APIs.
Brush Up on Python Fundamentals
Reinforce your Python skills, as Django REST Framework relies heavily on Python. This will help you write cleaner and more efficient code.
Browse courses on Python
Show steps
  • Review basic syntax, data structures, and control flow.
  • Practice writing functions and classes in Python.
  • Familiarize yourself with Python's package management (pip).
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Brush Up on Python Fundamentals
Reinforce your Python skills, as Django REST Framework relies heavily on Python. This will help you write cleaner and more efficient code.
Browse courses on Python
Show steps
  • Review Python syntax, data structures, and control flow.
  • Practice writing functions and classes in Python.
  • Familiarize yourself with Python's package management system (pip).
Read 'Django for Beginners'
Gain a deeper understanding of Django fundamentals to better grasp Django REST Framework concepts.
View Django for Beginners: Build websites with... on Amazon
Show steps
  • Read the book cover to cover.
  • Complete the exercises and projects in the book.
  • Take notes on key concepts and techniques.
Read 'Django for Beginners'
Gain a deeper understanding of Django fundamentals to better grasp Django REST Framework concepts.
View Django for Beginners: Build websites with... on Amazon
Show steps
  • Read the book chapter by chapter.
  • Complete the exercises and projects in the book.
  • Take notes on key concepts and techniques.
Build a Simple To-Do List API
Practice building a REST API from scratch using Django REST Framework. This hands-on experience will solidify your understanding of the framework's components and workflow.
Show steps
  • Define the API endpoints and data models.
  • Implement the API using Django REST Framework serializers and views.
  • Test the API endpoints using a tool like Postman or Insomnia.
Implement Basic CRUD APIs
Practice building Create, Read, Update, and Delete (CRUD) APIs using Django REST Framework. This will solidify your understanding of serializers, views, and models.
Show steps
  • Define a simple model (e.g., a 'Task' model).
  • Create serializers for the model.
  • Implement API endpoints for creating, reading, updating, and deleting tasks.
  • Test the API endpoints using a tool like Postman or Insomnia.
Document Your Learning Journey
Create a blog or journal to document your learning process. Explaining concepts in your own words will reinforce your understanding and help you identify areas where you need more practice.
Show steps
  • Create a blog or use a note-taking app.
  • Write summaries of each module or topic covered in the course.
  • Explain concepts in your own words and provide examples.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'
Deepen your understanding of machine learning concepts and techniques used in the stock prediction portal.
View Hands-On Machine Learning with Scikit-Learn,... on Amazon
Show steps
  • Read the chapters related to neural networks and time series analysis.
  • Experiment with the code examples provided in the book.
  • Apply the concepts learned to the stock prediction project.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'
Expand your knowledge of machine learning and deep learning beyond the specific stock prediction use case.
View Hands-On Machine Learning with Scikit-Learn,... on Amazon
Show steps
  • Read the chapters on neural networks and recurrent neural networks (RNNs).
  • Experiment with the code examples in the book.
  • Apply the concepts to other time series analysis problems.
Contribute to a Relevant Open Source Project
Contribute to an open-source project related to Django REST Framework, React, or machine learning. This will give you valuable experience working on real-world projects and collaborating with other developers.
Show steps
  • Find an open-source project on GitHub related to the course topics.
  • Identify a bug or feature to work on.
  • Submit a pull request with your changes.
Write a Blog Post on LSTM Networks
Deepen your understanding of LSTM networks by explaining the concepts in your own words. This will help you retain the information and identify any gaps in your knowledge.
Show steps
  • Research LSTM networks and their applications in time series analysis.
  • Outline the key concepts you want to cover in your blog post.
  • Write a clear and concise explanation of LSTM networks, including diagrams and examples.
  • Publish your blog post on a platform like Medium or your personal website.
Build a Simple To-Do List API with React Frontend
Apply your knowledge by building a complete full-stack application. This will reinforce your understanding of Django REST Framework, React, and how they work together.
Show steps
  • Design the API endpoints for creating, reading, updating, and deleting to-do items.
  • Implement the API using Django REST Framework.
  • Create a React frontend for interacting with the API.
  • Implement user authentication (optional).
  • Deploy the application to a platform like Heroku or Netlify.

Career center

Learners who complete Full Stack Machine Learning | Django REST Framework, React will develop knowledge and skills that may be useful to these careers:
Full-Stack Developer
A full stack developer is responsible for both front-end and back-end web development. This course provides a strong foundation for this role, teaching you how to build a complete application using Django REST Framework, React.js, and machine learning. The course's focus on connecting Django REST Framework with React.js to build a functional portal directly translates to the tasks a full stack developer performs. You will gain experience in data manipulation, visualization, and integrating machine learning models into web applications, skills that are highly sought after in the field.
See salaries and explore the career path for Full-Stack Developer
API Developer
An API developer designs, develops, and maintains application programming interfaces that allow different software systems to communicate with each other. This course focuses heavily on REST API development using Django REST Framework. You will learn how to create API endpoints, handle requests and responses, implement authentication, and ensure API security. The skills acquired in this course are directly applicable to the tasks an API developer performs. The course gives a great overview of how to use machine learning in the context of an API, and will allow you to better communicate with backend stakeholders.
See salaries and explore the career path for API Developer
Backend Developer
A backend developer focuses on server-side logic, databases, and APIs. This course provides in-depth training in Django REST Framework, a powerful tool for building robust APIs. You will learn how to create API endpoints, serialize data, implement user authentication, and handle database interactions. The skills acquired in this course are directly applicable to the tasks a backend developer performs, especially when building APIs for web applications. You can use this course to learn how to quickly build out a backend server that includes machine learning elements.
See salaries and explore the career path for Backend Developer
Web Developer
A web developer builds and maintains websites and web applications. This course provides a comprehensive introduction to web development using Django REST Framework and React.js. You will learn how to build both the front-end and back-end of a web application, connect them together, and implement essential features like user authentication. The hands-on project of building a stock prediction portal gives you practical experience in building a real-world web application. You will have a strong ability to create websites, regardless of the use case.
See salaries and explore the career path for Web Developer
Frontend Developer
A frontend developer specializes in building user interfaces and interactive web applications. This course provides comprehensive training in React.js, a popular JavaScript library for building dynamic user interfaces. You will learn how to create components, manage state, handle events, and build complex layouts. The skills acquired are directly applicable to the tasks a frontend developer performs. In particular, the elements of the course that focus on connecting the backend to the frontend are critical for a frontend developer to study. After taking this course, you will have familiarity in the creation of interactive, data-driven web applications.
See salaries and explore the career path for Frontend Developer
AI Application Developer
An AI application developer focuses on building applications that incorporate artificial intelligence and machine learning capabilities. This course provides hands-on experience in integrating a machine learning model with a web application. You will get a feel for using machine learning as a backend component, and how to integrate the data that it provides into a web application. The course allows you to build a fully functional stock ticker application using artificial intelligence. This experience is invaluable for AI application developers who need to build intelligent applications that solve real-world problems.
See salaries and explore the career path for AI Application Developer
Software Engineer
A software engineer designs, develops, and tests software applications. This course provides a comprehensive overview of full-stack development, covering both front-end and back-end technologies. You will gain experience in Django REST Framework, React.js, and machine learning, allowing you to contribute to various aspects of software development projects. The hands-on project of building a stock prediction portal offers valuable experience in integrating different technologies and solving real-world problems. This course is a great way to sharpen your skills as a software engineer, and learn how to create production-ready applications.
See salaries and explore the career path for Software Engineer
Technical Lead
A technical lead oversees a team of developers and guides the technical direction of a project. This course provides a broad overview of full-stack development, covering front-end, back-end, and machine learning technologies. You will learn how to integrate these different technologies and build a complete application. This experience is valuable for technical leads who need to understand the big picture and make informed decisions about technology choices. The hands-on project helps technical leads build empathy with their team.
See salaries and explore the career path for Technical Lead
Machine Learning Operations Engineer
A machine learning operations engineer deploys, monitors, and manages machine learning models in production environments. This course may be useful as it provides a practical example of integrating a machine learning model with a web application using Django REST Framework. You will learn how to create an API endpoint for the model and display the results in a user interface. This experience is valuable for machine learning operations engineers who need to deploy and maintain machine learning models in real-world applications. This course demonstrates the entire process, end to end, which is important for machine learning operations engineers.
See salaries and explore the career path for Machine Learning Operations Engineer
Machine Learning Engineer
A machine learning engineer designs, develops, and deploys machine learning models. This course offers practical experience in building and integrating an LSTM model for stock price prediction. While the course is not a comprehensive machine learning bootcamp, it provides a real-world use case that a machine learning engineer can directly relate to. You will learn how to choose the right machine learning approach, understand deep learning concepts, and integrate models with web applications. The stock prediction project offers unique hands-on knowledge to showcase to future employers. This course may be useful for those seeking to break into areas like quantitative finance.
See salaries and explore the career path for Machine Learning Engineer
Machine Learning Consultant
A machine learning consultant advises organizations on how to leverage machine learning to solve their business problems. This course may be useful as it provides a practical example of applying machine learning to a real-world problem. You will learn how to build and integrate a stock prediction model, which can be used as a case study to demonstrate the potential of machine learning to clients. Additionally, understanding the full stack, end to end process of creating an application is useful for machine learning consultants.
See salaries and explore the career path for Machine Learning Consultant
Data Scientist
A data scientist analyzes data to extract meaningful insights and develop data-driven solutions. This course may be useful as it teaches you how to manipulate and visualize data using Pandas and NumPy, skills essential for any data scientist. Furthermore, the course introduces you to machine learning concepts and their application in a real-world scenario. The project involving stock price prediction gives you experience in building and evaluating a predictive model, as well as solid experience working with time series data. Data scientists benefit from understanding the full stack, end to end process. The course is a great way to demonstrate understanding of predictive modeling.
See salaries and explore the career path for Data Scientist
Data Analyst
A data analyst collects, processes, and analyzes data to identify trends and insights. This course may be useful as it introduces you to data manipulation using Pandas and NumPy, also data visualization using Matplotlib. You will learn how to work with data, clean it, and prepare it for analysis. The stock prediction project gives you exposure to time series data and predictive modeling, which are important skills for data analysts. Using modern tools and frameworks, a data analyst can greatly benefit from the workflow taught in this course.
See salaries and explore the career path for Data Analyst
Data Engineer
A data engineer builds and maintains the infrastructure for data storage, processing, and analysis. While this course does not directly cover data engineering tools and technologies, it introduces you to data manipulation using Pandas and NumPy. You will learn how to work with data, clean it, and prepare it for analysis. This is a great starting point for aspiring data engineers who want to gain a basic understanding of data handling. The experience with time series data will be particularly relevant. Additionally, you can learn how to expose data science models using modern technologies.
See salaries and explore the career path for Data Engineer
Quantitative Analyst
A quantitative analyst, often working in the finance industry, develops and implements mathematical models for pricing and risk management. While a master's degree is typically required, this course provides a hands-on introduction to building a stock prediction model using an LSTM network. Though the model is purely for educational purposes, the experience of working with financial data, building a predictive model, and analyzing its performance can be valuable for understanding the fundamentals of quantitative analysis. The knowledge of Pandas will also be beneficial when processing financial data.
See salaries and explore the career path for Quantitative Analyst

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Full Stack Machine Learning | Django REST Framework, React.
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a comprehensive introduction to machine learning, covering essential concepts and practical techniques. It's a valuable resource for understanding the machine learning aspects of the course. It covers Scikit-Learn, Keras, and TensorFlow, which are relevant to the stock prediction project. This book will help you understand the underlying principles of the LSTM model used in the course.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
Check
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
Check
Cover image
Django for Beginners
Save
Provides a solid foundation in Django, covering essential concepts and best practices. It's a great resource for those new to the framework. It offers a step-by-step approach to building web applications with Django. While the course focuses on Django REST Framework, a strong understanding of Django itself is crucial.
Django for Beginners: Build websites with Python...
Paperback
$$

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Rating
5.0 Filled star Filled star Filled star Filled star Half star
Effort
18.5 total hours
Via
Udemy
Instructor
Rathan Kumar
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Offers a practical, hands-on approach to building a stock prediction portal, integrating Django REST Framework, React.js, and machine learning techniques, providing experience in modern web development
Develops skills in data manipulation using Pandas and NumPy, alongside data visualization using Matplotlib, which are essential for data analysis and machine learning projects
Covers the basics of machine learning, including how to choose the right approach for specific problems, and explains when and why to use deep learning with neural networks
Emphasizes the integration of machine learning models with web applications, providing a comprehensive understanding of how to deploy models in a real-world context
Focuses on building an LSTM model in Jupyter Notebook for stock price data analysis, offering practical experience in time series forecasting and neural network implementation
Explicitly states that the prediction model is for educational purposes only and should not be used for real stock market trading, which is an important disclaimer for learners to consider
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