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

    (By Hadrien Jean)

    Book Cover Watermark PDF Icon Read Ebook
    ×
    Size 26 MB (26,085 KB)
    Format PDF
    Downloaded 654 times
    Last checked 13 Hour ago!
    Author Hadrien Jean
    “Book Descriptions: 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”

    Google Drive Logo DRIVE
    Book 1

    Hands-On Machine Learning with Scikit-Learn and TensorFlow

    ★★★★★

    Aurélien Géron

    Book 1

    Infinite Powers: How Calculus Reveals the Secrets of the Universe

    ★★★★★

    Steven H. Strogatz

    Book 1

    The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma

    ★★★★★

    Mustafa Suleyman

    Book 1

    Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks

    ★★★★★

    Will Kurt

    Book 1

    Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

    ★★★★★

    Chip Huyen

    Book 1

    The Signal and the Noise: Why So Many Predictions Fail—But Some Don't

    ★★★★★

    Nate Silver

    Book 1

    Co-Intelligence: Living and Working with AI

    ★★★★★

    Ethan Mollick

    Book 1

    Head First Statistics: A Brain-Friendly Guide

    ★★★★★

    Dawn Griffiths

    Book 1

    The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

    ★★★★★

    Ben Horowitz

    Book 1

    When Einstein Walked with Gödel: Excursions to the Edge of Thought

    ★★★★★

    Jim Holt

    Book 1

    Natural Language Processing with Transformers: Building Language Applications with Hugging Face

    ★★★★★

    Lewis Tunstall

    Book 1

    Naked Statistics: Stripping the Dread from the Data

    ★★★★★

    Charles Wheelan

    Book 1

    Anything You Want

    ★★★★★

    Derek Sivers

    Book 1

    Learning Go: An Idiomatic Approach to Real-World Go Programming

    ★★★★★

    Jon Bodner

    Book 1

    Fundamentals of Software Architecture: An Engineering Approach

    ★★★★★

    Mark Richards

    Book 1

    R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

    ★★★★★

    Hadley Wickham