Numbers, Arrays, and NumPy: How AI Represents Data
In the previous article, we used pandas to load a dataset, inspect its rows and columns, find missing values, filter records, and calculate useful statistics.
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Working With Data Using Pandas
Once you move from basic Python into data science, machine learning, or AI, one of the first things you need to become comfortable with is working with tables of data. Most AI projects do not begin with a clever model or a complicated algorithm.
Python for AI: The Minimum You Actually Need
If you already know how to write a variable, create a function, run a loop, and use an `if` statement, you know enough Python to start moving toward AI.
What Makes a Good AI Problem?
Artificial intelligence is powerful, but that does not mean every problem should be solved with AI. In many situations, a simple rule, a spreadsheet formula, or basic automation can solve the problem faster, more cheaply, and more reliably. A good AI project begins by asking a more important question than “How can we use AI?” The better question is: “Does this problem actually need AI?”
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