How Machine Learning Algorithms Works? A 7-Step Model

Aren’t you surprised to understand the logic behind how Netflix or Amazon Prime subscribes to the kind of movies you love watching? Or perhaps, what makes Google maps predict there’s traffic on the route you’re traveling?

We’re all aware of the fact of how machine learning uses algorithms along with statistical models to perform tasks and come up with the perfect solution. Similarly, this kind of approach detects cancer and it helps in detecting faces on Facebook along with multiple uses.

Machine learning: The need

Machine learning algorithms mimic humans and the manner they’re developing daily. In simple terms, machine learning can be broken down into two concepts: Training and prediction.

Machine learning is already seen taking place in our everyday lives, yet we barely realize it. For instance, tagging people on social media platforms is nothing but the work of machine learning. Machine learning applications are being widely used – fraud detection, recommendation systems, and recognition. The day won’t be far where machine learning will be used in technologies for self-correcting, providing insightful values, and personalization.

How do machine learning algorithms work?

Machine learning creates a system that will answer every question the user needs to ask. Then this system builds a model by training the algorithms most appropriately based on which the questions are answered.

To be precise, machine learning has a 7-step model that needs to be followed: –

From detecting escalators that needs instant repairing to the detection of skin disease, machine learning has given birth to the computer systems to work magic with things we cannot fathom. But how does machine learning work? What are the steps taken and how do they function without explicit programming? Here’s what you need to know.

Over here we’ll be demonstrating how machine learning works by quoting an example: Beer and wine will be our examples through which a system will be created, to which the system will answer questions to clarify whether the given drink is wine or beer.

beer and wine for example

1. Data gathering

A simple example can be illustrated here. The data that is to be collected is taken from glasses that are filled with beer or either wine. As such, the data gathered here could be anything, from analyzing the shape of the glass to checking the amount of foam. Over here, the color of these liquid has been picked to be the wavelength of the light and the content (alcohol) is taken as features. The first and foremost step includes purchasing several types of alcohol from the retail store along with equipment to make the right and apt measurements such as spectrometer for measuring the color and perhaps a hydrometer for the alcohol content.

This step is crucial since the quality and quantity of the data gathered will further help in determining the right quality of the predictive model. Collecting the alcohol content and color of every drink is to find out whether the content consists of wine or beer which is the same system that is prepared to train our data.

2. Preparation of data

Once the data is being gathered, it is needed to be loaded in a system and prepare it for training in machine learning.

The data is placed randomly so that the system should not learn what is not part of determining whether the drink is a wine or a beer. The system itself should be able to recognize whether the drink is a wine or a beer. Visualizations can be done to ensure there’s no imbalance taking place between variables.

However, if we collect more data for the beer as compared to the wine then the model trained can show a certain amount of biasness toward the beer since most of the data collected is of the beer. But in real-time, if the model comes across an equal amount of both beer and wine, then perhaps half of the prediction of the beer could be wrong.

Thus, presenting the right amount of data for both variables is equally important.

3. Choosing the appropriate model

How does one know which model would be appropriate? According to multiple researchers and data scientists, it is evident the expertise would have an idea about choosing the right model.

For instance, some of these models are designed and are suited best for sequences like music or text, whereas some are great for numerical. In our example of beer and wine, it will be a linear model as you will see two distinct features, both of a beer and a wine.

linear regression diagram
Source: Towards Data Science

4. Training the model

This is one crucial process, as such that it uses data further improving the model’s performance – prediction whether wine and beer.

The formula: y=m*x+b

While y is the interceptor, m is the slope of a line, also y denotes the value of line at the x position, and b is the y interceptor. The slope m, b and y interceptors are the only values that can be trained and valued.

In machine learning, you will come across multiple m variables. However, a matrix such as a w matrix or weight matrix can be constructed from this information.

5. Evaluation

Next comes the evaluation, the evaluation process is needed to check whether is well-trained or competent. Through this method, you will easily get to test your model against data that were never released. This happens just to ensure how the model responds to the data it hasn’t come across yet. Evaluation is ideally done to analyze how the model might perform in real-time.

6. Hyperparameter tuning

This happens to check whether or not there is still room for improvement in the training model. This is easily done by tuning certain parameters – learning rate or how many times have the trained model runs during the training session.

During the training session, there are multiple parameters to be considered. For each parameter, they should be able to specify or define what makes a model suitable for your use, else you might find yourself wasting your time or tweaking parameters for a longer duration of time.

7. Prediction

The last step, once the above parameters have been followed the model can be run for tests. Given the color and the alcohol percentage, the machine can predict which drink is beer and which is wine. Machine learning helps determine the difference between wine and beer with the help of the model rather than using standard rules or human judgment.

ML algorithm working model
Source: randomtrees

Known applications of machine learning

It is incredible to experience how we’re already using machine learning even before we could realize it. Machine learning has been known to make its way in multiple industries and professions such as medical diagnosis, speech recognition, learning associations, financial services, prediction, and many more.

  • Medical diagnosis

Machine learning provides tools and techniques benefitting the medical sector as such it helps in solving prognostic and diagnostic problems.

It can be used to analyze the significance of clinical parameters for prognosis, for instance, it helps in predicting the progression of the disease, it also helps for therapy planning, and overall majorly used for patient management.

  • Speech recognition

In speech recognition, machine learning helps to translate the spoken words into texts known as automation speech recognition or speech to text or computer speech recognition.

  • Learning associations

This is a process through which insights are developed into associations taking place between products. Simply put, unrelated products can also reveal their association with one another.

  • Financial services

Machine learning systems are great tools to detect fraud by constant monitoring of the activities of individuals and assess is the activity of that individual is typical of the user or not.

  • Prediction

Machine learning offers the ability to predict the probability of the customer faulting a loan payment. However, for computing to happen the system needs to classify data for certain groups.

Organizations are now seeing progressive growth in machine learning. Experts believe it is rather tough to predict the future of machine learning owing to its drastic growth.


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