How Machine Learning Works

Last updated on May 20th, 2025 at 04:23 am

Artificial Intelligence(AI) is a popular and sought-after field of technology in today’s world. Every website and app is trying to use AI in some way or the other. Machine Learning is the backbone of every AI-based system. Unless you have properly implemented machine learning in your AI-system, it will not give the desired output. Therefore, it is essential to learn more about machine learning. In this article, we will learn how machine learning works.

What is Machine Learning

In simple words, Machine Learning uses a collection of algorithms & models to process an existing set of data to learn patterns from it. It then applies this knowledge on new sets of data, to get insights and make predictions. It is present in almost every AI-based system, and your AI-system is only as good as its machine learning capabilities. Machine learning is heavily used in retail, finance, and healthcare industries. For example, you can apply machine learning on retail data to predict demand and arrange for supply. You can use it to analyze a patient’s past healthcare data and diagnose diseases.

Types of Machine Learning

It is important to train your machine learning system using adequate and appropriate data. There are several ways to do this.

1. Supervised Learning

In this case, the model has to be spoon-fed labeled data where we specify the input values and their corresponding output values. This helps your system learn new information and patterns. It is done initially to seed the system with initial patterns and knowledge.

2. Unsupervised Learning

Once supervised learning is done to a certain extent, then the machine is fed unlabeled data. Here the machine has to determine patterns, groups, relations on its own, based on its existing knowledge. It can always use these results to strengthen its knowledge and improve accuracy of predictions.

3. Semi-supervised Learning

In this case, the model is trained using both labeled as well as unlabeled data sets. There may be some hand-holding but only to a certain extent.

4. Reinforced Learning

This approach is based on trial and error, where the machine keeps making moves based on its understanding of the environment. Based on repeated attempts, it keeps learning and improving its performance. This method is used commonly in gaming scenarios.

Components of Machine Learning

1. Algorithms

An algorithm is a sequence of steps to execute tasks. Algorithms can be about processing data, analyzing it, forming patterns & relationships, storing its results and more.

2. Models

Models are mathematical frameworks to store and apply the knowledge obtained by analyzing data. They are also used in making predictions. Frequency distribution, linear regression are some of the models that are used for machine learning.

3. Training

Training is a process where we feed back the result of past learning, current application and future prediction back into our models, to improve their performance.

4. Testing

Needless to say, every part of your AI-system has to be tested. We typically calculate r-squared or mean-squared error values for a dataset to check the performance of our model.

How Machine Learning Works

Now let us understand how machine learning actually works.

1. Data Collection

In the step, we collect data from different public and private data sources. The quality of data will determine the quality of knowledge derived from it, as well as the performance of your system. You can leverage social media, websites, APIs and even public data sets for this purpose. Sometimes, you may also need to connect to databases to import data. They should be credible and reliable. Also, make sure that you are not unlawfully scraping or stealing data, to avoid data theft and privacy concerns later.

2. Data Preprocessing

This is the part where we clean up the data before feeding it into our models. This involves identifying and removing duplicates, missing values, erroneous values. You may also need to transform multiple data sets into a single format for uniformity. You may also need to merge them so that they are easier to manage. Sometimes you may need to visualize data to identify patterns and outliers and take action.

3. Model Training

We use our data for training our model. We analyze the input data using our model and store the result as knowledge. You may want to split the data into two subsets – one for training and the other for testing. There are several types of models such as regression, neural networks, decision trees and frequency distribution, etc. used for this purpose.

4. Model Evaluation

Here we determine if our model gives desired output or not. There are two types of models – deterministic and probabilistic. Deterministic models will give a definite output for every input. Probabilistic models give output with a certain probability. So, depending on the type of model, you will need to determine what kind of metrics you need to use to evaluate your model. Accuracy, F1-score, k-fold are some of the commonly used methods for this purpose.

5. Model Deployment

Finally, we deploy the model into real-world applications. It can be done in a single step, if you have a small model. Otherwise, you can use a multi-step integration, if your model is complex with many moving parts. It is important to keep testing your model after deployment. If you are doing a multi-stage integration, then it is better to test the model at the end of each stage.

Key Points

There are some things to keep in mind while building a machine learning system.

1. Data Privacy

Ensure that you are responsibly sourcing data from reliable sources. It will train your model accordingly. If you get into legal problems due to data theft or scraping, then it is very difficult re-train a model or change its patterns. So it is better to be clean from the first step.

2. Data and Algorithm Bias

The data or algorithms you use may be skewed towards certain biases. In such cases, you may need to tweak your model or modify the data.

3. Interpretation of results

Your AI-system will be useful only if it gives easy to understand insights. So make it easy for users to easily understand the result of your system, using charts, graphs, tables, reports, dashboards, etc.

Conclusion

Today, AI is growing in importance and machine learning forms the core of every AI system. In this article, we have learnt what is machine learning, the different types and components of machine learning and also how machine learning works. We learnt about the different steps taken to build a machine learning system. It is equally important to learn about machine learning life cycle before you start building. These are fairly complicated systems and you need to be very careful while building, testing and deploying it. In fact, there are many machine learning libraries like NumPy, Tensorflow, etc. that will help you get started quickly. Since most AI systems are probabilistic in nature, their accuracy plays a very major role.

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