<jupyter_start><jupyter_text>Machine Learning for Medicine: Workshop What tools do we need? OverviewMachine learning (ML) is a powerful approach to studying nature through data.Lucky for us, ML is dominantly driven by *open source* tools, or tools that are freely available to all of us.In this notebook we'll outline the tools.Machine learning (ML) is a powerful approach to identifying and validating patterns in our data.What makes it challenging sometimes is that there isn't exactly a "standard toolkit".For more on the motivation head down to [background section](Background)In order to learn and apply ML you'll need a set of tools.Luckily, these tools are *general* tools that can actually help you program things in general.In this notebook we'll run through some of those tools and provide some resources to get more comfortable with ML tools. The list PythonIf you're viewing this then you're already interacting with Python and Jupyter.Python is basically a programming language, a way for us to tell the computer what to do.  JupyterJupyter is an addition to Python that lets us write notebooks with Python embedded in it.  SciPy and Stats<jupyter_code>import scipy.signal
import scipy.stats as stats<jupyter_output><empty_output>
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