This course includes:
- Video lessons
- Quiz assessment
- Survey lessons
- Certificate of completion
Data Pre-Processing
Created by Youtube External
Last updated: April 2025 | Language:
About this course
This course covers techniques for preparing raw data for analysis. It focuses on essential tasks like data cleaning, handling missing values, data transformation, normalization, and encoding categorical variables. Students will learn how to ensure data quality and structure, making it ready for analysis or machine learning models. This course is key for improving the accuracy and efficiency of data-driven projects.
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Course content
Author: @WsCubeTechENGLISH
Description: In this video, I am trying to explain Data Preprocessing in Machine Learning | Complete Steps
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Author: @AlexTheAnalyst
Description: In this lesson we are going to be building a data cleaning project in MySQL!
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Author: @krishnaik06
Description: In this tutorial, you will discover how to handle missing data for machine learning with Python.
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Author: @DataMagicAI
Description: Today I will Talk about What is outlier ? and How we can detect outliers from our data ?. Then will talk about how we can remove our outlier ? and whats the benefit of it ?.
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Author: @Computerphile
Description: Learn Data transformation
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Author: @krishnaik06
Description:
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This document provides additional resources to deepen your understanding of the topics covered in this program. Below, you’ll find links to articles related to each lesson to support your further reading.
Welcome to the quiz section. Before you begin, please make sure you have watched the lesson videos thoroughly, as the questions are designed to test your understanding of key concepts discussed.
Here are a few things to keep in mind:
1. This is a timed quiz, so you’ll need to manage your time effectively.
2. Read each question carefully and choose the best answer based on the material covered in the lesson.
3. The quiz is an excellent opportunity to test your knowledge and reinforce what you’ve learned, so try your best!