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Course Content

Introduction to Machine Learning

Environment Setup part 1

Environment Setup part 2

Environment Setup part 3

Quiz : Introduction to Machine Learning

Data Wrangling

Importing Libraries and Dataset

Handling Missing Data

Handling Missing Data - Practical

Encoding Categorical Data

Encoding Catergorical Data - Practical

Splitting Dataset

Splitting Dataset - Practical

Normalizing the Data - Part 1

Normalizing the Data - Part2

Finding Machine Learning Datasets

Exploratory Data Analysis

Plotting Graphs - Part 1

Plotting Graphs - Part 2

Distribution Models - Part 1

Distribution Models - Part 2

Assignment of Data Preprocessing for Machine Learning

Quiz : Data Preprocessing for Machine Learning

Machine Learning Paradigms

Sampling Methods

Underfitting and Overfitting in Models

Variance and Bias

Assignment of Machine Learning Paradigms

Quiz : Machine Learning Paradigms

Overview of Classification

Logistic Regression

Logistic Regression - Practical

KNN

KNN - Practical

Decision Trees for Classification

Decision Trees Practical - 1

Decision Trees Practical - 2

Random Forest

Support Vector Machine Classifier

Support Vector Machine Classifier - Practical 1

Support Vector Machine Classifier - Practical 2

Naive Bayes Classifier

Naive Bayes Classifier - Practical 1

Naive Bayes Classifier - Practical 2

Evaluating Classification Models Performance

Evaluating Classification Models Performance - Practical

Assignment of Supervised Learning Algorithms

Quiz : Supervised Learning Algorithms

Simple Linear Regression

Simple Linear Regression - Practical

Multi-Linear Regression

Support Vector Regressor

Support Vector Regressor - Practical

Decision Tree Regressor

Decision Tree Regressor - Practical

Regressor Model Selection

Evaluating Regression Model Performance

Evaluating Regression Model Performance - Practical

Assignment of Regression Algoritms

Quiz : Regression Algorithms

Advance Trends in Machine Learning

Course Summary

Interview Questions Part 1

Interview Questions Part 2

Interview Questions Part 3

Career Guidelines

Naive Bayes Classifier - Practical 1

Supervised Learning in Machine Learning Supervised Learning Algorithms

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Discussion

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4_6_Naive_Bayes_Classifier

Course Content

Introduction to Machine Learning

Environment Setup part 1

Environment Setup part 2

Environment Setup part 3

Data Wrangling

Importing Libraries and Dataset

Handling Missing Data

Handling Missing Data - Practical

Encoding Categorical Data

Encoding Catergorical Data - Practical

Splitting Dataset

Splitting Dataset - Practical

Normalizing the Data - Part 1

Normalizing the Data - Part2

Finding Machine Learning Datasets

Exploratory Data Analysis

Plotting Graphs - Part 1

Plotting Graphs - Part 2

Distribution Models - Part 1

Distribution Models - Part 2

Assignment of Data Preprocessing for Machine Learning

Machine Learning Paradigms

Sampling Methods

Underfitting and Overfitting in Models

Variance and Bias

Assignment of Machine Learning Paradigms

Overview of Classification

Logistic Regression

Logistic Regression - Practical

KNN

KNN - Practical

Decision Trees for Classification

Decision Trees Practical - 1

Decision Trees Practical - 2

Random Forest

Support Vector Machine Classifier

Support Vector Machine Classifier - Practical 1

Support Vector Machine Classifier - Practical 2

Naive Bayes Classifier

Naive Bayes Classifier - Practical 1

Naive Bayes Classifier - Practical 2

Evaluating Classification Models Performance

Evaluating Classification Models Performance - Practical

Assignment of Supervised Learning Algorithms

Simple Linear Regression

Simple Linear Regression - Practical

Multi-Linear Regression

Support Vector Regressor

Support Vector Regressor - Practical

Decision Tree Regressor

Decision Tree Regressor - Practical

Regressor Model Selection

Evaluating Regression Model Performance

Evaluating Regression Model Performance - Practical

Assignment of Regression Algoritms

Advance Trends in Machine Learning

Course Summary

Interview Questions Part 1

Interview Questions Part 2

Interview Questions Part 3

Career Guidelines

FAQs

Naive Bayes is a type of classifier that employs the Bayes Theorem. It calculates membership probabilities for each class, such as the likelihood that a given record or data point belongs to a specific class. The most likely class is the one with the greatest probability.

Naive Because it presupposes class conditional independence, Bayes classification is referred to as naive. The effect of an attribute value on a specific class is independent of the other attributes' values. This assumption is made to reduce computational costs and hence is considered Naive.

Predicting the class of the test data set is simple and quick. It also excels at multi-class prediction. When the assumption of independence is met, a Naive Bayes classifier outperforms alternative models such as logistic regression, and less training data is required.

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