Basic Statistics and Regression for Machine Learning in Python

Video description

Get ready to learn the basics of machine learning and the mathematics of statistical regression, which powers almost all machine learning algorithms.

About This Video

  • A comprehensive course that includes Python coding, visualization, loops, variables, and functions
  • Manual calculation and then using Python functions/codes to understand the difference
  • Beginner to advanced mathematics and statistical concepts that cover machine learning algorithms

In Detail

This course is for ML enthusiasts who want to understand basic statistics and regression for machine learning. The course starts with setting up the environment and understanding the basics of Python language and different libraries. Next, you'll see the basics of machine learning and different types of data. After that, you'll learn a statistics technique called Central Tendency Analysis.

Post this, you'll focus on statistical techniques such as variance and standard deviation. Several techniques and mathematical concepts such as percentile, normal distribution, uniform distribution, finding z-score, linear regression, polynomial linear regression, and multiple regression with the help of manual calculation and Python functions are introduced as the course progresses.

The dataset will get more complex as you proceed ahead; you'll use a CSV file to save the dataset. You'll see the traditional and complex method of finding the coefficient of regression and then explore ways to solve it easily with some Python functions.

Finally, you'll learn a technique called data normalization or standardization, which will improve the performance of the algorithms very much compared to a non-scaled dataset.

By the end of this course, you'll gain a solid foundation in machine learning and statistical regression using Python.

Who this book is for

This course is for beginners and individuals who want to learn mathematics for machine learning. You need not have any prior experience or knowledge in coding; just be ready with your learning mindset at the highest level.

Individuals interested in learning what's actually happening behind the scenes of Python functions and algorithms (at least in a shallow layman's way) will be highly benefitted.

Basic computer knowledge and an interest to learn mathematics for machine learning is the only prerequisite for this course.

Publisher resources

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Table of contents

  1. Chapter 1 : Introduction to the Course
    1. Course Introduction and Table of Contents
  2. Chapter 2 : Environment Setup – Preparing your Computer
    1. Environment Setup – Part 1
    2. Environment Setup – Part 2
  3. Chapter 3 : Essential Components Included in Anaconda
    1. Essential Components Included in Anaconda
  4. Chapter 4 : Python Basics - Assignment
    1. Python Basics - Assignment
  5. Chapter 5 : Python Basics - Flow Control
    1. Python Basics - Flow Control – Part 1
    2. Python Basics - Flow Control – Part 2
  6. Chapter 6 : Python Basics - List and Tuples
    1. Python Basics - List and Tuples
  7. Chapter 7 : Python Basics - Dictionary and Functions
    1. Python Basics - Dictionary and Functions - Part 1
    2. Python Basics - Dictionary and Functions - Part 2
  8. Chapter 8 : NumPy Basics
    1. NumPy Basics - Part 1
    2. NumPy Basics - Part 2
  9. Chapter 9 : Matplotlib Basics
    1. Matplotlib Basics - Part 1
    2. Matplotlib Basics - Part 2
  10. Chapter 10 : Basics of Data for Machine Learning
    1. Basics of Data for Machine Learning
  11. Chapter 11 : Central Data Tendency - Mean
    1. Central Data Tendency - Mean
  12. Chapter 12 : Central Data Tendency - Median and Mode
    1. Central Data Tendency - Median and Mode - Part 1
    2. Central Data Tendency - Median and Mode - Part 2
  13. Chapter 13 : Variance and Standard Deviation Manual Calculation
    1. Variance and Standard Deviation Manual Calculation - Part 1
    2. Variance and Standard Deviation Manual Calculation - Part 2
  14. Chapter 14 : Variance and Standard Deviation using Python
    1. Variance and Standard Deviation using Python
  15. Chapter 15 : Percentile Manual Calculation
    1. Percentile Manual Calculation
  16. Chapter 16 : Percentile using Python
    1. Percentile using Python
  17. Chapter 17 : Uniform Distribution
    1. Uniform Distribution
  18. Chapter 18 : Normal Distribution
    1. Normal Distribution - Part 1
    2. Normal Distribution - Part 2
  19. Chapter 19 : Manual Z-Score calculation
    1. Manual Z score calculation
  20. Chapter 20 : Z-Score calculation using Python
    1. Z-score calculation using Python
  21. Chapter 21 : Multi Variable Dataset Scatter Plot
    1. Multi Variable Dataset Scatter Plot
  22. Chapter 22 : Introduction to Linear Regression
    1. Introduction to Linear Regression
  23. Chapter 23 : Manually Finding Linear Regression Correlation Coefficient
    1. Manually Finding Linear Regression Correlation Coefficient - Part 1
    2. Manually Finding Linear Regression Correlation Coefficient - Part 2
  24. Chapter 24 : Manually Finding Linear Regression Slope Equation
    1. Manually Finding Linear Regression Slope Equation - Part 1
    2. Manually Finding Linear Regression Slope Equation - Part 2
  25. Chapter 25 : Manually Predicting the Future Value Using Equation
    1. Manually Predicting the Future Value Using Equation
  26. Chapter 26 : Linear Regression Using Python Introduction
    1. Linear Regression Using Python Introduction
  27. Chapter 27 : Linear Regression Using Python
    1. Linear Regression Using Python - Part 1
    2. Linear Regression Using Python - Part 2
  28. Chapter 28 : Strong and Weak Linear Regression
    1. Strong and Weak Linear Regression
  29. Chapter 29 : Predicting Future Value Using Linear Regression in Python
    1. Predicting Future Value Using Linear Regression in Python
  30. Chapter 30 : Polynomial Regression Introduction
    1. Polynomial Regression Introduction
  31. Chapter 31 : Polynomial Regression Visualization
    1. Polynomial Regression Visualization
  32. Chapter 32 : Polynomial Regression Prediction and R2 Value
    1. Polynomial Regression Prediction and R2 Value
  33. Chapter 33 : Polynomial Regression Finding SD Components
    1. Polynomial Regression Finding SD Components
  34. Chapter 34 : Polynomial Regression Manual Method Equations
    1. Polynomial Regression Manual Method Equations
  35. Chapter 35 : Finding SD Components for abc
    1. Finding SD Components for abc
  36. Chapter 36 : Finding abc
    1. Finding abc
  37. Chapter 37 : Polynomial Regression Equation and Prediction
    1. Polynomial Regression Equation and Prediction
  38. Chapter 38 : Polynomial Regression coefficient
    1. Polynomial Regression coefficient
  39. Chapter 39 : Multiple Regression Introduction
    1. Multiple Regression Introduction
  40. Chapter 40 : Multiple Regression Using Python - Data Import as CSV
    1. Multiple Regression Using Python - Data Import as CSV
  41. Chapter 41 : Multiple Regression Using Python - Data Visualization
    1. Multiple Regression Using Python - Data Visualization
  42. Chapter 42 : Creating Multiple Regression Object and Prediction Using Python
    1. Creating Multiple Regression Object and Prediction Using Python
  43. Chapter 43 : Manual Multiple Regression - Intro and Finding Means
    1. Manual Multiple Regression - Intro and Finding Means
  44. Chapter 44 : Manual Multiple Regression - Finding Components
    1. Manual Multiple Regression - Finding Components - Part 1
    2. Manual Multiple Regression - Finding Components - Part 2
  45. Chapter 45 : Manual Multiple Regression - Finding abc
    1. Manual Multiple Regression - Finding abc
  46. Chapter 46 : Manual Multiple Regression Equation Prediction and Coefficients
    1. Manual Multiple Regression Equation Prediction and Coefficients
  47. Chapter 47 : Feature Scaling Introduction
    1. Feature Scaling Introduction
  48. Chapter 48 : Standardization Scaling Using Python
    1. Standardization Scaling Using Python - Part 1
    2. Standardization Scaling using Python - Part 2
  49. Chapter 49 : Standardization Scaling Using Manual Calculation
    1. Standardization Scaling Using Manual Calculation - Part 1
    2. Standardization Scaling Using Manual Calculation - Part 2

Product information

  • Title: Basic Statistics and Regression for Machine Learning in Python
  • Author(s): Abhilash Nelson
  • Release date: October 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781803238487