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Hands on Machine Learning - Chapter 3 - Classification
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- čas přidán 8. 05. 2021
- A complete overview of Chapter 3 of the book Hands-on Machine Learning with Scikit-Learn Keras & Tensorflow
Dataset: drive.google.com/drive/folder...
You can get the book here: amzn.to/2SmaLBH
If you'd like to get the code along with much more soon to come please consider supporting me on my Patreon: / shashankkalanithi
FREE Python Tutorial: • Python for Data Analys...
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Twitter: / kalamari95
Hey dude, I really want to thank you for your high quality content, Machine Learning seems so much more easier to comprehend when you explain it so thanks a bunch man
Great video! Thank you Shashank. I like your pace and the way you describe how to progress through the tasks. Just btw, my prof for my MSc used to speak way too slowly, so I used to listen to his recordings at 1.25 to 1.50 speed lol (no need to speed you up, its perfect!)
Great content my man!
Really well explained.
I'am Working from the second edition and it contain further explanation for the multiclass classification , Multilabel classification that i thought you would explain in that video :)
That's really really great content
Thanks 👍
Loved the session
Thank you Aditya! Get ready for the next one!
thank you!
Hope you are doing good.loved the session.want to know about that how we can understand that what type of columns we have to go with in a dataset like some we have target and some we have to check for numerical or categorical.can you guide a little bit with in a video if possible.it’S help for beginners to understand.
For multiclass classification what is best to evaluate? Do you use precision and recall for each target variable and a different curve for each?
Hi Shashank, I’m new to data analytics. Can you tell how to start learning about it and make a career in it?
I would like to see something with transforming data on datasets. My biggest struggle is transforming and doing feature engineering. It might just be because I’m dumb. Not sure yet
Im right with you
i dont think we actually used the 'preprocessing' function that was created..? or did i miss it. watched twice. was wondering how you would reference it for future..
Thank you for your great teaching course. I have a question! Why when we want to rescale our dataset, we do not use all data together to rescale them and then separate them into training and test datasets? Why you first separate training data and then by using the training data you rescaled the data?
Because you do not want your results to be scaled. For suppose,if you are dealing with a regression problem(ex: housing prices) and if you predict scaled housing prices , it wont be appropriate. Scaling in neccessary for ML algo's to save machines resources, avoid integer overflow and unwanted results.
Can we get these notes ?
Thank you for the contents. Request you to kindly share your text contents
I like how you do it wrong and make it right. But could u make a another video of chap 3 that is the same as the book?
Hi shasank
Is the code editor VS code ?
its jupyter notebook
Can you upload for all other chapters as well?
Working on it :) chapter 4 is a bit more complicated so my upload speed will probably slow down
mmmmmm me cuesta todavía el ingle... por suerte hay compas q hacen lo mismo en castellano xd no mas
the voice is a little fast.
Hi Shashank, I’m new to data analytics. Can you tell how to start learning about it and make a career in it?
Hey Suchir thanks for watching. That’s a pretty complicated question but one that I’ll probably make a video of sometime in the future
@@ShashankData yeah Shashank thanks