MENU
Nasze ceny
![Essential Math for Data Science [DRM] - Ebook (Książka PDF) do pobrania w formacie PDF](https://cloud-cdn.virtualo.pl/covers/large/905568.jpg)
Essential Math for Data Science [DRM] - ebook
Essential Math for Data Science [DRM] - ebook
Ebook zabezpieczony DRM. Dowiedz się więcej https://www.empik.com/pomoc/faq-ebook.
Pamiętaj, ebook będzie dostępny do pobrania wyłącznie w wybranym przez Ciebie formacie.
Ebook po zakupie nie będzie dostępny do czytania w aplikacji Empik Go.
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 learningUnderstand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargonPerform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significanceManipulate vectors and matrices and perform matrix decompositionIntegrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networksNavigate 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
Pamiętaj, ebook będzie dostępny do pobrania wyłącznie w wybranym przez Ciebie formacie.
Ebook po zakupie nie będzie dostępny do czytania w aplikacji Empik Go.
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 learningUnderstand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargonPerform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significanceManipulate vectors and matrices and perform matrix decompositionIntegrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networksNavigate 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
| Kategoria: | Computer Technology |
| Język: | Angielski |
| Zabezpieczenie: | brak |
| ISBN: | 978-1-0981-0290-6 |
| Rozmiar pliku: | 12 MB |
