Feature Scaling in Machine Learning | Python | Standardization vs Normalization | One Hot encoding.
In the Feature Scaling in Machine Learning tutorial, we have discussed what is feature scaling, How we can do feature scaling and what are standardization and Normalization and how they scale or dataset. Then we have also learned what is One Hot encoding and label encoding and when to use them and how to use both encodings in scikit-learn.
Feature engineering is one of the most important concepts every machine learning engineer use during data preprocessing.
Topics which we have discussed in this tutorial :
1 - What is Feature scaling?
2 - Why we need Feature scaling?
3 - Types of Feature Scaling in python for machine learning.
4 - Standardization vs Normalization and when to use them.
5 - One Hot encoding Vs LabelEncoding.
6 - what is a column transformer, Label Encoder, One Hot encoder in sklearn/Scikit-Learn.
Link for Data Preprocessing Course Resources :
https://drive.google.com/drive/folder...
About the Data Preprocessing Course :
We will cover all the topics which are necessary to perform on dirty data to make it clean and ready to use for the Machine learning model. The whole course is divided into six chapters and each chapter is explained through Pandas and Scikit-learn both So that your fundaments become stronger and it will definitely help you clean any kind of dirty real-world dataset. For learning purposes, we have also used a raw real-world Car dataset and we will apply our data preprocessing steps to real-world data so you get to know how to deal with the real-world dataset.
In this course,, we will start with Importing Dataset and How to get basic insights from it. then we will learn how to deal with missing values in a dataset followed by Data Formatting and Data Binning, How to Deal with categorical values, Splitting of Dataset and Feature Scaling, and Data Normalization.
About Bit ML :
Bit Ml is a youtube channel on which we upload tutorials regarding Machine Learning in python in the Hindi language so that students get detailed explanations about every topic of machine learning. Our mission is to explain complex topics of machine learning in such an easy manner that students can understand the concepts easily.
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Bit ML.
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