Mastering Probability and Statistics in Python

Video description

A comprehensive course that teaches you the concepts and methodologies of statistics and probability with data science.

About This Video

  • Easy explanations, yet complete and comprehensive course
  • Fundamental, pythonic, and a complete course to master the important concepts used in data science
  • Practical with live coding of the implementation of the concepts learned theoretically

In Detail

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.

Who this book is for

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.

Publisher resources

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Table of contents

  1. Chapter 1 : Introduction to the Course
    1. Introduction to the Instructor
    2. Focus of the Course
  2. Chapter 2 : Probability and Statistics
    1. Probability Versus Statistics
  3. Chapter 3 : Sets
    1. Definition of Set
    2. Cardinality of a Set
    3. Subsets, Power Set, and Universal Set
    4. Python Practice Subsets
    5. Power Sets Solution
    6. Operations
    7. Python Practice Operations
    8. Venn Diagrams Operations
    9. Homework
  4. Chapter 4 : Experiment
    1. Random Experiment
    2. Outcome and Sample Space
    3. Event
    4. Recap and Homework
  5. Chapter 5 : Probability Model
    1. Probability model
    2. Probability Axioms
    3. Probability Axioms Derivations
    4. Probability Models Example
    5. More Examples of Probability Models
    6. Probability Models Continuous
    7. Conditional Probability
    8. Conditional Probability Example
    9. Conditional Probability Formula
    10. Conditional Probability in Machine Learning
    11. Conditional Probability Total Probability Theorem
    12. Probability Models Independence
    13. Probability Models Conditional Independence
    14. Probability Models Bayes' Rule
    15. Probability Models towards Random Variables
    16. Homework
  6. Chapter 6 : Random Variables
    1. Introduction
    2. Random Variables Examples
    3. Bernoulli Random Variables
    4. Bernoulli Trail Python Practice
    5. Geometric Random Variable
    6. Geometric Random Variable Normalization Proof Optional
    7. Geometric Random Variable Python Practice
    8. Binomial Random Variables
    9. Binomial Python Practice
    10. Random Variables in Real Datasets
    11. Homework
  7. Chapter 7 : Continuous Random Variables
    1. Zero Probability to Individual Values
    2. Probability Density Functions
    3. Uniform Distribution
    4. Uniform Distribution Python
    5. Exponential
    6. Exponential Python
    7. Gaussian Random Variables
    8. Gaussian Python
    9. Transformation of Random Variables
    10. Homework
  8. Chapter 8 : Expectations
    1. Definition
    2. Sample Mean
    3. Law of Large Numbers
    4. Law of Large Numbers Famous Distributions
    5. Law of Large Numbers Famous Distributions Python
    6. Variance
    7. Homework
  9. Chapter 9 : Project Bayes' Classifier
    1. Project Bayes' Classifier from Scratch
  10. Chapter 10 : Multiple Random Variables
    1. Joint Distributions
    2. Multivariate Gaussian
    3. Conditioning Independence
    4. Classification
    5. Naive Bayes' Classification
    6. Regression
    7. Curse of Dimensionality
    8. Homework
  11. Chapter 11 : Optional Estimation
    1. Parametric Distributions
    2. Maximum Likelihood Estimate (MLE)
    3. Log Likelihood
    4. Maximum A Posterior Estimate (MAP)
    5. Logistic Regression
    6. Ridge Regression
    7. Deep Neural Network (DNN)
  12. Chapter 12 : Mathematical Derivations for Math Lovers
    1. Permutations
    2. Combinations
    3. Binomial Random Variable
    4. Logistic Regression Formulation
    5. Logistic Regression Derivation

Product information

  • Title: Mastering Probability and Statistics in Python
  • Author(s): AI Sciences
  • Release date: June 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781801075091