The statistics and calculus workshop a comprehensive introduction to mathematics in Python for artificial intelligence applications
With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easy Key Features Discover how most programmers use the main Python libraries when performing statistics with Python Use descriptive statisti...
| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham
Packt Publishing,
2020.
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| Subjects: | |
| Online Access: | EBSCOhost Перейти в каталог НБ ТГУ |
Table of Contents:
- Cover
- FM
- Copyright
- Table of Contents
- Preface
- Chapter 1: Fundamentals of Python
- Introduction
- Control Flow Methods
- if Statements
- Exercise 1.01: Divisibility with Conditionals
- Loops
- The while Loop
- The for Loop
- Exercise 1.02: Number Guessing Game
- Data Structures
- Strings
- Lists
- Exercise 1.03: Multi-Dimensional Lists
- Tuples
- Sets
- Dictionaries
- Exercise 1.04: Shopping Cart Calculations
- Functions and Algorithms
- Functions
- Exercise 1.05: Finding the Maximum
- Recursion
- Exercise 1.06: The Tower of Hanoi
- Algorithm Design
- Exercise 1.07: The N-Queens Problem
- Testing, Debugging, and Version Control
- Testing
- Debugging
- Exercise 1.08: Testing for Concurrency
- Version Control
- Exercise 1.09: Version Control with Git and GitHub
- Activity 1.01: Building a Sudoku Solver
- Summary
- Chapter 2: Python's Main Tools for Statistics
- Introduction
- Scientific Computing and NumPy Basics
- NumPy Arrays
- Vectorization
- Exercise 2.01: Timing Vectorized Operations in NumPy
- Random Sampling
- Working with Tabular Data in pandas
- Initializing a DataFrame Object
- Accessing Rows and Columns
- Manipulating DataFrames
- Exercise 2.02: Data Table Manipulation
- Advanced Pandas Functionalities
- Exercise 2.03: The Student Dataset
- Data Visualization with Matplotlib and Seaborn
- Scatter Plots
- Line Graphs
- Bar Graphs
- Histograms
- Heatmaps
- Exercise 2.04: Visualization of Probability Distributions
- Visualization Shorthand from Seaborn and Pandas
- Activity 2.01: Analyzing the Communities and Crime Dataset
- Summary
- Chapter 3: Python's Statistical Toolbox
- Introduction
- An Overview of Statistics
- Types of Data in Statistics
- Categorical Data
- Exercise 3.01: Visualizing Weather Percentages
- Numerical Data
- Exercise 3.02: Min-Max Scaling
- Ordinal Data
- Descriptive Statistics
- Central Tendency
- Dispersion
- Exercise 3.03: Visualizing Probability Density Functions
- Python-Related Descriptive Statistics
- Inferential Statistics
- T-Tests
- Correlation Matrix
- Exercise 3.04: Identifying and Testing Equality of Means
- Statistical and Machine Learning Models
- Exercise 3.05: Model Selection
- Python's Other Statistics Tools
- Activity 3.01: Revisiting the Communities and Crimes Dataset
- Summary
- Chapter 4: Functions and Algebra with Python
- Introduction
- Functions
- Common Functions
- Domain and Range
- Function Roots and Equations
- The Plot of a Function
- Exercise 4.01: Function Identification from Plots
- Function Transformations
- Shifts
- Scaling
- Exercise 4.02: Function Transformation Identification
- Equations
- Algebraic Manipulations
- Factoring
- Using Python
- Exercise 4.03: Introduction to Break-Even Analysis
- Systems of Equations
- Systems of Linear Equations
- Exercise 4.04: Matrix Solution with NumPy
- Systems of Non-Linear Equations