Basic Statistics and Regression for Machine Learning in Python

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Abhilash Nelson

5:04:49

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01.01-course introduction and table of contents.mp4

10:16

02.01-environment setup-part 1.mp4

05:25

02.02-environment setup-part 2.mp4

04:16

03.01-essential components included in anaconda.mp4

04:58

04.01-python basics-assignment.mp4

06:39

05.01-python basics-flow control-part 1.mp4

04:45

05.02-python basics-flow control-part 2.mp4

03:57

06.01-python basics-list and tuples.mp4

04:45

07.01-python basics-dictionary and functions-part 1.mp4

05:03

07.02-python basics-dictionary and functions-part 2.mp4

03:32

08.01-numpy basics-part 1.mp4

03:51

08.02-numpy basics-part 2.mp4

05:07

09.01-matplotlib basics-part 1.mp4

04:29

09.02-matplotlib basics-part 2.mp4

03:52

10.01-basics of data for machine learning.mp4

05:06

11.01-central data tendency-mean.mp4

06:12

12.01-central data tendency-median and mode-part 1.mp4

02:41

12.02-central data tendency-median and mode-part 2.mp4

03:15

13.01-variance and standard deviation manual calculation-part 1.mp4

04:52

13.02-variance and standard deviation manual calculation-part 2.mp4

06:36

14.01-variance and standard deviation using python.mp4

03:42

15.01-percentile manual calculation.mp4

04:52

16.01-percentile using python.mp4

02:59

17.01-uniform distribution.mp4

06:22

18.01-normal distribution-part 1.mp4

04:16

18.02-normal distribution-part 2.mp4

02:53

19.01-manual z score calculation.mp4

05:51

20.01-z-score calculation using python.mp4

05:09

21.01-multi variable dataset scatter plot.mp4

04:18

22.01-introduction to linear regression.mp4

04:52

23.01-manually finding linear regression correlation coefficient-part 1.mp4

06:31

23.02-manually finding linear regression correlation coefficient-part 2.mp4

06:27

24.01-manually finding linear regression slope equation-part 1.mp4

06:04

24.02-manually finding linear regression slope equation-part 2.mp4

02:49

25.01-manually predicting the future value using equation.mp4

03:00

26.01-linear regression using python introduction.mp4

03:30

27.01-linear regression using python-part 1.mp4

05:18

27.02-linear regression using python-part 2.mp4

03:59

28.01-strong and weak linear regression.mp4

04:11

29.01-predicting future value using linear regression in python.mp4

03:33

30.01-polynomial regression introduction.mp4

04:58

31.01-polynomial regression visualization.mp4

05:47

32.01-polynomial regression prediction and r2 value.mp4

04:07

33.01-polynomial regression finding sd components.mp4

05:29

34.01-polynomial regression manual method equations.mp4

05:35

35.01-finding sd components for abc.mp4

05:19

36.01-finding abc.mp4

04:03

37.01-polynomial regression equation and prediction.mp4

03:14

38.01-polynomial regression coefficient.mp4

06:19

39.01-multiple regression introduction.mp4

04:12

40.01-multiple regression using python-data import as csv.mp4

05:08

41.01-multiple regression using python-data visualization.mp4

04:47

42.01-creating multiple regression object and prediction using python.mp4

05:24

43.01-manual multiple regression-intro and finding means.mp4

05:49

44.01-manual multiple regression-finding components-part 1.mp4

05:21

44.02-manual multiple regression-finding components-part 2.mp4

04:39

45.01-manual multiple regression-finding abc.mp4

05:03

46.01-manual multiple regression equation prediction and coefficients.mp4

05:20

47.01-feature scaling introduction.mp4

04:10

48.01-standardization scaling using python-part 1.mp4

04:56

48.02-standardization scaling using python-part 2.mp4

05:22

49.01-standardization scaling using manual calculation-part 1.mp4

06:11

49.02-standardization scaling using manual calculation-part 2.mp4

03:23

Description

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

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Python

Machine Learning

Abhilash Nelson

Instructor's Courses

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.

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.