Essential math data science von nield thomas (17 Ergebnisse)

Paperback. Zustand: Fair. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. …

Paperback. Zustand: Good. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. …

Paperback. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Zustand: Used - Very Good. 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.…

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Zustand: New.

Paperback. Zustand: Brand New. 350 pages. 9.19x7.00x0.73 inches. In Stock.

Zustand: New. In English.

Zustand: New. 2022. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Neuware - 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. …

Zustand: New. &Uumlber den AutorThomas Nield is the founder of Nield Consulting Group as well as an instructor at O Reilly Media and University of Southern California. He enjoys making technical content relatable and relevant to those unfamiliar or i.

Taschenbuch. Zustand: Neu. Essential Math for Data Science | Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics | Thomas Nield | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2022 | O'Reilly Media | EAN 9781098102937 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Taschenbuch. Zustand: Neu. Neuware -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 marketLibri GmbH, Europaallee 1, 36244 Bad Hersfeld Englisch. …

Taschenbuch. Zustand: Neu. Neuware -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 Englisch. …