How is data prepared for machine learning?

Data is one of the main factors determining whether machine learning projects will succeed or fail. That's why it is necessary to prepare data in the most digestible form for a future ML model.
Watch our video to find out more about data preparation for machine learning:
00:00 Intro
01:19 Dataset size
03:07 Dataset quality
04:35 Labeling
06:35 Data reduction and cleansing
09:56 Data wrangling
12:14 Feature engineering
Sources:
[1] www.reuters.com/article/us-am...
[2] www.sciencedirect.com/science...
Read more in our article: www.altexsoft.com/blog/datasc...
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[@anirbansarkar6306]

This channel has got one of the best videos and visuals which gives indepth understanding. Thank you so much for such immense hardwork

[@mukundyadav6913]

The analogy goes like this: modelling, tuning the hyperparameterss and using different algorithms to get better results is like working out, whereas data is like the food you put in your body. No matter how much you workout, how good and intense and your workout sessions are, if you dont eat good, balanced, high quality food then you are not gonna be healthy and get good results. Similarly, if you dont use good and high quality, accurate data then your model will not produce efficient results(data is literally the food for the model). So, when trying to improve your model's results, always try to improve your data quality first instead of changing your parameters and algorithms!

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[@goldenmilktea4992]

Great content as usual. Now I have a better understanding of what machine learning is. Thank you!

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Very informative video. Covers all the topics.

[@kaasboyzz]

Awesome video. Thanks!

[@srimant101]

Very useful. Thanks a lot

[@sidlist]

Great Content in very simple language. Can you please make a detailed video on ''Feature Engineering'' - Handcrafted features and Derived features?

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CHAD AI

[@Seiven2077]

AI was correct. there is nothing sexist about being statistically right. Men are statistically better achievers, they work more, prefer more technical majors and manage stress better -> get hired more for such positions because they fit better. If you want to have a balanced race and gender for your data set you will end up with average at best candidates, because some talent would be excluded as a result of Quota sampling. And this is not what you want as a HR. Gender factor is important, that is why you don't hire creepy men to work in kindergarten, nursing or daycare. Stereotypes + less success in this field statistically among men.