the best part about your teaching is the path to follow, that you show before starting out something I have wasted a lot of time going round and round while trying to learn ds ml it saves a lot of time if there's a clear path laid out and you know what you have to learn. Thank you so much man, you are amazing. :)
Great content, you have put everything in structured way which is great part of your teaching and teaching in very simple way, I will watch each and every video more than 5 times, God bless you for educating others.
Can anyone help me understand the difference between Feature Transformation and Data Preprocessing? Aren't both of these doing the same thing like Missing value imputation, Outliers detection , Scaling ? If we have already done data processing, why do we need to do feature transformation?
there is a catch, Data Preprocessing involves Data Cleaning,Scaling,Encoding Categorical Variables,Handling Imbalanced data etc. Feature Engg involves creating new features or transforming existing feature such as deriving features like ratios,Dimensionality Reduction using PCA,Feature Selection,Feature Extraction
I have paid for some courses, its fully waste. urs is next lvl of understanding, man let me get an internship, I will surely transfer u a part of small amount as GuruDhakshana
Sir can I please ask why did you covered data cleaning part after EDA ? I'm having a little confusion about it can you please tell. Is there a specific reason ?
Sir web scrapping ki ek apor video bna dijiye isse pahle wala samjh me nhi aaya wo Jo aapne recorded dala tha plz sir mai aapki Puri playlist follow kr raha hu
Guys I have doubt, can anyone help. For scaling data: we have numerical column and categorical column are encoded in to numerical. So scaling need to done only on numerical column or on encoded column as well