A comprehensive course that teaches you the concepts and methodologies of statistics and probability with data science.
About This Video
In Detail
In today's ultra-competitive business universe, …
A comprehensive course that teaches you the concepts and methodologies of statistics and probability with data science.
About This Video
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:
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.
Chapter 1 : Introduction to the Course
Introduction to the Instructor
Focus of the Course
Chapter 2 : Probability and Statistics
Probability Versus Statistics
Chapter 3 : Sets
Definition of Set
Cardinality of a Set
Subsets, Power Set, and Universal Set
Python Practice Subsets
Power Sets Solution
Operations
Python Practice Operations
Venn Diagrams Operations
Homework
Chapter 4 : Experiment
Random Experiment
Outcome and Sample Space
Event
Recap and Homework
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
Zero Probability to Individual Values
Probability Density Functions
Uniform Distribution
Uniform Distribution Python
Exponential
Exponential Python
Gaussian Random Variables
Gaussian Python
Transformation of Random Variables
Homework
Chapter 8 : Expectations
Definition
Sample Mean
Law of Large Numbers
Law of Large Numbers Famous Distributions
Law of Large Numbers Famous Distributions Python
Variance
Homework
Chapter 9 : Project Bayes’ Classifier
Project Bayes’ Classifier from Scratch
Chapter 10 : Multiple Random Variables
Joint Distributions
Multivariate Gaussian
Conditioning Independence
Classification
Naive Bayes’ Classification
Regression
Curse of Dimensionality
Homework
Chapter 11 : Optional Estimation
Parametric Distributions
Maximum Likelihood Estimate (MLE)
Log Likelihood
Maximum A Posterior Estimate (MAP)
Logistic Regression
Ridge Regression
Deep Neural Network (DNN)
Chapter 12 : Mathematical Derivations for Math Lovers
Permutations
Combinations
Binomial Random Variable
Logistic Regression Formulation
Logistic Regression Derivation