done once again

computation easy, understanding easy, numerical data represented on graph easily

we do feature encoding so the model can work easily on our data. Computation and processing become easier and faster because the CPU works with numerical values (binary numbers). Feature encoding makes the data simple to understand.

Done

Done

Very Important Lecture. Because without learning these techniques, we are unable to train our model. Or we can say that model will ignore our data, model will say you are poor and I am rich so go out from here and wear clean clothes, then meet me.

I learned in this lecture about (Algorithm Compatibility, Efficiency and Performance, Feature Representation, Support Unseen categories, Less Memory Usage, One Hot Encoding).

I learned from this video about (1- Algorithm Compatibility, 2- Efficiency and Performance, 3- Feature Representation, 4- Support Unseen category, 5- Less Memory Usage, and 6-One Hot Encoding)

AOA, I also learned how encoding is necessary. There are a few reasons why encoding is important; let's look at them. 1- Algorithm Compatibility ( work at numeric data ) 2- Efficiency and Performance ( less computation power ) 3- Feature Representation ( remove bias) 4- Support Unseen category 5- Less Memory Usage 6-One Hot Encoding ALLAH KAREEM ap ko dono jahan ki bhalian ata kray AAMEEN.

The main benefits of feature encoding are (1) algorithm compatibility match (2) efficiency and performance increase (3) feature representation becomes easy (5) less memory usage (6) one-hot simple understandable encoding