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Tag: machine learning: an applied mathematics introduction

  • Analyzing Data with Power BI and Power Pivot for Excel (Business Skills)


    Price: $39.99
    (as of Jan 22,2025 10:51:04 UTC – Details)




    Publisher ‏ : ‎ Microsoft Press; 1st edition (April 19, 2017)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 256 pages
    ISBN-10 ‏ : ‎ 150930276X
    ISBN-13 ‏ : ‎ 978-1509302765
    Item Weight ‏ : ‎ 14.7 ounces
    Dimensions ‏ : ‎ 7.35 x 0.8 x 8.95 inches

    Customers say

    Customers find the book provides practical examples and useful information for building data models. They describe it as a valuable reference for finance analysts and advanced Excel users.

    AI-generated from the text of customer reviews


    Analyzing Data with Power BI and Power Pivot for Excel (Business Skills)

    In today’s data-driven world, being able to effectively analyze and interpret data is a crucial skill for business professionals. With tools like Power BI and Power Pivot for Excel, you can easily manipulate and visualize large datasets to uncover valuable insights and make informed decisions.

    Power BI is a powerful business intelligence tool that allows you to connect to various data sources, create interactive reports and dashboards, and share insights with colleagues. Power Pivot, on the other hand, is an Excel add-in that enables you to perform advanced data analysis and modeling within the familiar Excel environment.

    In this post, we will explore how to leverage these tools to analyze data and extract actionable insights. We will cover topics such as importing data, creating relationships between different datasets, building calculated columns and measures, and creating interactive visualizations.

    By mastering Power BI and Power Pivot for Excel, you can unlock the full potential of your data and gain a competitive edge in today’s fast-paced business environment. Stay tuned for more tips and tricks on how to harness the power of these tools for effective data analysis.
    #Analyzing #Data #Power #Power #Pivot #Excel #Business #Skills,machine learning: an applied mathematics introduction

  • Introduction to Machine Learning (Adaptive Computation and Machine Learning s…



    Introduction to Machine Learning (Adaptive Computation and Machine Learning s…

    Price : 99.34

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    Introduction to Machine Learning (Adaptive Computation and Machine Learning series)

    Machine learning is a rapidly growing field that is revolutionizing various industries and sectors. In simple terms, machine learning is a subset of artificial intelligence that involves the development of algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.

    The book “Introduction to Machine Learning” from the Adaptive Computation and Machine Learning series provides a comprehensive overview of the fundamental concepts, algorithms, and techniques in machine learning. Written by renowned experts in the field, this book covers topics such as supervised and unsupervised learning, reinforcement learning, deep learning, and more.

    Whether you are a beginner looking to delve into the world of machine learning or an experienced practitioner seeking to deepen your understanding, this book offers a valuable resource that will help you grasp the core principles and applications of machine learning. With practical examples, exercises, and case studies, “Introduction to Machine Learning” is an essential guide for anyone interested in harnessing the power of data-driven decision-making.

    So, grab a copy of this book and embark on your journey into the exciting and transformative realm of machine learning. Explore the endless possibilities and potentials that await you in this dynamic field, and unlock the key to unlocking the true potential of artificial intelligence.
    #Introduction #Machine #Learning #Adaptive #Computation #Machine #Learning #s..,machine learning: an applied mathematics introduction

  • Introduction to Machine Learning in Quantitative Finance, Paperback by Ni, Ha…



    Introduction to Machine Learning in Quantitative Finance, Paperback by Ni, Ha…

    Price : 56.32

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    Introduction to Machine Learning in Quantitative Finance, Paperback by Ni, Hao

    Are you interested in learning how machine learning can be applied in the field of quantitative finance? Look no further than Hao Ni’s comprehensive guide, “Introduction to Machine Learning in Quantitative Finance.”

    In this book, Ni explores the various ways in which machine learning techniques can be used to analyze and predict financial data, including stock prices, market trends, and risk management. From regression analysis to neural networks, readers will gain a deep understanding of how machine learning algorithms can be leveraged to make more informed investment decisions.

    Whether you are a seasoned finance professional looking to enhance your analytical skills or a student interested in the intersection of finance and technology, “Introduction to Machine Learning in Quantitative Finance” is a must-read. Pick up your copy today and start unlocking the potential of machine learning in the world of finance.
    #Introduction #Machine #Learning #Quantitative #Finance #Paperback #Ha..,machine learning: an applied mathematics introduction

  • Machine Learning: An Overview Of Artificial Intelligence



    Machine Learning: An Overview Of Artificial Intelligence

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    Machine Learning: An Overview Of Artificial Intelligence

    Artificial intelligence (AI) is a rapidly growing field that has the potential to revolutionize industries ranging from healthcare to transportation. One of the key components of AI is machine learning, a subset of AI that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data.

    Machine learning algorithms can be classified into three broad categories: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, meaning that the input data is paired with the correct output. The goal of the algorithm is to learn a mapping from inputs to outputs that can then be used to make predictions on new, unseen data.

    Unsupervised learning, on the other hand, involves training the algorithm on unlabeled data, meaning that the input data is not paired with the correct output. The goal of unsupervised learning is to discover hidden patterns or structures in the data, such as clustering similar data points together.

    Reinforcement learning is a type of machine learning where the algorithm learns by interacting with its environment and receiving feedback in the form of rewards or penalties. The algorithm’s goal is to learn a policy that maximizes its cumulative reward over time.

    Machine learning has a wide range of applications, including natural language processing, computer vision, healthcare, finance, and more. Companies are increasingly using machine learning algorithms to automate tasks, improve decision-making, and gain insights from large amounts of data.

    Overall, machine learning is a powerful tool that is driving the advancement of artificial intelligence and has the potential to transform industries and improve our everyday lives.
    #Machine #Learning #Overview #Artificial #Intelligence,machine learning: an applied mathematics introduction

  • Introduction To Machine Learning With R: Rigorous Mathematical Analysis



    Introduction To Machine Learning With R: Rigorous Mathematical Analysis

    Price : 53.07

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    Machine learning is a powerful tool that allows computers to learn from data and make predictions or decisions without being explicitly programmed. In this post, we will explore the basics of machine learning with R, a popular programming language for statistical analysis and data visualization.

    To truly understand machine learning, it’s important to have a solid foundation in mathematics. In this introduction, we will delve into the rigorous mathematical analysis behind machine learning algorithms and how they work in R.

    Key topics that will be covered include:

    – Linear algebra: Understanding matrices and vectors, as well as operations such as matrix multiplication and transposition.
    – Calculus: Grasping concepts such as differentiation and integration, which are crucial for optimizing machine learning models.
    – Probability theory: Learning about probability distributions, random variables, and how they are used in machine learning.

    We will also delve into specific machine learning algorithms, such as linear regression, logistic regression, and decision trees, and demonstrate how they can be implemented in R.

    By the end of this post, you will have a solid understanding of the mathematical principles behind machine learning and how to apply them in R. Stay tuned for more in-depth tutorials and practical examples in future posts!
    #Introduction #Machine #Learning #Rigorous #Mathematical #Analysis,machine learning: an applied mathematics introduction

  • Introduction to Machine Learning with R: Rigorous Mathematical Analysis: New



    Introduction to Machine Learning with R: Rigorous Mathematical Analysis: New

    Price : 24.52

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    In this post, we will be exploring the world of machine learning with R through a rigorous mathematical analysis. Machine learning is a powerful tool that allows computers to learn from data and make predictions or decisions without being explicitly programmed. R is a popular programming language for data analysis and statistical computing, making it an ideal platform for implementing machine learning algorithms.

    Through this series, we will delve into the fundamental principles of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and more. We will explore the underlying mathematical concepts behind these algorithms, such as linear algebra, calculus, and probability theory, to gain a deeper understanding of how they work.

    By the end of this series, you will have a solid foundation in machine learning with R and be able to apply these techniques to real-world data analysis tasks. So, buckle up and get ready to embark on an exciting journey into the world of machine learning with R!
    #Introduction #Machine #Learning #Rigorous #Mathematical #Analysis,machine learning: an applied mathematics introduction

  • Genetic Programming: An Introduction (The Morgan Kaufmann Series in Artificial Intelligence)


    Price: $8.98
    (as of Jan 22,2025 09:51:29 UTC – Details)




    ASIN ‏ : ‎ B002ACPAI8
    Publisher ‏ : ‎ Morgan Kaufmann; 1st edition (February 24, 1998)
    Publication date ‏ : ‎ February 24, 1998
    Language ‏ : ‎ English
    File size ‏ : ‎ 6379 KB
    Text-to-Speech ‏ : ‎ Not enabled
    Enhanced typesetting ‏ : ‎ Not Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 496 pages


    Genetic Programming: An Introduction (The Morgan Kaufmann Series in Artificial Intelligence)

    Genetic programming is a powerful and innovative approach to solving complex problems in artificial intelligence. In this book, readers will find a comprehensive introduction to the principles and techniques of genetic programming, as well as practical guidance on how to apply this cutting-edge technology to real-world challenges.

    Written by leading experts in the field, this book covers the fundamentals of genetic programming, including the underlying principles of evolutionary algorithms and genetic programming, as well as advanced topics such as multi-objective optimization and symbolic regression. Readers will also learn how to design and implement genetic programming systems, and how to evaluate and compare different approaches.

    Whether you are a student, researcher, or practitioner in the field of artificial intelligence, this book will provide you with the knowledge and tools you need to harness the power of genetic programming. With its clear and accessible writing style, Genetic Programming: An Introduction is an essential resource for anyone interested in exploring the potential of this exciting technology.
    #Genetic #Programming #Introduction #Morgan #Kaufmann #Series #Artificial #Intelligence,machine learning: an applied mathematics introduction

  • Introduction To Machine Learning In Quantitative Finance, An (Advanced Textbook,



    Introduction To Machine Learning In Quantitative Finance, An (Advanced Textbook,

    Price : 81.99

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    Machine learning has revolutionized the field of quantitative finance, allowing for more accurate predictions and faster decision-making processes. In this advanced textbook, we will explore the intersection of machine learning and quantitative finance, providing a comprehensive introduction to the topic.

    This textbook is designed for students and professionals in the finance industry who are looking to deepen their understanding of machine learning techniques and how they can be applied to financial data. We will cover a range of topics, including supervised and unsupervised learning, reinforcement learning, deep learning, and more.

    Throughout the book, we will provide real-world examples and case studies to help readers understand how machine learning algorithms can be used to analyze financial data, predict market trends, and optimize investment strategies. By the end of the book, readers will have a solid foundation in machine learning techniques and their applications in quantitative finance.

    Whether you are a seasoned finance professional looking to enhance your skills or a student interested in the cutting-edge intersection of finance and technology, this textbook is a valuable resource for anyone looking to stay ahead in the rapidly evolving field of quantitative finance.
    #Introduction #Machine #Learning #Quantitative #Finance #Advanced #Textbook,machine learning: an applied mathematics introduction

  • Using R for Introductory Statistics (Chapman & Hall/CRC The R Series)


    Price: $61.95 – $38.91
    (as of Jan 22,2025 08:54:17 UTC – Details)




    Publisher ‏ : ‎ Chapman and Hall/CRC; 1st edition (November 29, 2004)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 432 pages
    ISBN-10 ‏ : ‎ 1584884509
    ISBN-13 ‏ : ‎ 978-1584884507
    Item Weight ‏ : ‎ 1.6 pounds
    Dimensions ‏ : ‎ 6.75 x 1.25 x 9.5 inches

    Customers say

    Customers find this book provides an easy-to-understand introduction to R and basic statistics. It’s suitable for beginners, using a hands-on approach. Many consider it a helpful reference for their thesis projects and work. However, some customers feel the index is poor.

    AI-generated from the text of customer reviews


    Are you looking to dive into the world of statistics using R? Look no further than “Using R for Introductory Statistics,” part of the Chapman & Hall/CRC The R Series. This comprehensive book is perfect for beginners looking to gain a solid foundation in statistics using the powerful R programming language.

    With step-by-step guidance, clear explanations, and practical examples, this book covers essential statistical concepts such as hypothesis testing, regression analysis, and data visualization using R. Whether you’re a student, researcher, or data analyst, this book will help you develop the skills needed to analyze and interpret data effectively.

    So why wait? Pick up “Using R for Introductory Statistics” today and start mastering the fundamentals of statistics with R.
    #Introductory #Statistics #Chapman #HallCRC #Series,machine learning: an applied mathematics introduction

  • The Algorithm Design Manual


    Price: $17.31
    (as of Jan 22,2025 07:54:41 UTC – Details)




    Publisher ‏ : ‎ Springer-Nature New York Inc; 2nd edition (October 5, 2008)
    Language ‏ : ‎ English
    Hardcover ‏ : ‎ 730 pages
    ISBN-10 ‏ : ‎ 1848000693
    ISBN-13 ‏ : ‎ 978-1848000698
    Item Weight ‏ : ‎ 2.94 pounds
    Dimensions ‏ : ‎ 7.3 x 1.4 x 9.3 inches

    Customers say

    Customers find the book provides a comprehensive guide through algorithms and data structures with great explanations. They appreciate the engaging story-telling that helps the reasoning sink in. Many consider it a good value for money, considering run-time costs at every step. The author is described as passionate about the subject and has a real passion for computer science. However, some readers report issues with missing pages and complicated grammar. Opinions differ on the readability, with some finding it easy to read and well-written, while others find it difficult to understand with complex grammar and professional slang. There are mixed views on the algorithm approach, with some finding it original and pragmatic, while others feel it lacks depth on implementation.

    AI-generated from the text of customer reviews


    In today’s digital age, algorithms play a crucial role in shaping the way we interact with technology. Whether it’s search engines, social media platforms, or online shopping websites, algorithms determine what content we see and how we navigate through the digital world.

    One book that has become a must-read for anyone interested in algorithm design is “The Algorithm Design Manual” by Steven S. Skiena. This comprehensive guide covers a wide range of topics, from basic data structures and sorting algorithms to advanced topics like graph algorithms and dynamic programming.

    What sets this book apart is its practical approach to algorithm design. Skiena not only explains the theory behind each algorithm but also provides real-world examples and exercises to help readers understand how to apply these concepts in their own projects.

    Whether you’re a student studying computer science, a software engineer looking to improve your algorithmic skills, or just someone curious about how algorithms work, “The Algorithm Design Manual” is a valuable resource that will deepen your understanding of this fascinating field. So why not pick up a copy and dive into the world of algorithms today?
    #Algorithm #Design #Manual,machine learning: an applied mathematics introduction

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