Data Preprocessing For Machine Learning Using MATLAB

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Online

£ 50 + VAT

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Description

  • Type

    Course

  • Methodology

    Online

  • Start date

    Different dates available

Basic Course Description This course is for you if you want to fully equip yourself with the art of applied machine learning using MATLAB. This course is also for you if you want to apply the most commonly used data preprocessing techniques without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning implementation but could never figure out how to further improve the peformance of the machine learning algorithms. By the end of this course, you will have at your fingertips, a vast variety of most commonly used data preprocessing techniques that you can use instantly to maximize your insight into your data set. The approach in this course is very practical and we will start everything from very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. All the coding will be done in MATLAB which is one of the fundamental programming languages for engineer and science students and is frequently used by top data science research groups world wide. Below is the brief outline of this course. Segment 1: Introduction to course and MATLAB
Segment 2: Handling Missing Values 
Segment 3: Dealing with Categorical Variables
Segment 4: Outlier Detection
Segment 5: Feature Scaling and Data Discretization
Segment 6: Project: Selecting Techniques for your DatasetYour Benefits and Advantages: You will be sure of receiving quality contents from the instructors.
You have lifetime access to the course.
You have instant and free access to any updates i add to the course.
You have access to all Questions and discussions initiated by other students.
You will receive my support regarding any issues related to the course.
Check out the curriculum and Freely available lectures for a quick insight.It's time to take Action!Click the "Take This Course" button at the top right now!
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Facilities

Location

Start date

Online

Start date

Different dates availableEnrolment now open

About this course

How to effectively proprocess data before analysis
How to implement different preprocessing methods using matlab
Take away code templates for quickly preprocessing your data
Decide which method choose for your dataset

Students, Entrepreneurs, Researchers, Instructors, Engineers, Programmers, Simulators
Anyone who want to analyze the data

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This centre's achievements

2021

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More than 50 reviews in the last 12 months

This centre has featured on Emagister for 4 years

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Course programme

Introduction to course and MATLAB 3 lectures 20:26 Introduction to course Introduction to MATLAB Importing Dataset into MATLAB Introduction to course and MATLAB 3 lectures 20:26 Introduction to course Introduction to MATLAB Importing Dataset into MATLAB Introduction to course Introduction to course Introduction to course Introduction to course Introduction to MATLAB Introduction to MATLAB Introduction to MATLAB Introduction to MATLAB Importing Dataset into MATLAB Importing Dataset into MATLAB Importing Dataset into MATLAB Importing Dataset into MATLAB Handling Missing Values 6 lectures 53:16 Code and Data Deletion strategies Using mean and mode Considering as a special value Class specific mean and mode Random Value Imputation Handling Missing Values 6 lectures 53:16 Code and Data Deletion strategies Using mean and mode Considering as a special value Class specific mean and mode Random Value Imputation Code and Data Code and Data Code and Data Code and Data Deletion strategies Deletion strategies Deletion strategies Deletion strategies Using mean and mode Using mean and mode Using mean and mode Using mean and mode Considering as a special value Considering as a special value Considering as a special value Considering as a special value Class specific mean and mode Class specific mean and mode Class specific mean and mode Class specific mean and mode Random Value Imputation Random Value Imputation Random Value Imputation Random Value Imputation Dealing with Categorical Variables 5 lectures 38:27 Code and Data Categorical Variables Categorical data with no order Categorical data with order Frequency based encoding Target based encoding Dealing with Categorical Variables 5 lectures 38:27 Code and Data Categorical Variables Categorical data with no order Categorical data with order Frequency based encoding Target based encoding Code and Data Categorical Variables Code and Data Categorical Variables Code and Data Categorical Variables Code and Data Categorical Variables Categorical data with no order Categorical data with no order Categorical data with no order Categorical data with no order Categorical data with order Categorical data with order Categorical data with order Categorical data with order Frequency based encoding Frequency based encoding Frequency based encoding Frequency based encoding Target based encoding Target based encoding Target based encoding Target based encoding Outlier Detection 9 lectures 01:21:33 Code and Data Outlier Detection 3 sigma rule with deletion strategy 3 sigma rule with filling strategy Box plots and iterquartile rule Class specific box plots Histograms for outliers Local Outlier Factor (Part 1) Local Outlier Factor (Part 2) Outliers in Categorical Variables Outlier Detection 9 lectures 01:21:33 Code and Data Outlier Detection 3 sigma rule with deletion strategy 3 sigma rule with filling strategy Box plots and iterquartile rule Class specific box plots Histograms for outliers Local Outlier Factor (Part 1) Local Outlier Factor (Part 2) Outliers in Categorical Variables Code and Data Outlier Detection Code and Data Outlier Detection Code and Data Outlier Detection Code and Data Outlier Detection 3 sigma rule with deletion strategy 3 sigma rule with deletion strategy 3 sigma rule with deletion strategy 3 sigma rule with deletion strategy 3 sigma rule with filling strategy 3 sigma rule with filling strategy 3 sigma rule with filling strategy 3 sigma rule with filling strategy Box plots and iterquartile rule Box plots and iterquartile rule Box plots and iterquartile rule Box plots and iterquartile rule Class specific box plots Class specific box plots Class specific box plots Class specific box plots Histograms for outliers Histograms for outliers Histograms for outliers Histograms for outliers Local Outlier Factor (Part 1) Local Outlier Factor (Part 1) Local Outlier Factor (Part 1) Local Outlier Factor (Part 1) Local Outlier Factor (Part 2) Local Outlier Factor (Part 2) Local Outlier Factor (Part 2) Local Outlier Factor (Part 2) Outliers in Categorical Variables Outliers in Categorical Variables Outliers in Categorical Variables Outliers in Categorical Variables Feature Scaling and Data Discretization 4 lectures 32:14 Code and Data Feature Scaling Feature Scaling Discretization using Equal width binning Discretization using Equal Frequency binning Feature Scaling and Data Discretization

Additional information

MATLAB 2017a or heigher version. No prior knowledge of MATLAB is required In version below 2017a there might be some functions that will not work We cover everything from scratch and therefore do not require any prior knowledge of MATLAB

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Data Preprocessing For Machine Learning Using MATLAB

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