There are some basic steps involved to develop a machine learning application. I will guide with the basic 7 steps to get started with a machine learning application.
You can collect the data from different sources. Like, you can collect the samples of data by scraping a website and extracting data, or getting the information from an RSS feed or an API. You can use device which measures wind speed and send the respective wind speed measurements in the form of data and that data you can use to make a ML application.
You can use crowdsourcing to collect the data. The number of options is endless. To save some time and effort, you could use publicly available data.
2.Prepare the Input Data
Once you have your data make sure that it is in a useable format.
You need to some algorithm specific formatting here. Some algorithms need features(Input data) in a special format, some algorithm can deal with target variables and strings in string format, and some algorithms need them in a integer format. So, make sure your data is in a format that can be used by your algorithm.
3.Analyze the Input Data
In this we look at the data from the previous task. Here, we check that we followed our first and second steps is properly followed, like you don't have empty values in your data. You can also look at the data to see if you can recognize any patterns or if there's anything obvious,such as few data points that are vastly different from the rest of the set.
You can plot your data in 1-D, 2-D or 3-D format that can aldo help you to analyze your data. But most of the time you'll have more than three features, and you can't easily plot the data across all the features at one time.
You have to use some advance methods which we will be discussing later on in the blog post.
If you're working with production system and you know that what the data should look like, or you trust it's source you can skip this step.
This step takes human involvement, and for an automated system you don't want human involvement. The value of this step is that it makes you understand you don't have garbage coming in.
5.Train the algorithm
This it the step where machine learning starts. This step and our next step includes the core algorithm . You feed the algorithm good clean data from the first two steps and extract knowledge or information.
This knowledge you often store in a format that's readily useable by a machine for the next two steps.
In the case of Unsupervised learning, there's no training step because you don't have a target value. Everything is used in the next step.
6.Test the algorithm
This is where the information learned in the previous step is put to use. When you're evaluating an algorithm, you will test it to see how well your algorithm does.
In case of Supervised learning, you have some known values you can use to evaluate the algorithm.
In case of Unsupervised learning, you may have to use some other metrics to evaluate the success.
In case if you are not satisfied you can go back to step 4, change some things and try testing again. Often the collection or preparation of the data may have been the problem, and you'll have to go back to step 1.
Here, you are at the end.
Here, you will make a real world program to do some task and once again you see if all the previous steps worked as you expected. If you encounter some new data then you have to revisit steps 1-5.
Happy Machine learning😊😊😊
In , next blog post we will talking about a language to implement machine learning,