Data Mining in Arabic Data Lecture 4 - Data preprocessing 2 - تنقيب البيانات بالعربي

Feature Engineering is the art of extracting more value from your data! 🚀

While cleaning data is about fixing errors, this lecture focuses on transforming your data to boost model performance. We dive deep into Data Preprocessing Part 2, covering everything from handling categorical variables to complex mathematical transformations.

In this video, we explore how to avoid Overfitting, the math behind Orthogonal Features, and the essential techniques for Encoding and Normalization that every Data Scientist needs to know.

🎯 Topics Covered | المواضيع المشروحة:
00:00 Introduction
00:34 Overfitting vs. Underfitting (Bias vs. Variance Trade-off)
15:32 Orthogonal Features
27:00 Feature Engineering Encoding(Label & One-Hot Encoding
)
43:12 Advanced Encoding: Frequency, Target Encoding & Weight of Evidence (WoE)
1:01:38 Numeric Predictors
1:10:09 Mathematical Transformations (Box-Cox & Logit)
1:19:49 Data Normalization (Min-Max, Z-Score, Decimal Scaling)
1:33:11 Basis Expansion & Polynomial Features


الكورس مناسب لـطلاب علوم الحاسب، طلاب علم البيانات، محللي الأعمال، وأي شخص مهتم بفهم كيفية تحسين دقة خوارزميات التعلم الآلي.

📌أعمل فولو واخبط الجرس لو عايز تتابع الفيديوهات الجاية من الكورس Don't forget to subscribe and turn on notifications for upcoming lectures!

#DataMining #تنقيب_البيانات #FeatureEngineering #هندسة_البيانات #DataPreprocessing #MachineLearning #Overfitting #Normalization #DataScience #علم_البيانات #ArabicTutorial #شرح_بالعربي