Learning Management System for different Machine Learning Algorithms by using Chatbot Test phase, srs, design phase and source code final deliverable
Project Domain / Category Web Programming
Abstract/Introduction
The field of machine learning is rapidly growing, and it is becoming increasingly important for students to have a strong foundation in this area. However, learning Machine Learning algorithms can be challenging and time-consuming. Traditional teaching methods, such as lectures and textbooks, are not always effective for all students. Therefore, the proposed system aims to develop an innovative approach to learning machine learning algorithms through a Learning Management System (LMS) that utilizes chatbot technology. The LMS will provide students with an interactive and personalized learning environment that caters to their individual needs and preferences. This system will be developed with the aim of improving the overall learning experience and knowledge retention of students.
The aim of this project is to design a Learning Management System for different Machine Learning Algorithms by using Chatbot. The users (students) of this system will be able to learn different Supervised/Unsupervised Machine Learning Algorithms. Chatbot will assist the student to select the appropriate Machine Learning algorithms of student’s choice and will demonstrate the working of the particular Algorithm with appropriate graph. Chatbot will also recommends the different ML algorithms as per their usage scenario or requirements. Proposed application aims to provide an effective academic environment to the students by providing mentoring of different ML algorithms.
Functional Requirements:
The proposed system aims to develop a Learning Management System (LMS) that utilizes chatbot technology to assist students in learning different machine learning algorithms.Student can select two categories(Supervised/Unsupervised) of Machine Learning algorithms and Chatbot will further provide sub category of different ML algorithms and their demonstration through graphs for a particular requirement.
The proposed system will have the following main users: Admin Instructor and students.
- Registration module: It will facilitate the registration process for students, instructors and Admin. Admin will approve and perform activation of the student’s accounts and registration requests.
- Login Module: After successful registrations, all types of the users will be able to login to the system using their registered email and password.
- Pop up window should be displayed at the bottom right of the system by prompting the students for any kind of guidance related to particular Machine Learning Algorithms.
- Supervised Machine Learning Algorithms: Your application will assist the students with the following seven Supervised Machine Learning Algorithms Including: Linear Regression, Logistic Regression, Decision Tree, SVM algorithm, Gradient Descent Algorithms and KNN algorithm.
- Unsupervised Machine Learning Algorithms: Furthermore, your application will demonstrate following unsupervised Machine Learning Algorithms including: K-means clustering and Neural Networks
- Demonstration of Supervised ML: For selecting the supervised learning algorithm, system should be able to demonstrate how you represent the labelled data for both discrete and continuous values variables.
- Adding Features in Supervised ML Algorithms: Your application also facilitates to add features and displays it through graph.
- Demonstration of Unsupervised ML Algorithms: Same with the case for Unsupervised Machine learning algorithms, system should be able to illustrate the clustering and association methods through graph.
- Integration of Machine Learning Algorithms: Integration of different machine learning algorithms such as supervised and unsupervised learning to optimize the learning experience for students
10.Data upload: The system should allow users to upload their data sets in various formats, such as CSV, Excel, and JSON.
11.Pre-processing: The system should provide pre-processing functions, such as cleaning and transforming data, to improve the quality of input data.
12.Model training: The system should provide a feature for model training, allowing students to choose parameters and run the model on their data.
13.Model evaluation: The system should evaluate the model’s performance based on standard metrics such as accuracy, precision, recall, and F1 score.
14.Model deployment: The system should allow students to deploy the trained model on new data for prediction.
15.Visualization: The system should provide visualization tools for the analysis of data and the results of the model.
16.Performance monitoring: The system should monitor the performance of the deployed model and provide alerts if any issues occur.
17.Progress Tracking: Module that enables students to track their progress, access their grades, and receive feedback from instructors. Progress tracking: A system that enables students to track their progress, access their grades, and receive feedback from instructors.
18.Quiz Creation and Grading: A feature that enables instructors to create quizzes and assessments, and grade them automatically or manually.
19.Performance Analysis: A system that analyzes students’ learning behavior, performance, and progress, and provides insights for instructors to improve the course content and delivery.
20.Content Management: A system that enables instructors to manage and upload different types of content such as videos, texts, images, and audio files.
21.Conversation Flow Module: Chatbot be responsible to ask the queries from student, related to ML algorithms and will suggest the most suitable type of ML algorithms as per the requirements. To make conversation flow smooth and efficient, it is important to apply the best practices for developing chatbot. For this purpose, supervised/unsupervised Machine learning and deep learning algorithms be used, while taking into account of business objectives and parents’ expectations.
22.Collaboration tools: A platform that enables students to collaborate with their peers and instructors, such as discussion forums, group projects, and peer-to-peer evaluations.
23.Historical data analysis: Chatbot will assess the student’s behavior from historical data and will guide students as per the previous queries.
24.Supervised/Unsupervised Machine learning module: For Chatbot should already be “taught / trained” common questions so that student will be guided as per the specific scenario about machine learning algorithms.
25.The proposed system should have Student-teacher interaction module: It should guide the teacher about the progress of their students for different academic activities.
26.For successful human-like interaction, visual module is also used in chatbot for perfect tone and dialect. To achieve coherence, a character is used to effectively communicate in audio synced with the text to help out the students for specific Machine Learning Algorithms.
27.There should be Frequently Asked Questions module to generate a chatbot’s list of preprogrammed queries and responses.
Tools: JSP, SQL server 2019, Python,TensorFlow,Scikit-Learn,Jupyter Notebook, PHP, MySQL, Python, Dialogflow, IBM Watson, Microsoft Bot Framework, Wit.ai, Api.ai, Chatfuel and RASA.
Supervisor:
Name: Muhammad Umar Farooq