Mastering Probability and Statistics in Python [Video]
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-
Free ChapterIntroduction to the Course
-
Probability and Statistics
-
Sets
-
Experiment
-
Probability Model
- Probability model
- Probability Axioms
- Probability Axioms Derivations
- Probability Models Example
- More Examples of Probability Models
- Probability Models Continuous
- Conditional Probability
- Conditional Probability Example
- Conditional Probability Formula
- Conditional Probability in Machine Learning
- Conditional Probability Total Probability Theorem
- Probability Models Independence
- Probability Models Conditional Independence
- Probability Models Bayes' Rule
- Probability Models towards Random Variables
- Homework
-
Random Variables
- Introduction
- Random Variables Examples
- Bernoulli Random Variables
- Bernoulli Trail Python Practice
- Geometric Random Variable
- Geometric Random Variable Normalization Proof Optional
- Geometric Random Variable Python Practice
- Binomial Random Variables
- Binomial Python Practice
- Random Variables in Real Datasets
- Homework
-
Continuous Random Variables
-
Expectations
-
Project Bayes' Classifier
-
Multiple Random Variables
-
Optional Estimation
-
Mathematical Derivations for Math Lovers
About this
video
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
- Publication date:
- June 2021
- Publisher
- Packt
- Duration
- 12 hours 30 minutes
- ISBN
- 9781801075091