Essential Math for Data Science
Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics
Seiten
2021
O'Reilly Media (Verlag)
978-1-0981-1556-2 (ISBN)
O'Reilly Media (Verlag)
978-1-0981-1556-2 (ISBN)
- Titel wird leider nicht erscheinen
- Artikel merken
Master the math needed to excel in data science and machine learning. If you're a data scientist who lacks a math or scientific background or a developer who wants to add data domains to your skillset, this is your book. Author Hadrien Jean provides you with a foundation in math for data science, machine learning, and deep learning.
Through the course of this book, you'll learn how to use mathematical notation to understand new developments in the field, communicate with your peers, and solve problems in mathematical form. You'll also understand what's under the hood of the algorithms you're using.
Learn how to:
Use Python and Jupyter notebooks to plot data, represent equations, and visualize space transformations
Read and write math notation to communicate ideas in data science and machine learning
Perform descriptive statistics and preliminary observation on a dataset
Manipulate vectors, matrices, and tensors to use machine learning and deep learning libraries such as TensorFlow or Keras
Explore reasons behind a broken model and be prepared to tune and fix it
Choose the right tool or algorithm for the right data problem
Through the course of this book, you'll learn how to use mathematical notation to understand new developments in the field, communicate with your peers, and solve problems in mathematical form. You'll also understand what's under the hood of the algorithms you're using.
Learn how to:
Use Python and Jupyter notebooks to plot data, represent equations, and visualize space transformations
Read and write math notation to communicate ideas in data science and machine learning
Perform descriptive statistics and preliminary observation on a dataset
Manipulate vectors, matrices, and tensors to use machine learning and deep learning libraries such as TensorFlow or Keras
Explore reasons behind a broken model and be prepared to tune and fix it
Choose the right tool or algorithm for the right data problem
Hadrien Jean is a machine learning scientist at Ava Accessibility in the domain of speech transcription. He completed his Ph.D. in cognitive science at the Ecole Normale Superieure (Paris, France) on the topic of auditory perceptual learning with a behavioral and electrophysiological approach. He has published a series of blog articles aiming at building intuition on mathematics through code and visualization (https: //hadrienj.github.io/posts/).
| Erscheint lt. Verlag | 30.6.2021 |
|---|---|
| Verlagsort | Sebastopol |
| Sprache | englisch |
| Maße | 178 x 233 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
| ISBN-10 | 1-0981-1556-2 / 1098115562 |
| ISBN-13 | 978-1-0981-1556-2 / 9781098115562 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
Stochastik: von Abweichungen bis Zufall
Buch | Softcover (2025)
De Gruyter (Verlag)
CHF 48,90
Buch | Softcover (2024)
Springer Spektrum (Verlag)
CHF 69,95
