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Machine learning projects can cover many different topics.

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It's important to design a framework you can use to approach different kinds of problems.

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You can consider what we're about to go through as like a little field guide that you can use for machine

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learning.

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So when you come up against a problem you can refer back to this field guide and go.

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Hold on.

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I need to break this problem down into a few little steps.

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What does a field guide say the framework we're going to be using comprises six steps.

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After working on many machine learning projects across multiple different industries these are the steps

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I found which come up time and time again.

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We're going to see this this diagram a lot in the next few lectures but in this one we're gonna We're

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gonna dive into each of these steps individually and and see what what kind of components they have.

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Step one is Problem Definition.

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Since we'll be focused on code first practical solutions it's important to define what problem we're

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trying to solve.

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Is it a supervised or unsupervised learning problem.

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Is it a classification or regression problem.

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Don't worry we'll see how to figure these out in the next few lectures.

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Step two is data since machine learning involves using algorithms to find and learn different patterns

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in data.

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Data is a requirement for any machine learning project.

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The question we're trying to answer here in step two is what kind of data do we have.

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Depending on the problem there are different kinds of data structure data such as rows and columns or

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what you'd expect to find in an Excel spreadsheet or unstructured data such as images or audio.

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Once we know what kind of data we have we can start to make decisions on how to use machine learning

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with it Step three is evaluation here will define what success means to us.

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Since machine learning since much of machine learning actually is experimental you could keep going

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forever trying to improve your results in search of the perfect model.

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However since we are practitioners we know the perfect model doesn't exist.

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Instead we begin by saying for this machine learning real estate project to be feasible we need at least

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a 95 percent accurate model at predicting the cost of houses.

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Of course in the beginning this evaluation metric won't be exact and will likely change over time.

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But having this at the start of a project gives us something to aim for Step 4 is features.

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The question we answer here is what do we already know about the data.

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Now even within different types of data there are different kinds of features.

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For example for predicting whether or not someone has heart disease you might use their body weight

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as a feature since body weight is a number.

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It's called a numerical feature and after talking to a doctor they might tell you if someone's body

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weight is over a certain number.

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They're more likely to have heart disease.

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There are more kinds of features such as categorical and derived.

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We're going to look at these in future lessons.

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But the premise remains a machine learning algorithms goal is to turn these features such as weight

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sex blood pressure and chest pain into patterns to make predictions such as whether or not a patient.

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We've got unique patient ideas here has heart disease or not Step five is modelling.

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Once you've learned a little bit about your data the next step is to model it.

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The question here is based on our problem and data what machine learning model should we use.

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Unlike other algorithms and sets of instructions you have to write from scratch.

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Many of the most useful machine learning algorithms have already been coded for you which is beautiful

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for us.

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Some models work better on different problems in others and in the beginning your focus will be to figure

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out the right model for the right kind of problem.

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Step six is experimentation.

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All of the steps we've just been through happen in a cycle.

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You might start out with one problem definition and find your data isn't suited to it then you might

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build a model and find it doesn't work as well as you outlined in your evaluation metric.

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So you build another one and you find out this one actually works pretty good.

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What's important to remember is although these steps are here those steps that we've been through in

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this framework it doesn't mean that they have to be followed in order nor are they set in stone.

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Consider them a rough guide now we've been through each of them briefly.

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Let's look at each one in a little bit more detail.