Cover image for Essential Math for Data Science

Essential Math for Data Science

Author Hadrien Jean
Release Date: 2020/09/01
ISBN: 9781098115555
Topic:
0%
7
Chapters
0-1
Hours read
0k
Total Words
    Start Reading Now    
       Add to Wishlist       
View table of contents

Book Description

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

Table of Contents

  1. 1. Basic Algebra
    1. 1.1 Variables
      1. 1.1.1 From Computer Programming to Calculus
      2. 1.1.2 Unknowns
      3. 1.1.3 Dependent And Independent Variables
    2. 1.2 Equations And Inequalities
      1. 1.2.1 Equations
      2. 1.2.2 Inequalities
      3. 1.2.3 Hands-On Project: Paris Apartments
    3. 1.3 Functions
      1. 1.3.1 From Equations To Functions
      2. 1.3.2 Computer Programming And Mathematical Functions
      3. 1.3.3 Nonlinear Functions
      4. 1.3.4 Inverse Function
      5. 1.3.5 Hands-On Project: Activation Function
  2. 2. Math On The Cartesian Plane
    1. 2.1 Coordinates And Vectors
      1. 2.1.1 Geometric Vectors
      2. 2.1.2 Coordinate Vectors
      3. 2.1.3 Hands-On Project: Images As Model Inputs
    2. 2.2 Distance formula
      1. 2.2.1 Definitions
      2. 2.2.2 Hands-On Project: k-Nearest Neighbors
    3. 2.3 Graphical Representation of Equations And Inequalities
      1. 2.3.1 Intuition
      2. 2.3.2 How To Plot Equations
      3. 2.3.3 Solving Equations Graphically
      4. 2.3.4 Inequalities
    4. 2.4 Slope And Intercept
      1. 2.4.1 Slope
      2. 2.4.2 Intercept
    5. 2.5 Nonlinear functions
      1. 2.5.1 Definition
      2. 2.5.2 Function Shape
    6. 2.6 Hands-On Project: MSE Cost Function With One Parameter
      1. 2.6.1 Cost function
      2. 2.6.2 Mathematical Definition of the Cost Function
      3. 2.6.3 Implementation
  3. 3. Calculus
    1. 3.1 Derivatives
      1. 3.1.1 Insights
      2. 3.1.2 Mathematical Definition of Derivative
      3. 3.1.3 Derivatives of Linear And Nonlinear Functions
      4. 3.1.4 Derivative Rules
      5. 3.1.5 Hands-On Project: Derivative Of The MSE Cost Function
    2. 3.2 Integrals And Area Under The Curve
      1. 3.2.1 Insights
      2. 3.2.2 Mathematical Definition
      3. 3.2.3 Hands-On Project: The ROC Curve
    3. 3.3 Partial Derivatives And Gradients
      1. 3.3.1 Partial Derivatives
      2. 3.3.2 Gradient
    4. 3.4 Hands-On Project: MSE Cost Function With Two Parameters
      1. 3.4.1 The Cost Function
      2. 3.4.2 Partial Derivatives
  4. 4. Scalars and Vectors
    1. 4.1 Introduction
      1. 4.1.1 Vector Spaces
      2. 4.1.2 Coordinate Vectors
    2. 4.2 Special Vectors
      1. 4.2.1 Unit Vectors
      2. 4.2.2 Basis Vectors
      3. 4.2.3 Zero Vectors
      4. 4.2.4 Row and Columns Vectors
      5. 4.2.5 Orthogonal Vectors
    3. 4.3 Operations and Manipulations on Vectors
      1. 4.3.1 Scalar Multiplication
      2. 4.3.2 Vector Addition
      3. 4.3.3 Using Addition and Scalar Multiplication
      4. 4.3.4 Transposition
      5. 4.3.5 Operations on Other Vector Types - Functions
    4. 4.4 Norms
      1. 4.4.1 Definitions
      2. 4.4.2 Examples of Norms
      3. 4.4.3 Norm Representations
    5. 4.5 The Dot Product with vectors
      1. 4.5.1 Definition
      2. 4.5.2 Geometric interpretation
      3. 4.5.3 Properties
      4. 4.5.4 Hands-on Project: Vectorizing the Squared L 2 Norm with the Dot Product
    6. 4.6 Hands-on Project: Regularization
3.129.70.63