What Is Feature Engineering? Missing Values, One‑Hot Encoding & Scaling скачать в хорошем качестве

What Is Feature Engineering? Missing Values, One‑Hot Encoding & Scaling

Learn feature engineering in the simplest way possible! In this video, I explain step by step: What is feature engineering in machine learning What are feature variables and target variables Why we use feature engineering (and why it’s so important) How to handle missing values in your dataset How to use SimpleImputer to fill missing values How to use OneHotEncoder to convert categories to numbers How and why we do feature scaling (standardization / normalization) A simple end‑to‑end example so beginners can follow easily This video is made for absolute beginners, students, and anyone who wants to understand ML preprocessing in very simple words. No advanced math, just clear concepts and practical examples. numpy:    • NumPy Tutorial in One Shot 🚀 | Complete Nu...   pandas:    • Pandas Tutorial for Beginners | One Shot F...   python playlist:    • Python for Beginners: Complete Programming...   If you find this helpful, don’t forget to LIKE, SHARE and SUBSCRIBE for more easy machine learning and data science tutorials! #FeatureEngineering #MachineLearning #DataScience #MLForBeginners #PythonML #MissingValues #SimpleImputer #OneHotEncoder #FeatureScaling #DataPreprocessing feature engineering for beginners, what is feature engineering, features and target variable explained, feature and target variables in machine learning, why we use feature engineering in ml, handling missing values in python, how to fix missing values, simple imputer sklearn tutorial, sklearn, SimpleImputer example, one hot encoder sklearn, one hot encoding explained simply, encoding categorical variables, feature scaling in machine learning, standardization and normalization, ml preprocessing tutorial, data preprocessing in machine learning, machine learning basics in hindi (add if video is in Hindi), feature engineering tutorial, easy machine learning tutorial