Standardization in Machine Learning | Feature Scaling | Data Preprocessing

Описание: Welcome to ML Journey: Day by Day
Where we master one machine learning concept every single day!

📂 In This Video:
We’ll dive into Standardization, one of the most powerful feature scaling techniques in Machine Learning.

Many beginners skip this step and end up with poor model performance or unstable predictions. But Standardization transforms your data so every feature has a mean of 0 and standard deviation of 1, giving ML algorithms like Linear Regression, Logistic Regression, SVM, and K-Means a fair and balanced dataset to learn from.

By the end of this video, you’ll know exactly when and how to use Standardization to make your models more accurate, reliable, and efficient.

What You’ll Learn in This Video:

Why feature scaling is crucial for ML models

What Standardization is and how it works

Difference between Normalization vs Standardization

How to apply StandardScaler in Python (step-by-step)

Visualizing standardized data

When to use Standardization (and when not to)

Impact on model performance

⏳ Timestamps:
00:00 - Introduction
00:23 - What is Standardization?
01:10 - Formula Explained
01:20 - Example
02:02 - Why Standardize
03:16 - When to Use Standardization
03:54 - Applying StandardScaler in Python
08:15 - Difference in Standardization & Normalization
09:59 - Wrap-Up

💡 This skill is essential for data preprocessing and will help you build faster, more accurate models in both regression and classification tasks.

📂 This video is part of my playlist:
👉 ML Journey: Day by Day

💻 Source Code on GitHub:
🔗 https://github.com/WasayRabbani/Machi...

✅ Don’t forget to:
🔔 Subscribe for daily ML insights
👍 Like if this helped
💬 Comment your questions — I reply to everyone!

#Standardization #FeatureScaling #MachineLearning #DataPreprocessing #PythonML #MLTutorial #DataScience #LearnMachineLearning #PandasTutorial #ScikitLearn #AI #MLJourney #DataScienceProjects #ZScoreScaling