Video description
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.
What You Will Learn
 The importance of statistics and probability in data science
 The foundations for machine learning and its roots in probability theory
 The concepts of absolute beginning indepth with examples in Python
 Practical explanation and live coding with Python
 Probabilistic view of modern machine learning
 Implementation of Bayes’ classifier on a real dataset
Audience
This course is for individuals who want to learn statistics and probability along with its implementation in realistic projects. Data scientists and business analysts and those who want to upgrade their data analysis skills will also get the benefit. People who want to learn statistics and probability with real datasets in data science and are passionate about numbers and programming will get the most out of this course.
No prior knowledge is needed. You start from the basics and gradually build your knowledge of the subject. A basic understanding of Python will be a plus but not mandatory.
About The Author
AI Sciences: AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.
AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.
Their courses have successfully helped more than 100,000 students master AI and data science.
Table of contents
 Chapter 1 : Introduction to the Course
 Chapter 2 : Probability and Statistics
 Chapter 3 : Sets
 Chapter 4 : Experiment

Chapter 5 : 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

Chapter 6 : 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
 Chapter 7 : Continuous Random Variables
 Chapter 8 : Expectations
 Chapter 9 : Project Bayes' Classifier
 Chapter 10 : Multiple Random Variables
 Chapter 11 : Optional Estimation
 Chapter 12 : Mathematical Derivations for Math Lovers
Product information
 Title: Mastering Probability and Statistics in Python
 Author(s):
 Release date: June 2021
 Publisher(s): Packt Publishing
 ISBN: 9781801075091
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