1) Collect data: You could collect the samples by scraping a website and extracting data, or you could get information from an RSS feed or an API. To save some time and effort, you could use publicly available data.
2) Prepare the input data: Once you have this data, you need to make sure it’s in a use-able format. You may need to do some algorithm-specific formatting here.
3) Analyze the input data: This is looking at the data from the previous task. This could be as simple as looking at the data you’ve parsed in a text editor to make sure steps 1 and 2 are actually working and you don’t have a bunch of empty values.
4) Train the algorithm: This is where the machine learning takes place. This step and the next step are where the “core” algorithms lie, depending on the algorithm. You feed the algorithm good clean data from the first two steps and extract knowledge or information. This knowledge you often store in a format that’s readily use-able by a machine for the next two steps.
5) Test the algorithm: This is where the information learned in the previous step is put to use. When you’re evaluating an algorithm, you’ll test it to see how well it does. In the case of supervised learning, you have some known values you can use to evaluate the algorithm.
6) Use it: Here you make a real program to do some task, and once again you see if all the previous steps worked as you expected. You might encounter some new data and have to revisit steps 1–5.
2) Prepare the input data: Once you have this data, you need to make sure it’s in a use-able format. You may need to do some algorithm-specific formatting here.
3) Analyze the input data: This is looking at the data from the previous task. This could be as simple as looking at the data you’ve parsed in a text editor to make sure steps 1 and 2 are actually working and you don’t have a bunch of empty values.
4) Train the algorithm: This is where the machine learning takes place. This step and the next step are where the “core” algorithms lie, depending on the algorithm. You feed the algorithm good clean data from the first two steps and extract knowledge or information. This knowledge you often store in a format that’s readily use-able by a machine for the next two steps.
5) Test the algorithm: This is where the information learned in the previous step is put to use. When you’re evaluating an algorithm, you’ll test it to see how well it does. In the case of supervised learning, you have some known values you can use to evaluate the algorithm.
6) Use it: Here you make a real program to do some task, and once again you see if all the previous steps worked as you expected. You might encounter some new data and have to revisit steps 1–5.
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