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
In today’s ultracompetitive 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 indemand 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/MasteringProbabilityandStatisticsinPython
 Publication date:
 June 2021
 Publisher
 Packt
 Duration
 12 hours 30 minutes
 ISBN
 9781801075091