Machine learning is the process of making systems that learn and improve by themselves, by being specifically programmed.
The ultimate goal of machine learning is to design algorithms that automatically help a system gather data and use that data to learn more. Systems are expected to look for patterns in the data collected and use them to make vital decisions for themselves.
In general, machine learning is getting systems to think and act like humans, show human-like intelligence, and give them a brain. In the real world, there are existing machine learning models capable of tasks like :
•Separating spam from actual emails, as seen in Gmail
•Correcting grammar and spelling mistakes, as seen in autocorrect
Machine Learning Steps
The task of imparting intelligence to machines seems daunting and impossible. But it is actually really easy. It can be broken down into 7 major steps :
1. Collecting Data:
As you know, machines initially learn from the data that you give them. It is of the utmost importance to collect reliable data so that your machine learning model can find the correct patterns. The quality of the data that you feed to the machine will determine how accurate your model is. If you have incorrect or outdated data, you will have wrong outcomes or predictions which are not relevant.
2. Preparing the Data:
After you have your data, you have to prepare it. You can do this by Putting together all the data you have and randomizing it. This helps make sure that data is evenly distributed, and the ordering does not affect the learning process.
Cleaning the data to remove unwanted data, missing values, rows, and columns, duplicate values, data type conversion, etc. You might even have to restructure the dataset and change the rows and columns or index of rows and columns.
3. Choosing a Model:
A machine learning model determines the output you get after running a machine learning algorithm on the collected data. It is important to choose a model which is relevant to the task at hand. Over the years, scientists and engineers developed various models suited for different tasks like speech recognition, image recognition, prediction, etc. Apart from this, you also have to see if your model is suited for numerical or categorical data and choose accordingly.
4. Training the Model:
Training is the most important step in machine learning. In training, you pass the prepared data to your machine learning model to find patterns and make predictions. It results in the model learning from the data so that it can accomplish the task set.
5. Evaluating the Model:
After training your model, you have to check to see how it’s performing. This is done by testing the performance of the model on previously unseen data. The unseen data used is the testing set that you split our data into earlier. If testing was done on the same data which is used for training, you will not get an accurate measure, as the model is already used to the data, and finds the same patterns in it, as it previously did. This will give you disproportionately high accuracy.
6. Parameter Tuning:
Once you have created and evaluated your model, see if its accuracy can be improved in any way. This is done by tuning the parameters present in your model. Parameters are the variables in the model that the programmer generally decides. At a particular value of your parameter, the accuracy will be the maximum.
7. Making Predictions
In the end, you can use your model on unseen data to make predictions accurately.
Different Testing Techniques in Machine Learning
Regression Testing
Regression testing covers already tested software to ensure it doesn’t suddenly break, even after a change of component or module.
For example, retesting a dialer after making a feature upgrade. If the software didn’t work after several modifications, then it would be called a regression.
Unit Testing
Here, the program is split into blocks, or units, and each section is tested separately. Each unit is called and validated to ensure the individual components of the model meet the user requirements.
Beta Testing
Beta testing, or usability testing, gives selected target users an almost finished version of the program. It helps finding bugs and is carried out to match and validate the program with the user’s requirements.
Alpha Testing
This type of testing is done just before the product launches. It’s similar to beta testing, where users test the program. But this time it’s done in-house with the testing team. Alpha testing aims to find and fix bugs that weren’t discovered through previous tests
Integration Testing
In integration testing, the result set is taken from unit testing and groups of modules combined to see how they work together. The main purpose of integration testing is to ensure that modules interact correctly when combined and that the standards of the system and model are met.
Conclusion
A machine learning system is a powerful tool for efficiency and the proper maintenance of it is a must. Machine learning testing helps companies ensure that their software systems are meeting the desired quality, detecting bugs and flaws easier so that they can be quickly dealt with.