Basic Statistics and Regression for Machine Learning in Python

Description

This course is a perfect supplement for ML enthusiasts. If you are only just beginning your adventures in machine learning and want to know the basics of statistics and regression used for machine learning, then go for it. Discover how you can level up and gain confidence to implement statistical methods and regression in machine learning with Python.

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. All the code files and related files are available on the GitHub repository at https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-Machine-Learning-in-Python

Set up the environment
Learn central tendency analysis
Learn statistical models and analysis
Learn regression models and analysis
Use NumPy, matplotlib, and scikit-learn libraries
Learn the data normalization or standardization technique

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.

This is a comprehensive and hands-on course to learn from basic to advanced mathematics and statistical concepts that cover machine learning algorithms. The instructor will take you through every step of the code.

The instructor shows both the manual calculation approach and then the Python functions to work around in solving statistical and regression problems.

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

https://github.com/PacktPublishing/Basic-Statistics-and-Regression-for-Machine-Learning-in-Python

Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.

1. Course Introduction and Table of Contents

2. Environment Setup: Preparing your Computer

3. Essential Components Included in Anaconda

4. Python Basics - Assignment

5. Python Basics - Flow Control

6. Python Basics - List and Tuples

7. Python Basics - Dictionary and Functions

8. Numpy Basics

9. Matplotlib Basics

10. Basics of Data for Machine Learning

11. Central Data Tendency - Mean

12. Central Data Tendency - Median and Mode

13. Variance and Standard Deviation Manual Calculation

14. Variance and Standard Deviation using Python

15. Percentile Manual Calculation

16. Percentile using Python

17. Uniform Distribution

18. Normal Distribution

19. Manual Z score calculation

20. Z score calculation using python

21. Multi Variable Dataset Scatter Plot

22. Introduction to Linear Regression

23. Manually finding Linear Regression Correlation Coefficient

24. Manually finding Linear Regression Slope Equation

25. Manually Predicting the Future Value using Equation

26. Linear Regression using Python Introduction

27. Linear Regression using Python

28. Strong and Weak Linear Regression

29. Predicting Future value using Linear Regression in Python

30. Polynomial Regression Introduction

31. Polynomial Regression Visualization

32. Polynomial Regression Prediction and R2 value

33. Polynomial Regression Finding SD Components

34. Polynomial Regression Manual Method Equations

35. Finding SD components for abc

36. Finding abc

37. Polynomial Regression Equation and Prediction

38. Polynomial Regression coefficient

39. Multiple Regression Introduction

40. Multiple Regression using Python - Part 1 - Data Import as CSV

41. Multiple Regression using Python - Part 2 - Data Visualization

42. Creating Multiple Regression Object and Prediction using Python

43. Manual Multiple Regression - Intro and Finding Means

44. Manual Multiple Regression - Finding Components

45. Manual Multiple Regression - Finding a b c

46. Manual Multiple Regression Equation Prediction and Coefficients

47. Feature Scaling Introduction

48. Standardization Scaling using Python

49. Standardization Scaling using Manual Calculation

50. Further Learning References and Resource Download