Mastering Probability and Statistics in Python

Focused View

AI Sciences

12:24:54

148 View

01.01-introduction to the instructor.mp4

12:36

01.02-focus of the course.mp4

10:15

02.01-probability versus statistics.mp4

06:14

03.01-definition of set.mp4

08:31

03.02-cardinality of a set.mp4

15:43

03.03-subsets power set and universal set.mp4

06:39

03.04-python practice subsets.mp4

07:23

03.05-power sets solution.mp4

14:57

03.06-operations.mp4

11:51

03.07-python practice operations.mp4

07:37

03.08-venn diagrams operations.mp4

06:37

03.09-homework.mp4

04:10

04.01-random experiment.mp4

06:04

04.02-outcome and sample space.mp4

10:01

04.03-event.mp4

07:36

04.04-recap and homework.mp4

05:20

05.01-probability model.mp4

09:54

05.02-probability axioms.mp4

11:49

05.03-probability axioms derivations.mp4

05:03

05.04-probability models example.mp4

06:41

05.05-more examples of probability models.mp4

06:17

05.06-probability models continuous.mp4

07:07

05.07-conditional probability.mp4

10:49

05.08-conditional probability example.mp4

10:49

05.09-conditional probability formula.mp4

07:18

05.10-conditional probability in machine learning.mp4

19:11

05.11-conditional probability total probability theorem.mp4

07:55

05.12-probability models independence.mp4

06:05

05.13-probability models conditional independence.mp4

07:13

05.14-probability models bayes rule.mp4

06:28

05.15-probability models towards random variables.mp4

11:25

05.16-homework.mp4

01:05

06.01-introduction.mp4

09:21

06.02-random variables examples.mp4

08:28

06.03-bernoulli random variables.mp4

11:52

06.04-bernoulli trail python practice.mp4

15:27

06.05-geometric random variable.mp4

08:32

06.06-geometric random variable normalization proof optional.mp4

06:32

06.07-geometric random variable python practice.mp4

15:06

06.08-binomial random variables.mp4

06:55

06.09-binomial python practice.mp4

10:58

06.10-random variables in real datasets.mp4

22:54

06.11-homework.mp4

01:32

07.01-zero probability to individual values.mp4

08:16

07.02-probability density functions.mp4

14:21

07.03-uniform distribution.mp4

05:58

07.04-uniform distribution python.mp4

05:11

07.05-exponential.mp4

03:33

07.06-exponential python.mp4

08:11

07.07-gaussian random variables.mp4

07:11

07.08-gaussian python.mp4

23:08

07.09-transformation of random variables.mp4

12:44

07.10-homework.mp4

00:50

08.01-definition.mp4

05:07

08.02-sample mean.mp4

10:58

08.03-law of large numbers.mp4

11:51

08.04-law of large numbers famous distributions.mp4

12:55

08.05-law of large numbers famous distributions python.mp4

21:28

08.06-variance.mp4

10:54

08.07-homework.mp4

01:08

09.01-project bayes classifier from scratch.mp4

52:11

10.01-joint distributions.mp4

09:57

10.02-multivariate gaussian.mp4

06:46

10.03-conditioning independence.mp4

05:02

10.04-classification.mp4

04:49

10.05-naive bayes classification.mp4

03:36

10.06-regression.mp4

04:01

10.07-curse of dimensionality.mp4

05:44

10.08-homework.mp4

01:27

11.01-parametric distributions.mp4

05:23

11.02-maximum likelihood estimate (mle).mp4

05:18

11.03-log likelihood.mp4

07:45

11.04-logistic regression.mp4

04:20

11.05-ridge regression.mp4

09:45

11.06-deep neural network (dnn).mp4

05:47

12.01-permutations.mp4

08:46

12.02-combinations.mp4

13:20

12.03-binomial random variable.mp4

06:40

12.04-logistic regression formulation.mp4

07:25

12.05-logistic regression derivation.mp4

18:48

Description

In today’s ultra-competitive business universe, probability and statistics are the most important fields of study. That is because statistical research presents businesses with the data they need to make informed decisions in every business area, whether it is market research, product development, product launch timing, customer data analysis, sales forecast, or employee performance.

But why do you need to master probability and statistics in Python?

The answer is that an expert grip on the concepts of statistics and probability with data science will enable you to take your career to the next level. This course is designed carefully to reflect the most in-demand skills that will help you in understanding the concepts and methodology with regard to Python.

The course is as follows:

Easy to understand

Expressive

Comprehensive

Practical with live coding

About establishing links between probability and machine learning

By the end of this course, you will be able to relate the concepts and theories in machine learning with probabilistic reasoning and understand the methodology of statistics and probability with data science, using real datasets.

The code files and all related files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Mastering-Probability-and-Statistics-in-Python

More details

User Reviews

Rating

average 0

Total votes0

Focused display

Python

Statistics

AI Sciences

Instructor's Courses

AI Sciences is a group of experts, PhDs, and practitioners of AI, ML, computer science, and statistics. Some of the experts work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.They have produced a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science.Initially, their objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory. Today, they also publish more complete courses for a wider audience. Their courses have had phenomenal success and have helped more than 100,000 students master AI and data science.

PacktPub

View courses PacktPub

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.