Description
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Lester Leong – CFI Education – Bayesian Thinking
Bayesian Thinking
Explore an alternative approach to probability with Bayesian Thinking for a deeper understanding of statistics to solve business problems.
Better leverage your data for business insights with three different approaches to probability
Predict the probability of future events and make better decisions by applying Bayes theorem
Communicate your results more effectively by recognizing the benefits of your models and evaluating the results
Overview
Bayesian Thinking Course Overview
Bayesian methods give us an alternative way to think about probability, with applications in business decision-making.
While traditional statistics requires us to observe a meaningful sample to inform decisions, Bayesian methods allow a “best guess†approach based on available information. These approaches also allow us to include other information such as beliefs and outside knowledge.
This course will take you on a step-by-step journey, from traditional statistical approaches, through conditional probability and Bayes Theorem. These concepts will form a foundation to help you understand two basic Machine-Learning examples introduced in the course. In the end, you’ll produce a real-world classification model using Python.
Bayesian Thinking Objectives
Upon completing this course, you will be able to:
Describe, compare, and contrast the three main approaches to probability
Understand the fundamentals of the Bayesian approach—such as conditional probability, priors, and updating beliefs
Apply Bayesian methods such as Bayes theorem and contingency tables to simple problems
Describe two Bayesian machine learning methods—multinomial and gaussian Bayes classifiers
Recognize the benefits of using these machine learning methods for modeling complex scenarios
Evaluate the results of the machine learning tests against business goals in Python
Who Should Take this Course?
This Bayesian Thinking course is perfect for professionals who work with data and want to apply an understanding of statistics to solving business problems. This course covers critical concepts for anyone working with statistics or data science and introduces both the concepts and practical applications. No background in coding with Python is required for this course. Common career paths for students who take the BIDAâ„¢ program are Business Intelligence, BI Developer, Data Analyst, Quantitative Analyst, and other finance careers.
What you’ll learn
Introduction
Describing Uncertainty with Probability
Course Outline
Learning Objectives
Course File Download
Chapter 1: Approaches to Probability
Approaches to Probability
Scenario 1
Scenario 1 Questions
Scenario 2
Scenario 2 Questions
Strengths and Limitations of the Classical Approach
Strengths and Limitations of the Frequentist Approach
Chapter 1 Exercises
Chapter 2: Bayesian Thinking
Another Example Introducing Bayesian Thinking
Bayes Theorem
Updating Bayes Theorem with New Data
Odds vs Probability
Forming a Posterior Belief Using Odds
A Summary of the Bayesian Approach
Strengths and Weaknesses of the Bayesian Approach
Chapter 3: Conditional Probability & Bayes Theorem
Chapter Introduction
Introduction to Conditional Probabilities
Conditional Probability Example
Working From Limited Data
CEO Contingency Table
Using the Bayes Factor to Update Your Belief
Chapter 4: Introduction to Bayesian Machine Learning Methods
Chapter Introduction
Scenario Introduction
Mutlinomial Naïve Bayes Classifier
Testing Our Classifier
Removing Zeros
Multinomial Naïve Bayes Classifier Recap
Multinomial Naïve Bayes Evaluation
Scenario Check-in
Gaussian Naïve Bayes Classifier
Testing Our Gaussian Naïve Bayes Classifier
Gaussian Naïve Bayes Model Evaluation
Chapter 5: Naïve Bayes ML Models in Python
Set-up Guide for Following Along
Introduction
Python Packages
Exploratory Data Analysis
Loading Data Into a Data Frame
Feature Engineering
Test-Train Split
Multinomial Naïve Bayes Classifier
Evaluation Metrics Theory
Model Evaluation
Gauss Naïve Bayes Classifier
Model Comparison
Qualified Assessment
Qualified Assessment
What our students say
Machine learning
Using machine learning.
Lydia Endjala
Keep learning
Please, keep learning new things like Bayesian Statistics
Atinafu Asefa
Amazing experience
The tutor really explained the concept
Stephen Akinosi
