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Machine Learning is a relatively new field, but how to solve machine learning problems remains a challenge – not because it is difficult, but more so because the approach might not be efficient at arriving at a solution quickly. The most popular algorithms are implemented by deep learning and neural networks, but only recently have computers been able to solve problems where there isn’t an exact solution. In this article, we’re going to talk about how to use tools like Python and R for machine learning, learn about the popular methods for solving a problem in machine learning, and then wrap it all up with some more practical advice on how you can go from start-to-finish with your own project.
How to solve machine learning problems
1) Identify the problem
First, learn about the problem you’re solving. This is one of the most important steps when it comes to solving any sort of machine learning problem. Making an informed decision on your approach and how you will measure success will save you time and frustration in the long run.
This is true of any course in computer science or engineering; if you don’t know what to build, or what your end goal is, how do you know if your code works? How do you know that it’s fast enough? How do you know when to stop tweaking parameters? You don’t, because all of these are qualitative questions that are impossible to answer without a framework for evaluating success.
As developers, we need to pay attention to the main issues that will influence success. That’s where this article picks up. Let’s talk about some specific problems you may want to solve.
2) Prepare the data to solve it
Think about the data you will be using. In many cases, you will have to make adjustments to the variables coming out of your database or scraped data. As you learn more and more about this particular problem, it may become clear that you need a variable to represent something like gender, or another variable to represent something like birthday. So what do you do?
Once you have the data broken up in the way you want, look into how to produce and normalize it. We’ll talk more about what we mean by normalized data later, but for now let’s assume that we decide that we need a new variable for gender in order to support a model for predicting gender based on age.
Our first step is figuring out all of the sources of data that are necessary to build your model. If you’re using a database, then you’ll want to figure out how you’re going to pull in the dataset you want, and maybe even create new tables if it makes sense for your application (and don’t forget about scaling!).
We should also look into how much data is available in each variable. There will be times that you’ll be able to pull values from these variables, like pulling birthdays from a database, but on other occasions, you’ll have to do some type of calculation based on it in order to get a meaningful output for your machine learning problem. If we know the quantities aren’t going to change, then we might as well save ourselves the hassle of having to normalize everything now.
3) Build a model
The next step (and arguably the most important part) is picking out algorithms that are specific for your problem and building a model. This is the most abstract part of learning machine learning because you’ll be doing a lot of experimenting with different algorithms to see what works best.
The first step is to make sure you’re building a model with the algorithm that’s written for your data, rather than just using an algorithm from the pack without thinking about how it will work with your specific data. For example, if your data has many features like height, weight, and gender and you’re trying to classify them based on age, then a typical neural network wouldn’t make sense because it doesn’t explicitly take gender into account.
You’ll need to experiment with all sorts of different techniques that aren’t yet implemented by default in most machine learning libraries. Just remember to keep in mind that these techniques are only going to work with your own data, so if you have a data set that doesn’t match the assumptions made by your algorithm, you’re in trouble.
4) Test and iterate
Once you’ve finished building the model, get out there and start testing it. Testing will help you find errors quickly and refine it as necessary. When you’re done testing, go back over what worked for each algorithm that you tested. This is where you pick out the best algorithm for your data and run with it, then try to use that same list of constraints or priors to reuse the algorithm code for future problems.
When you’re making predictions on your machine learning problem, it may also be appropriate to change some of your data variables so that you can better evaluate (or train) the model. Why? Because in many cases there will be a different win condition when your predictive output is based on values that are meaningful rather than the original variables that were just used as inputs.
5) Automate and scale up
Once you’ve got everything working, there are many ways to go about automating and scaling up this process. You can automate some of the preprocessing, or the training and testing, for example. This will save you a lot of time and make it easier to take your code to production.
But that’s not all! You can also look into what other problems people have faced in their own projects and see if you can use that work as a general guide on how to solve your own problem. This could mean learning about how to train models with overfitting so that you don’t end up making the same mistakes again or perhaps using different approaches depending on what other data is available in your problem.
In the end, even though machine learning can feel daunting and confusing, it’s an essential tool that can help you make powerful applications for your business. In the time it takes you to apply this best practice to your own projects, you can start taking advantage of all of the benefits that machine learning promises.
It may take a while before you get a working model and see how much it improves your work, but in the end, it will be worth it. You’ll be able to answer more questions about whether certain features are significant in a data set and how these features spill over into more complicated problems like anomaly detection. You’ll be able to generate visualizations and dashboards in seconds that would have taken you hours before.
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