Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical eBooks all over the World
 
From Data to Intelligence: An Introduction to Machine Learning and Artificial Intelligence
🌠 Top Free C++ Books - 100% Free or Open Source!
  • Title: From Data to Intelligence: An Introduction to Machine Learning and Artificial Intelligence
  • Author(s) Manfred Füllsack
  • Publisher: Graz University Library Publishing (2024); eBook (Creative Commons Licensed)
  • License(s): Creative Commons License (CC)
  • Paperback: N/A
  • eBook: PDF
  • Language: English
  • ISBN-10: N/A
  • ISBN-13: 978-3-903374-31-7
  • Share This:  

Book Description

This book introduces and explains essential prerequisites for understanding, applying, researching, and further developing the tools currently debated under the terms Machine Learning (ML) and Artificial Intelligence (AI).

About the Authors
  • N/A
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Data Journeys in the Sciences (Sabina Leonelli, et al.)

    This groundbreaking, open access book analyses and compares data practices across several fields through the analysis of specific cases of data journeys. How such journeys affect the use of data as evidence and the knowledge being produced.

  • An Introduction to Data Mining (Dr. Saed Sayad)

    This book presents fundamental concepts and algorithms for those learning data mining for the first time, provides both theoretical and practical coverage of all data mining topics. Includes extensive number of integrated examples and figures.

  • Mining Social Media: Finding Stories in Internet Data

    This book shows you how to use Python and key data analysis tools to find the stories buried in social media. Perform advanced data analysis using Python, Jupyter Notebooks, and the Pandas library.

  • Data Mining for the Masses (Matthew North)

    This book uses simple examples, clear explanations and free, powerful, easy-to-use software to teach you the basics of data mining; techniques that can help you answer some of your toughest business questions.

  • A Programmer's Guide to Data Mining (Ron Zacharski)

    This book is a tool for learning basic data mining techniques. If you are a programmer interested in learning a bit about data mining you might be interested in a beginner's hands-on guide as a first step. That's what this book provides.

  • Advanced Data Analysis from an Elementary Point of View

    This is a textbook on data analysis methods, intended for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It presumes that you can read and write simple functions in R.

  • Mining of Massive Datasets (Jure Leskovec, et al)

    It focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically.

  • Bayesian Data Analysis (Andrew Gelman, et al.)

    This classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. It takes an applied approach to analysis using up-to-date Bayesian methods.

  • Data Mining and Analysis: Fundamental Concepts and Algorithms

    This textbook provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.

  • Answering Questions with Data (Matthew Crump, et al.)

    This is a free textbook teaching introductory statistics for undergraduates. Students will learn to select an appropriate data analysis technique, carry out the analysis, and draw appropriate conclusions.

Book Categories
:
Other Categories
Resources and Links