## Video description

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

• 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

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

## 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