Row 38654

Row ID: 38654 | Dataset Entry | Axioma AXP Content Repository

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This definitely depends on if you are in a tech or business role and whether or not you have people working under you, I will try my best to address how I would go about this in a couple different situations:

If you are a developer: In this case I would try to learn some ML algorithms and figure out how to build some neural networks and train them on the data. I find python most intuitive for machine learning work but R is great too and assuming you have access to all the training data you will need, these languages are both great for data manipulation so you will be able to build your datasets. After that you will be able to explore the data you have, do some regression modeling to see what variables or variable interactions have effects on your variable of interest. Finally, train and evaluate some models (depending on the problems you will want to try different algorithms) and see if they have some predictive validity.

If you are in a business role in charge of developers: First, look up some high level ai descriptions and particularly focus on machine learning. Do not worry yourself with the math or linear algebra and just do your best. Maybe just watch a few crash course videos and try to conceptualize how the data should be organized and how you would make the predictions and bring it to your developers. Figure out your X matrix (inputs) and y vector (outputs).

If you are in a business role not in charge of developers: Do the above steps for business role, but now you are the developer. Learn some basic numpy/python and use some chatGPT to help you organize the data. Training the models should be easyish once you have the data organized, you don't need to optimize and find the absolute best model just convince yourself that the model is able to successfully predict a reasonable amount of test entries when the test entries are separated from the training entries before training time. After this you have successfully made some predictions and you will be able to take this to some other people within the business and continue to improve the model before making real-time predictions on live data.

FieldValue
text This definitely depends on if you are in a tech or business role and whether or not you have people working under you, I will try my best to address how I would go about this in a couple different situations: If you are a developer: In this case I would try to learn some ML algorithms and figure out how to build some neural networks and train them on the data. I find python most intuitive for machine learning work but R is great too and assuming you have access to all the training data you will…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-22
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Raw Record

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  "text": "This definitely depends on if you are in a tech or business role and whether or not you have people working under you, I will try my best to address how I would go about this in a couple different situations:\n\nIf you are a developer: In this case I would try to learn some ML algorithms and figure out how to build some neural networks and train them on the data. I find python most intuitive for machine learning work but R is great too and assuming you have access to all the training data you will need, these languages are both great for data manipulation so you will be able to build your datasets. After that you will be able to explore the data you have, do some regression modeling to see what variables or variable interactions have effects on your variable of interest. Finally, train and evaluate some models (depending on the problems you will want to try different algorithms) and see if they have some predictive validity.\n\nIf you are in a business role in charge of developers: First, look up some high level ai descriptions and particularly focus on machine learning. Do not worry yourself with the math or linear algebra and just do your best. Maybe just watch a few crash course videos and try to conceptualize how the data should be organized and how you would make the predictions and bring it to your developers. Figure out your X matrix (inputs) and y vector (outputs).\n\nIf you are in a business role not in charge of developers: Do the above steps for business role, but now you are the developer. Learn some basic numpy/python and use some chatGPT to help you organize the data. Training the models should be easyish once you have the data organized, you don't need to optimize and find the absolute best model just convince yourself that the model is able to successfully predict a reasonable amount of test entries when the test entries are separated from the training entries before training time. After this you have successfully made some predictions and you will be able to take this to some other people within the business and continue to improve the model before making real-time predictions on live data.",
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  "datetime": "2024-05-22",
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