Free 100 Days of Data Science Master Classes - Machine Learning for Data Science Part 3 | 8 hours Full Course in Hindi | The iScale #datascience

Github Link for Notes: https://github.com/TheiScale/30_Days_Machine_Learning/tree/main Connect in WhatsApp with us: https://wa.me/917880113112 Download Our Android App 📲- https://play.google.com/store/apps/details?id=com.logixhunt.ihhpet&pli=1 Python Lecture | How to Install Jupyter Notebook- https://www.youtube.com/watch?v=AS1a5K8zejk&list=PLxzTa0VPR9ryvGSuCm4RS8aeAvOLXz9XM&index=2 ➖➖➖➖➖➖ ✨ Kickstart your career in a Data Analyst. Apply today! - https://www.theiscale.com/DataAnalytic?analytics=13 Timestamps: 00:00 - Introduction of Machine Learning Full Course - Part 3 04:51 - Univariate Imputation: Mean | Median | Arbitrary & Random Value 01:28:11 - Categorical Data Imputation: Mode | Most Frequent | Missing Value 02:47:29 - Random Sampling Imputation: Numerical Data | Categorical Data 04:11:52 - Missing Indicator | Automatic Select Value | 2D/3D Calculation For KNN 05:13:38 - KNN Imputer | Find Euclidean Distance | Impute KNN Value 06:22:14 - Iterative Imputer | MICE | MCAR | MAR | MNAR Topic Content: Day: 11 Univariate Imputation: Mean | Median | Arbitrary & Random Value • Univariate Imputation in Numerical Data • Difference between univariate imputation and multivariate imputation • Mean or Median Imputation • Arbitrary Value Imputation • End of Distribution • Random value Imputation Day: 12 Categorical Data Imputation: Mode | Most Frequent | Missing Value • What is the purpose of using “Mode”? • What is the most frequent imputation? • Which variables can be imputed with the most frequent / mode Imputation? • When to use mode / most frequent category imputation? • Missing value imputation? Day: 13 Random Sampling Imputation: Numerical Data | Categorical Data • What is random sampling imputation? • Advantages in random sampling imputation. • Disadvantage in random sampling imputation. • Random Imputation in Univariate Imputation. • Random sampling imputation for Numerical Data. • Random sampling imputation for Categorical Data. Day: 14 Missing Indicator | Automatic Select Value | 2D/3D Calculation For KNN • Missing Indicator in Univariate Imputation • Automatic select value for Imputer parameter • Coordinate Geometric for KNN Imputer • Calculation in 2D and 3D Distance Day: 15 KNN Imputer | Find Euclidean Distance | Impute KNN Value • KNN Imputer • K-Nearest Neighbour Calculation Method • What is Euclidean Distance in Machine Learning? • How to find K nearest neighbour? • Find missing imputation value? Day: 16 Iterative Imputer | MICE | MCAR | MAR | MNAR • Iterative Imputer • MICE- Multiple Imputation by Chained Equations • Missing Completely at Random (MCAR) • Missing at Random (MAR) • Missing Not at Random (MNAR) • Find Predictive Value for Iterative Imputer Technique ➖➖➖➖➖➖ ❇️ Explore our Job Oriented Courses: https://www.theiscale.com/explore-course ➖➖➖➖➖➖ ❇️ For Jobs Updates on Data Analyst Profile, Join our WhatsApp Group- https://whatsapp.com/channel/0029VaB5ekEKQuJQV572vi2c ➖➖➖➖➖➖ 💢 For Any Further Queries or Doubts? Contact- 7880-113-112 (Student Helpline Number) ❇️ WhatsApp Now: https://wa.me/917880113112 ➖➖➖➖➖➖ ✳️ Join Telegram Channel- https://t.me/TheiScale #dataanalytics #datascience #machinelearning #freecourse #python #pythonprogramming #freeclasses #artificialintelligence #hindi #courseinhindi #job #part1 #9hoursstream #iscale #industrieshelpinghands #machinelearning #artificialintelligence #generativeai #GenerativeAIAWS #aws #amazon #amazonwebservices #techtrends2024 #innovation #aiinnovations #futuretech #ai #machinelearning #theiscale