Last Updated on January 9, 2019
So, you’re working on a machine learning problem.
I want to really nail down where you’re at right now.
Let me make some guesses…
What You Will Learn
1) You Have a Problem
So you have a problem that you need to solve.
Maybe it’s your problem, an idea you have, a question, or something you want to address.
Or maybe it is a problem that was provided to you by someone else, such as a supervisor or boss.
This problem involves some historical data you have or can access. It also involves some predictions required from new or related data in the future.
Let’s dig deeper.
2) More on Your Problem
Let’s look at your problem in more detail.
You have historical data.
You have observations about something, like customers, voltages, prices, etc. collected over time.
You also have some outcome related to each observation, maybe a label like “good” or “bad” or maybe a quantity like 50.1.
The problem you want to solve is, given new observations in the future, what is the most likely related outcome?
So far so good?
3) The Solution to Your Problem
You need a program. A piece of software.
You need a thing that will take observational data as input and give you the most likely outcome as output.
The outcomes provided by the program need to be right, or really close to right. The program needs to be skillful at providing good outcomes for observations.
With such a piece of software, you could run it multiple times for each observation you have.
You could integrate it into some other software, like an app or webpage, and make use of it.
Am I right?
4) Solve with Machine Learning
You want to solve this problem with machine learning or artificial intelligence, or something.
Someone told you to use machine learning or you just think it is the right tool for this job.
But, it’s confusing.
- How do you use machine learning on problems like this?
- Where do you start?
- What math do you need to know before solving this problem?
Does this describe you?
Or maybe you’ve started working on your problem, but you’re stuck.
- What data transforms should you use?
- What algorithm should you use?
- What algorithm configurations should you use?
Is this a better fit for where you’re at?
I Am Here to Help
I am thinking about writing a step-by-step playbook that will walk you through the process of defining your problem, preparing your data, selecting algorithms, and ultimately developing a final model that you can use to make predictions for your problem.
But to make this playbook as useful as possible, I need to know where you are having trouble in this process.
Please, describe where you’re stuck in the comments below.
Share your story. Or even just a small piece.
I promise to read every single one, and even offer advice where possible.
If you are struggling, I strongly recommend following this process when working through a predictive modeling problem: