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Data Preprocessing for Machine Learning using MATLAB
Introduction to course and MATLAB
1 - Intorduction to Course (4:24)
2 - Introduction to matlab (8:26)
3 - Importing the dataset into MATLAB (7:34)
Handling Missing Values
Code and Data
1 - Deletion Strategies (8:41)
2 - Using mean and mode (10:42)
3 - Adding_Special_Value (6:58)
4 - class_specific_mode_mean (12:48)
5 - Random_Value_Imputation (14:05)
Dealing with Categorical Variables
Code and Data
2 - Categorical data with order (6:11)
1 - Categorical data with no order (9:51)
3 - Frequency_encoding (13:04)
4 - Target_based_Encoding (9:20)
Outlier Detection
Code and Data
1 - 3 sigma rule with deletion strategy (11:27)
2 - 3 sigma rule with filling strategy (5:55)
3 - Histograms for outliers (12:56)
4 - Box Plots (Part 1) (8:18)
5 - Box Plots (Part 2 (15:41)
6 - LOF (Part 1) (6:21)
7 - LOF (Part 2) (12:49)
8 - Outliers in categorical variables (8:03)
Feature Scaling and Data Discretization
Code and Data
1 - Feature Scalling (8:50)
2 - Equal Width Binning (15:48)
3 - Equal Frequency Binning (7:35)
Project: Selecting the Right Method for your Data
Code and Data
Selecting the right method (Part 1) (16:53)
Selecting the right method (Part 2) (10:59)
5 - Random_Value_Imputation
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