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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

Handling Missing Data - Practical

Supervised Learning in Machine Learning Data Preprocessing for Machine Learning

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Discussion

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2_3_Handling_Missing_Data

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

Subtractions. Deletion in pairs. Row Dropping/Listwise Deletion Complete columns are being removed.
Fundamental Imputation Techniques Imputation with a fixed value. Imputation
making use of statistics (mean, median, mode) Imputation of the K-Nearest Neighbor.

Substituting an arbitrary value.
Mode Replacement.
Substituting Median
Forward filling with the previous value
Backward filling with the following value
Interpolation.
Determine the Most Frequent Value.

The first method is to delete rows or columns. When it comes to empty cells, we normally employ this strategy.
The second method is to replace missing data with aggregated values.
The Three Method involves the creation of an unknown category.
The Four Method is used to forecast missing values.

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