Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics by Hadrien Jean

Free digital downloadable books Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics 9781098115562 in English MOBI by Hadrien Jean

Download Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics PDF

Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics
Hadrien Jean
Page: 250
Format: pdf, ePub, mobi, fb2
ISBN: 9781098115562
Publisher: O'Reilly Media, Incorporated

Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics

Free digital downloadable books Essential Math for Data Science: Take Control of Your Data with Fundamental Calculus, Linear Algebra, Probability, and Statistics 9781098115562 in English MOBI by Hadrien Jean

Overview

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

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