BOOKS - Regression and Machine Learning for Education Sciences Using R
Regression and Machine Learning for Education Sciences Using R - Cody Dingsen 2025 PDF | EPUB Routledge BOOKS
ECO~15 kg CO²

1 TON

Views
74591

Telegram
 
Regression and Machine Learning for Education Sciences Using R
Author: Cody Dingsen
Year: 2025
Pages: 377
Format: PDF | EPUB
File size: 54.0 MB
Language: ENG



Pay with Telegram STARS
Book Description: This book provides an overview of regression and machine learning techniques that can be applied to education sciences using R programming language. It covers topics such as linear regression, logistic regression, decision trees, random forests, clustering, and neural networks. The book also discusses the importance of understanding these techniques and their applications in education research. The Need to Study and Understand the Process of Technology Evolution In today's rapidly evolving world, it is crucial to understand the process of technology evolution and its impact on society. As technology advances at an unprecedented pace, it is essential to comprehend how these developments can shape our future and the future of our planet. This book serves as a guide to help readers grasp the significance of technological progress and its potential to transform our lives. The Need and Possibility of Developing a Personal Paradigm As we navigate this technological landscape, it is vital to develop a personal paradigm for perceiving the technological process of developing modern knowledge. By doing so, we can better understand the implications of technological advancements and how they relate to our daily lives. This book offers a framework for developing such a paradigm, enabling readers to approach technology with a more informed and critical perspective. Survival of Humanity and Unification in a Warring State The survival of humanity and the unification of people in a warring state are dependent on our ability to harness the power of technology for the greater good.
В этой книге представлен обзор методов регрессии и машинного обучения, которые могут быть применены к наукам об образовании с использованием языка программирования R. Он охватывает такие темы, как линейная регрессия, логистическая регрессия, деревья решений, случайные леса, кластеризация и нейронные сети. В книге также обсуждается важность понимания этих методов и их применения в образовательных исследованиях. Необходимость изучения и понимания процесса эволюции технологий В современном быстро развивающемся мире крайне важно понимать процесс эволюции технологий и его влияние на общество. Поскольку технологии развиваются беспрецедентными темпами, важно понять, как эти разработки могут сформировать наше будущее и будущее нашей планеты. Эта книга служит руководством, помогающим читателям понять значение технического прогресса и его потенциал для преобразования нашей жизни. Потребность и возможность развития личностной парадигмы По мере того, как мы ориентируемся в этом технологическом ландшафте, жизненно важно развивать личностную парадигму восприятия технологического процесса развития современных знаний. Таким образом, мы сможем лучше понять последствия технологических достижений и то, как они связаны с нашей повседневной жизнью. Эта книга предлагает основу для разработки такой парадигмы, позволяющей читателям подходить к технологиям с более информированной и критической точки зрения. Выживание человечества и объединение в воюющем государстве Выживание человечества и объединение людей в воюющем государстве зависят от нашей способности использовать силу технологий для всеобщего блага.
''

You may also be interested in:

Regression and Machine Learning for Education Sciences Using R
Regression and Machine Learning for Education Sciences Using R
Simple Machine Learning for Programmers Beginner|s Intro to Using Machine Learning, Deep Learning, and Artificial Intelligence for Practical Applications
Machine Learning for Beginners A Complete and Phased Beginner’s Guide to Learning and Understanding Machine Learning and Artificial Intelligence Algoritms
A Machine Learning, Artificial Intelligence Approach to Institutional Effectiveness in Higher Education
TensorFlow for Deep Learning From Linear Regression to Reinforcement Learning
Python Machine Learning The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science
Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models
Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps (English Edition)
Machine Learning for Business The Ultimate Artificial Intelligence & Machine Learning for Managers, Team Leaders and Entrepreneurs
Building Machine Learning Systems Using Python Practice to Train Predictive Models and Analyze Machine Learning Results
Machine Learning for Beginners An Introductory Guide to Learn and Understand Artificial Intelligence, Neural Networks and Machine Learning
Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands
Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices
Machine Learning Master Machine Learning Fundamentals for Beginners, Business Leaders and Aspiring Data Scientists
Machine Learning for Data Streams with Practical Examples in MOA (Adaptive Computation and Machine Learning series)
Online Machine Learning: A Practical Guide with Examples in Python (Machine Learning: Foundations, Methodologies, and Applications)
Machine Learning The Ultimate Guide to Understand AI Big Data Analytics and the Machine Learning’s Building Block Application in Modern Life
Machine Learning A Guide to PyTorch, TensorFlow, and Scikit-Learn Mastering Machine Learning With Python
Machine Learning with Core ML 2 and Swift A beginner-friendly guide to integrating machine learning into your apps
Machine Learning A Guide to PyTorch, TensorFlow, and Scikit-Learn Mastering Machine Learning With Python
Robust Machine Learning: Distributed Methods for Safe AI (Machine Learning: Foundations, Methodologies, and Applications)
Machine Learning for Beginners Build and deploy Machine Learning systems using Python, 2nd Edition
Machine Learning: A Guide to PyTorch, TensorFlow, and Scikit-Learn: Mastering Machine Learning With Python
Programming Machine Learning Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning
Programming Machine Learning Machine Learning Basics Concepts + Artificial Intelligence + Python Programming + Python Machine Learning
Programming With Python 4 Manuscripts - Deep Learning With Keras, Convolutional Neural Networks In Python, Python Machine Learning, Machine Learning With Tensorflow
Computer Programming This Book Includes Machine Learning for Beginners, Machine Learning with Python, Deep Learning with Python, Python for Data Analysis
Pragmatic Machine Learning with Python Learn How to Deploy Machine Learning Models in Production
Machine Learning for Finance Master Financial Strategies with Python-Powered Machine Learning
Machine Learning for Finance Master Financial Strategies with Python-Powered Machine Learning
Machine Learning, Animated (Chapman and Hall CRC Machine Learning and Pattern Recognition)
Machine Learning for Beginners A Practical Guide to Understanding and Applying Machine Learning Concepts
Machine Learning for Absolute Beginners An Absolute beginner’s guide to learning and understanding machine learning successfully
Machine Learning with Python The Ultimate Guide to Learn Machine Learning Algorithms. Includes a Useful Section about Analysis, Data Mining and Artificial Intelligence in Business Applications
Machine Learning Tutorial: Machine Learning Simply Easy Learning
Machine Learning The Ultimate Guide to Understand Artificial Intelligence and Big Data Analytics. Learn the Building Block Algorithms and the Machine Learning’s Application in the Modern Life
Machine Learning Interviews Kickstart Your Machine Learning and Data Career (Final)
Introduction to Machine Learning (Adaptive Computation and Machine Learning), 4th Edition
Machine Learning An In-Depth Beginners Guide into the Essentials of Machine Learning Algorithms