Feature Engineering for Machine Learning| Feature Scaling using Normalization Technique

preview_player
Показать описание
In this video, we are going to understand
1. What is feature scaling?
2. Different feature scaling techniques, such as Normalization, Standardization, and more?
3. Understand their impact on the machine learning model
4. Implement them using sci-kit-learn and jupyter notebook
#machinelearning #artificialintelligence #ai #datascience #unsupervisedlearning #feature engineering #feature scaling

Code uploaded to GitHub repository link:

Don't forget to hit like and comment below on which topics we should create videos on? and also subscribe to our channel to become a master in Machine Learning.

Feature Engineering for Machine Learning| Feature Scaling using Standardization Technique

Telegram for more insights(ML papers, PDF and Books):

To Learn Machine learning click the Link below

Example of ML Classification Technique on iris Dataset using Support Vector Machine SVM

Types of Naive Bays Classifier !!! Applications, Pros, and Cons of Naive Bays

DEMO on Different Types of Naive Bayes Classifier, Gaussian, Multinomial and Bernoulli

Classification technique on Iris Dataset using Logistic Regression:

To understand what is Classification watch below video:

Roadmap to learn Machine Learning? | How to learn Machine Learning?

How to Apply Machine Learning?

Multiple Linear Regression for Housing Price Prediction:

First Algorithm in Machine Learning | Linear Regression | What is Linear Regression?

Building the first Machine Learning model using Jupyter Notebook and Scikit Learn| Linear Regression:-

Writing First ML algorithm:-

Types of Machine Learning:-

--------------------------------------------------------------------------------------------------------------------------------------------------------
To join our courses and more exciting free videos/books/classes visit our website:

Telegram for more insights(ML papers, PDF's and Books):

Рекомендации по теме
Комментарии
Автор

What are the other techniques to improve the quality of data in Machine Learning?

Code uploaded to GitHub repository link:





Telegram for more insights(ML papers, PDF and Books):

AsifImmanad