Practical Linear Algebra For Data Science

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Practical Linear Algebra For Data Science

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Author : MIKE. COHEN
language : en
Publisher: O'Reilly Media
Release Date : 2022-11-30

Practical Linear Algebra For Data Science written by MIKE. COHEN and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-30 with Computers categories.

If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis

Practical Linear Algebra For Data Science

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Author : Mike X Cohen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-09-06

Practical Linear Algebra For Data Science written by Mike X Cohen and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-06 with Computers categories.

If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis

Practical Linear Algebra For Data Science

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Author : Mike Cohen
language : en
Publisher:
Release Date : 2022

Practical Linear Algebra For Data Science written by Mike Cohen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis.

Practical Linear Algebra For Data Science

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Author : Mike X. Cohen
language : en
Publisher:
Release Date : 2023

Practical Linear Algebra For Data Science written by Mike X. Cohen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Algebras, Linear categories.

Linear Algebra In Data Science

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Author : Peter Zizler
language : en
Publisher: Springer Nature
Release Date :

Linear Algebra In Data Science written by Peter Zizler and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Essential Math For Data Science

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Author : Thomas Nield
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-05-26

Essential Math For Data Science written by Thomas Nield and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-26 with Computers categories.

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

Data Science From Scratch

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Author : Ramon Base
language : en
Publisher:
Release Date : 2019-07-26

Data Science From Scratch written by Ramon Base and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-26 with categories.

Buy the Paperback version of this book, and get the Kindle eBook version included for FREE Do you want to know all about data science? Do you really want to understand why it is the future in all the most demanding job? The truth is: Data science is the need of the hur. Over the last few years, it has changed the concept of technology. It is adding value to every model of business by using deep learning and statistics. Technological benefits of data science cannot also be ignored. The demand for data science is increasing rapidly. The basic reason behind this is the massive boom in the data generated and retained by the companies. Also known as "big data", data scientists make the best use of this available information and figure out their best use. The team of data scientists also help in generating a good amount of analytics from the information available. This information brings clarity to people on how to interact with the web and are the foundations on which most of the critical business strategies rest. The demand for the data scientists is gradually increasing and so there must be proper learning resources for them to cultivate their knowledge. DOWNLOAD: Data Science from Scratch, Ultimate Guide to Master Data Mining and data-Analytic from Linear Algebra and Statistics to Practical Examples of Neural Networks and Machine Learning in Python. A data scientist has the following tasks to do: Analyse data Modelling/statistics Engineering/prototyping A lot of data is available in every organization. All of them are not that easy to format. A data scientist's job mainly revolves around formatting these data, keeping in mind some set of rules. The goal of this book is simple: The book provides a perfect insight into data science from the very scratch. It clearly explains the difference between data science and big data, explaining the concept of big data very concisely and clearly. Operations on data are also very clearly explained in this eBook. The book also stresses on Ultimate Guide to Master Data Mining and data-Analytic from Linear Algebra and Statistics to Practical Examples of Neural Networks and Machine Learning in Python. You will also learn: What is big data Difference between Data Science and big data Operations on data munging, cleaning, manipulating and rescaling data basic linear algebra with exercises Statistics and basic concept Decision tree and how to use them Languages required for data science Basics of python Neural network and what to use for (with examples) Machine learning (with examples) Importance of data science in job and business Who is a data scientist Data Science and its applications Would you like to know more? Download the eBook, Data Science from Scratch, and know about the trend that is ruling the world today. Scroll to the top of the page and select the buy now button.

Before Machine Learning Volume 1 Linear Algebra For A I

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Author : Jorge Brasil
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-24

Before Machine Learning Volume 1 Linear Algebra For A I written by Jorge Brasil and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-24 with Computers categories.

Unlock the essentials of linear algebra to build a strong foundation for machine learning. Dive into vectors, matrices, and principal component analysis with expert guidance in "Before Machine Learning Volume 1 - Linear Algebra." Key Features Comprehensive introduction to linear algebra for machine learning Detailed exploration of vectors and matrices In-depth study of principal component analysis (PCA) Book DescriptionIn this book, you'll embark on a comprehensive journey through the fundamentals of linear algebra, a critical component for any aspiring machine learning expert. Starting with an introductory overview, the course explains why linear algebra is indispensable for machine learning, setting the stage for deeper exploration. You'll then dive into the concepts of vectors and matrices, understanding their definitions, properties, and practical applications in the field. As you progress, the course takes a closer look at matrix decomposition, breaking down complex matrices into simpler, more manageable forms. This section emphasizes the importance of decomposition techniques in simplifying computations and enhancing data analysis. The final chapter focuses on principal component analysis, a powerful technique for dimensionality reduction that is widely used in machine learning and data science. By the end of the course, you will have a solid grasp of how PCA can be applied to streamline data and improve model performance. This course is designed to provide technical professionals with a thorough understanding of linear algebra's role in machine learning. By the end, you'll be well-equipped with the knowledge and skills needed to apply linear algebra in practical machine learning scenarios.What you will learn Understand the fundamental concepts of vectors and matrices Implement principal component analysis in data reduction Analyze the role of linear algebra in machine learning Enhance problem-solving skills through practical applications Gain the ability to interpret and manipulate high-dimensional data Build confidence in using linear algebra for data science projects Who this book is for This course is ideal for technical professionals, data scientists, aspiring machine learning engineers, and students of computer science or related fields. Additionally, it is beneficial for software developers, engineers, and IT professionals seeking to transition into data science or machine learning roles. A basic understanding of high school-level mathematics is recommended but not required, making it accessible for those looking to build a foundational understanding before diving into more advanced topics.

Linear Algebra For Data Science Machine Learning And Signal Processing

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Author : Jeffrey A. Fessler
language : en
Publisher: Cambridge University Press
Release Date : 2024-04-30

Linear Algebra For Data Science Machine Learning And Signal Processing written by Jeffrey A. Fessler and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-30 with Computers categories.

Master matrix methods via engaging data-driven applications, aided by classroom-tested quizzes, homework exercises and online Julia demos.

Practical Linear Algebra For Machine Learning

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Author : Amirsina Torfi
language : en
Publisher:
Release Date : 2019-12-26

Practical Linear Algebra For Machine Learning written by Amirsina Torfi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-26 with categories.

Machine Learning is everywhere these days and a lot of fellows desire to learn it and even master it! This burning desire creates a sense of impatience. We are looking for shortcuts and willing to ONLY jump to the main concept. If you do a simple search on the web, you see thousands of people asking "How can I learn Machine Learning?", "What is the fastest approach to learn Machine Learning?", and "What are the best resources to start Machine Learning?" \textit. Mastering a branch of science is NOT just a feel-good exercise. It has its own requirements.One of the most critical requirements for Machine Learning is Linear Algebra. Basically, the majority of Machine Learning is working with data and optimization. How can you want to learn those without Linear Algebra? How would you process and represent data without vectors and matrices? On the other hand, Linear Algebra is a branch of mathematics after all. A lot of people trying to avoid mathematics or have the temptation to "just learn as necessary." I agree with the second approach, though. \textit: You cannot escape Linear Algebra if you want to learn Machine Learning and Deep Learning. There is NO shortcut.The good news is there are numerous resources out there. In fact, the availability of numerous resources made me ponder whether writing this book was necessary? I have been blogging about Machine Learning for a while and after searching and searching I realized there is a deficiency of an organized book which \textbf teaches the most used Linear Algebra concepts in Machine Learning, \textbf provides practical notions using everyday used programming languages such as Python, and \textbf be concise and NOT unnecessarily lengthy.In this book, you get all of what you need to learn about Linear Algebra that you need to master Machine Learning and Deep Learning.