How to Prepare Data for Machine Learning and AI
In this video, Alina discusses how to prepare data for Machine Learning and AI. Artificial Intelligence is only as powerful as the quality of the data collection, so it's important to prepare data for Machine learning correctly to ensure no data bias in the prediction models.
We received a lot of questions about how much data someone needs to run a prediction model for a business, and how to avoid common mistakes in data collection for AI. The answer is you don't need big-data to run AI models and Machine learning models, it can be done with ‘medium-data’!
It's also important to be careful with the quality of data collected for Machine learning and AI. Marketers must be careful, all the data that we collect for machine learning models can be subject to bias. This bias can fall into many categories, two common ones are selection bias and exclusion bias. And both types can affect the usability and relevance of your AI models and findings.
Selection bias is when the group of users used for data collection is not like your target audience. Exclusion bias is when you've already collected your data, but you hold back bits of your information from your analysis. Artificial Intelligence reveals unexpected insights and good data collection is driven by knowing we're all subject to bias. So instead of only feeding a subset of your data to a machine learning model, dump it all in. Even the stuff you think is irrelevant. If it's irrelevant, the algorithm will ignore it.
Just remember: - You don't need big data when you prepare data for machines learning & A.I. , you only need medium data. - Make sure that you try to avoid bias as much as possible when you collect data for A.I.
This video covers: Artificial intelligence in business Data collection for A.I. Machine Learning Data Bias Big Data AI tools Selection bias Exclusion bias Prediction model bias Machine learning Artificial intelligence Algorithmic bias Preparing data for machine learning Machine learning models Preparing AI datasets
