A guide to understand the basics of machine learning and its application in the field of education
Key Features
● Create a more efficient and effective learning environment that meets the needs of all students.
● Learn how to use the profound Machine learning advancements in the field of education.
● Understand how students can use machine learning tools to improve their learning and track their progress.
Description
"Machine Learning for Education" is a book that explores the application of machine learning techniques in the field of education. It covers a wide range of topics, including personalizing learning using machine learning, improving student outcomes, and optimizing educational resources. The book delves into various machine learning techniques such as supervised and unsupervised learning, deep learning, and reinforcement learning, and how they can be applied to different areas of education such as curriculum design, assessment, and student engagement.
The book takes a novel approach to explain the applications of machine learning in education through relatable real-life examples, existing problems and proposing thought-provoking solutions to the same. At the end of each chapter, there are crossword puzzles, word grids, and comprehensive exercises designed to reinforce the concepts discussed in the chapter and ensure a thorough understanding.
After reading the book, you will have gained the ability to implement the various features and solutions offered by machine learning within the field of education.
What you will learn
● Get familiar with the fundamentals of Machine Learning.
● Explore different Machine learning solutions for primary and secondary education.
● Discover ways to use Machine learning enhanced teaching and learning.
● Understand how AI can be used to assess your child's academic progress.
● Understand how Machine learning can assist educational administrators.
Who this book is for
The book is suitable for anyone who is directly or indirectly involved in the current education landscape, including students, parents, teachers, and educational administrators. No previous knowledge of machine learning is required.
Table of Contents
1. Basics of Machine Learning
2. Machine Learning for Students
3. Machine Learning for Parents
4. Machine Learning for Teachers
5. Machine Learning for Educational Administrators
Sankha Das is a final-year Computer Science student at BITS Pilani, Rajasthan, India, and a student member of the IEEE Gujarat Section. He has been actively involved in domains of software engineering, machine learning, cyber security, and blockchain research. He takes an avid interest in applications of machine learning and digital privacy to areas of social good such as healthcare, ed-tech, and education for children with special needs.
Pulkit Sinha is a Senior Data Scientist involved in various projects. His contributions to the AI and ML industry across multiple sectors in the past two years are a well-established fact in the AI industry. His passion for data science provides him to work on meticulous details calmly. Non-disclosure agreements mainly bind his work; however, a fragment of his work is available in the public domain through his official engagement with Yushu Excellence Technologies Pvt. Ltd as a senior data scientist.
