Everything we see as a final output has a process involved. Machine Learning algorithms also follow a process, it doesn’t matter you are working with numerical data, images, audio or data in any form the process remains the same. In this article, I will take you through the process of Machine Learning you should use for perfectly developing a machine learning application.
Most machine learning algorithms are different, but there are some common steps you should take with all machine learning algorithms when building a machine learning application. Here I will walk you through the whole process of machine learning.
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The Process Of Machine Learning
Collect Data:
The first step in the process of Machine Learning is collection of data. You can collect the samples by scraping a website and extracting data, or you can get information from an RSS feed or API. You can have a device collect wind speed readings and send them to you, or blood sugar levels, or whatever you can measure.
The number of options is endless. To save time and effort, you can use publicly available data.
Prepare the Input Data
The second step in the process of Machine Learning is data preparation. Once you have the data, you need to make sure it is in a usable format. The format we’ll be using in this book is the Python list. The advantage of having this standard format is that you can mix and match algorithms and data sources.
You might need to do some algorithm specific formatting here. Some algorithms need features in a special format, some algorithms can treat target variables and features as strings, and some need them to be integers.
Analyze the Input Data
The thrid step in the process of machine learning is analyzing the data. This means to look at the data from the previous task. It can be as simple as examining the data you’ve analyzed in a text editor to make sure Steps 1 and 2 are working and you don’t have a bunch of empty values.
You can also look at the data to see if you can recognize patterns or if there is something obvious, like a few data points that are very different from the rest of the set.
Plotting data in one, two or three dimensions can also help. But most of the time you have more than three features and you cannot easily plot the data for all features at once. However, there are some advanced methods you can use which we will talk about later to reduce multiple dimensions to two or three so that you can visualize the data.
Some Human Involvement
The fourth step in the process of machine learning is human involvement. If you are working with a production system and you know what the data should look like or if you trust the source, you can skip this step.
This step requires human involvement, and for an automated system, you don’t want human involvement. The advantage of this step is that it lets you know that you have no waste.
Train The Algorithm
The fifth step in the process of machine learning is training the algorithm with data. This is where machine learning takes place. This step and the next step are the main algorithms, depending on the algorithm. You feed good clean data into the algorithm from the first two steps and extract knowledge or information.
You often store this knowledge in a format that is easily usable by a machine for the next two steps. In the case of unsupervised learning, there is no training step because you have no target value. Everything is used in the next step.
Test The Algorithm
In this step in the process of machine learning, the information learned in the previous steps is put to use. When you evaluate an algorithm, you test it to see how well it works. In the case of supervised learning, you have some known values that you can use to evaluate the algorithm.
In unsupervised learning, you may need to use other metrics to gauge success. Either way, if you’re not happy, you can go back to step 4, change some things, and try testing again. Often, collecting or preparing the data may have been problematic, and you will need to go back to step 1.
Using The Model
The last step in the process of Machine Learning is using the model practically. Here you create a real program to perform a task and once again see if all of the previous steps worked as expected. You may encounter new data and need to revisit Steps 1 through 5.
I hope you liked this article on the steps involved in the process of Machine Learning. Feel free to ask your valuable questions in the comments section below.
