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Applied Bayes' Theorem And Naive Bayes Classifiers - OneDDL - 11-14-2024
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Free Download Applied Bayes' Theorem And Naive Bayes Classifiers
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.49 GB | Duration: 14h 7m
Learn the fundamentals to better develop or acquire such Machine Learning Methods
What you'll learn
Detailed and fundamental probability principles, rules and procedures
The Confused Matrix and its KPI's to be used as an evaluation of Naïve Bayes Classifier
The Variant of Confused Matrices when datasets have multiple labels in their class or multiple classes
The theory and principles behind the Bayes' Theorem
How to develop the inference procedures in Bayes' Theorem using vertical and horizontal tables, contingency tables and decision trees
The theoretical basis of Naïve Bayes Classifier
Categorical Naïve Bayes Classifiers
Laplace Smoothing Correction and M-Estimates
Continuous Naïve Bayes Classifiers based on Gaussian distributions
Continuous Naïve Bayes Classifiers based on non-Gaussian distributions (in this case, the Beta Distribution)
The Beta Distribution and how to derive its parameters from our data
The four discrete distributions in use in the Bayes' Theorem: Categorical, Bernoulli, Binomial and Multinomial
Bernoulli Naïve Bayes Classifiers
Multinomial Naïve Bayes Classifiers
Weighted Naïve Bayes Classifiers
Complemented Naïve Bayes Classifiers
Entropy and Information Gain for better classification
The Kononenko Information Gain and its application in Naïve Bayes Classifiers
The Log Odds Ratio and its application in Naïve Bayes Classifiers
The Kernel Density Estimates and its application in Naïve Bayes Classifier
The optimization of the bandwidth, h, in the Kernel Density Estimates
Requirements
A working knowledge of Excel
A beginner's knowledge of VBA (in Excel)
No Python or R are needed
All statistical methods will be presented in the course
Description
A) The Purpose of the CourseMost courses on this subject are aimed at Machine Learning and Data Science experts. Often, they are presented for use with specialized development platforms or even as part of advanced off-the-shelf applications. On the other hand, the Bayes' Theorem and its applications are based on statistical principles and concept not often clearly explained.The purpose of this course is educational. The techniques, algorithms and procedures presented in this course aim more at making machine learning methods based on the Theorem easier to understand as opposed to getting used.The Bayes' Theorem is is one of those theorems where we can apply the proverb: "Still water is deep". The Theorem was developed in an article by Thomas Bayes in 1763. In due course, it found itself being used in a wide variety of statistical applications. The Theorem itself was an application of inference. From there on, and specifically with the advent of Machine Learning algorithms, the Theorem was extended to be the core of a wide variety of applications such as Classification, Networks and Optimization.The Theorem and its applications are best developed using specialized programming environments. This is due to the mere fact that the applications of the Theorem require the handling of large data and performance intensive environments.B) So, why do we Present a Course based on Excel?Analysts require the use and the development of such applications have the following environments available to them:· Off the shelf applications, ready-made and commercially available.· Open source or free integrated development environments (IDE) that host a large number of scientific and statistical libraries to use in such applicationsIn both cases, the Analyst is faced with an insurmountable learning curve, often not climbable at all. Whether the objective is to use off-the-shelf products or to develop their own applications, learning the methods in a machine learning environment is not possible via these two environments.The course will then use Excel specifically for educational purposes and not as a machine learning tool. Excel is known by everyone, and if not, it is easy to learn. Excel is highly flexible in terms of exposing how things work. The course will then exploit such facilities to expose to the Analyst in a common sense and step-by-step manner the basis and procedures of these algorithms.B) What Does the Course Cover?The course is made up of 5 major sections preceded by a short introduction.Section 1: Introducing the CourseThis section consists of one lecture that presents the objectives of the course, its structure and resources as well as what to expect and what not to expect.Section 2: An In-Depth Presentation of Probability Rules and PracticesThe section starts with lectures that run through a detailed exposure to the fundamentals and practices of probability rules. Bayes' Theorem is highly linked with such rules and it will not be possible for analysts embarking on its use (and the understanding of its extensions) to learn and use these algorithms without a deep understanding of probability.The section uses common sense to clarify often obscure concepts in probability. Many examples are presented and explained in detail.Section 3: The Use of the Confusion Matrix for Evaluating Bayesian ResultsSome might wonder why we are introducing the Confusion Matrix and its useful KPI's in this course. The answer is that in both Sections 3 and 4, we will need to evaluate our results in terms of precision, accuracy, error rates, etc. The Confusion Matrix is a contingency table consisting of four results extracted from comparing the algorithm's outcome with the historically known outcome of the classes in a Test Table. Four measurements consist of True Positive, True Negative, False Positive and False Negative. These four counts can be used in a variety of ways to measure such KPI's as accuracy, precision, error rates and such. (The confusion matrix is also used in a variety of other classification machine learning methods: logistic regression, decision trees, etc.)Section 4: The Fundamental Application of Bayes' Theoremthis section presents the Theorem of Bayes first running through a common-sense example. This is followed by the derivation of the Theorem and a clear explanation of the terms used in the Bayes' Theorem formula. A set of 8 major workouts present the use of the Theorem in different formats (vertical and horizontal tables, decision trees and graphic solutions). The last 3 workouts output the results of the workouts to a Confusion Matrix and shows how that can be used to evaluate the results of the Theorem.Section 5: How to Use the Naïve Bayes Classifiersthis is the heart of the course. It presents a wide variety of algorithms whose purpose is the supervised classification of data. The Naïve Bayes Classifiers are a family of algorithms based on the Bayes' Theorem. They differ in various ways from each other. They are listed below.Amongst the lectures detailing these algorithms with clear examples are "support" lectures that present topics that are needed as a support to these algorithms.After starting with two lectures that present the fundamentals of Naïve Bayes Classifiers and the required theory, the course proceeds with a set of lectures consisting of 8 Naïve Bayes Classifier variants:1) Categorical Naïve Bayes Classifiers2) Gaussian and Continuous Naïve Bayes Classifiers3) Non-Gaussian Continuous Naïve Bayes Classifiers4) Bernoulli Naïve Bayes Classifier5) Multinomial Naïve Bayes Classifier6) Weighted Naive Bayes Classification7) Complement Naïve Bayes Classification8) Kernel Distance Estimation and Naive Bayes ClassificationTo support the presentations above, the course will interleave the following detailed presentations consisting of methods, topics and procedures:1) Laplace Smoothing Correction2) Extensions to Continuous Features: checking for normality, checking for independence of features, smoothing corrections for Gaussian features3) Two Discrete Distributions - Bernoulli and Categorical4) Two Discrete Distributions - Binomial and Multinomial5) Entropy and Information and how used in Naïve Bayes Classification6) Kononenko Information Gain and Evaluation of Classifiers7) Log Odds Ratio and Nomograms used in Bayes Classification8) Kernel Distance Estimation - Estimating the Bandwidth hResourcesAll lectures will be supported by a variety of resources:· Solved and documented workouts in Excel· Dedicated workbooks that animate and describe various probability distributions· Links to Interesting articles and books
Overview
Section 1: Introduction
Lecture 1 The Purpose and Structure of this Course
Section 2: An In-Depth Presentation of Probability Rules and Practices
Lecture 2 Fundamental Definitions of Probability
Lecture 3 Probability - Distribution Tables (Contingency)
Lecture 4 Probability Rule 1) Intersection Rule for Independent Events (JOIN)
Lecture 5 Probability Rule 2) The Union Rule (OR or UNION)
Lecture 6 Probability Rule 3) The Union Rule (XOR or Exclusive UNION)
Lecture 7 Probability Rule 4) The Intersection Rule (AND or CONDITIONAL) for Dependent Eve
Lecture 8 Probability Rule 4) The Intersection Rule for Dependent Events (Examples)
Lecture 9 Probability - Decision Trees and Probability
Lecture 10 Probability Rule 5) The Meaning of Independence and Mutual Exclusivity
Lecture 11 Probability Rule 6) Total Probability
Lecture 12 Probability Rule 7) The Chain Rule of Probability
Section 3: The Use of the Confusion Matrix for Evaluating Bayesian Results
Lecture 13 Evaluating Classifiers with the Confusion Matrix and its KPIs
Lecture 14 Extracting KPI's from the Confusion Matrix
Lecture 15 Evaluating Classifiers in the Case of Multiple Classes or Multiple Labels
Section 4: The Fundamental Application of Bayes' Theorem
Lecture 16 Bayes Theorem - Rationale and Derivation of Theorem
Lecture 17 Bayes Theorem - A Bayesian Story, an Example without Formulas
Lecture 18 Bayes Theorem - Defining the Factors in Bayes' Theorem
Lecture 19 Bayes' Theorem - (W1) The Famous HIV Test
Lecture 20 Bayes' Theorem - (W2-W4) Spam Testing 3 Events, Defective Machines and HIV (Hori
Lecture 21 Bayes' Theorem - (W5) Spam Testing (Contingency Table and Graphic Solutions)
Lecture 22 Bayes' Theorem - (W5) Spam Testing (Continued - Deriving Posteriors with the Con
Lecture 23 Bayes' Theorem - (W6-W8) Predicting Rain and Identifying Product Suppliers + Mon
Section 5: Naive Bayes Classifiers
Lecture 24 Naive Bayes Classifiers - Introduction
Lecture 25 Naive Bayes Classifiers - Introducing the Algorithm
Lecture 26 Naive Bayes Classifiers - The Algorithm thru a Short Example
Lecture 27 Naive Bayes Classifiers - The Naive Bayes Classifier Procedure Applied on a Cate
Lecture 28 Naive Bayes Classifiers - More Categorical Examples
Lecture 29 Naive Bayes Classifiers - Laplace Smoothing Correction
Lecture 30 Naive Bayes Classifiers - Correction using M-Estimates
Lecture 31 Naive Bayes Classifiers - Gaussian and Continuous
Lecture 32 Naive Bayes Classifiers - Three Gaussian Examples
Lecture 33 Naive Bayes Classifiers - Some Extensions to Continuous Features
Lecture 34 Naive Bayes Classifiers - Handling Non-Gaussian Continuous Features (Beta Distri
Lecture 35 Naive Bayes Classifiers - Applying the Non-Gaussian (Beta) Procedure to Personal
Lecture 36 Naive Bayes Classifiers - Discrete Distributions - Bernoulli and Categorical
Lecture 37 Naive Bayes Classifiers - Discrete Distribution - Binomial
Lecture 38 Naive Bayes Classifiers - Discrete Distribution - Multinomial
Lecture 39 Naive Bayes Classifiers - Bernoulli Naive Bayes Examples
Lecture 40 Naive Bayes Classifiers - Multinomial Naive Bayes Examples
Lecture 41 Naive Bayes Classifiers - Weighted Naive Bayes
Lecture 42 Naive Bayes Classifiers - Complement Classifier
Lecture 43 Naive Bayes Classifiers - Entropy and Information
Lecture 44 Naive Bayes Classifiers - Kononenko Information Gain and Evaluation Feature Infl
Lecture 45 Naive Bayes Classifiers - Log Odds Ratio and Nomograms
Lecture 46 Naive Bayes Classifiers - Kernel Distance Estimation (Discrete and Continuous Di
Lecture 47 Naive Bayes Classifiers - Kernel Distance Estimation and Application of Naive Ba
Lecture 48 Naive Bayes Classifiers - Kernel Distance Estimation - Estimating the Bandwidth
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