When we build any type of project, there are checks that the project should accomplish. In the case of machine learning projects are nos distinct.Since now we have been explaining the mathematics(Statistics, Probability, Linear algebra, Calculus) that will allow us to understand how machine learning models work. But machine learning is more than an algorithm, it’s easy to train a model by itself, the difficult part is making it useful!

A basic structure for your machine learning projects

Define the problem

The first thing to do is to detect a problem and try to answer these questions:

• What objective do we have?Who is going to use the solution?What’s the actual workaround to this problem?What type of algorithms will work(Supervise/unsupervised, etc…)Select a performance metric/validation metric.Is the problem similar to other problems already solved?Define the assumptions that we will consider.

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