How to Build a Machine Learning Model for Beginners

Data science is the ultimate technique you can use to boost your business. It gathers valuable information about your customers, market, and business. That’s why many companies invest in complex server infrastructures, IoT, and various other artificial intelligence-powered technology.

Today’s topic would be helpful to all data scientist enthusiasts willing to build their machine learning model. We will explain what a machine learning model is and how to create it. Also, we will give tutorials and simple examples you can make from scratch. In the end, if you are still hungry for more information, we will share other links where to find more valuable and informative articles to guide you through your science journey.

What is a Machine Learning Model?

Before we get in-depth with our exciting topic, we must ensure that you fully understand the machine learning model and the way it works.

A machine learning model, in its essence, is a mathematical file. You can train it with an algorithm (code) to recognize patterns and make predictions. Every machine learning model requires a significant amount of data for the training process. We can find this variety of data in data warehouses or data lakes where it's usually stored.

The training model stores the training data of the learning machine algorithm. It represents other algorithms and specific data structures needed for forecasts, such as neural networks, evolutionary neural networks, and machine vision.

How to Build a Machine Learning Pipeline?

So far, we have talked only about the basics of the machine learning models. Now is the time that we get right into the core of the topic and share the key steps of building a machine learning pipeline with all beginner data scientists.

What is a machine learning model
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Set Goal

In this step, you have to introduce the model to the problem it faces and the final output. While you work on it, many topics and issues will invade your mindset. But the first questions you have to ask yourself are:

  • What is the purpose of this model?
  • What data will you use for input?
  • What is the expected output?

By answering these questions, you will compose the plan for your workflow. In most cases, we cannot say that the model will work correctly and deliver the wanted result before we finish it. And even then, the model may need more training cycles to achieve maximum accuracy.

Collecting Data for Your Machine Learning Model

The second step of creating a working ML model is to collect the required data. Depending on what they make the model for, you can get a labeled or unlabeled dataset. Collecting data is an easy step, so we won’t get into many details. The critical part here is what data you will be using. We highly recommend you to work with labeled one. The reason is that the labeled data is more manageable and can be applied directly to the training model. if you don't have enough data or suspect that your data might be biased, you can contact a specialized data science company to generate synthetic data for you. Synthetic data helps avoid bias, enlarge smaller datasets, and generate required amounts of structured data.

Machine Learning Model Training

Training machine learning model

The actual machine learning process starts when you train your model. This training is always a cyclic process. Depending on the data you have and the cycles you run through the model, the predictions can only improve. Your model’s decisions will become more accurate the more training sessions you run. Once developed and prepared, machine learning algorithms help design and create systems that can automatically interpret data. They use the patterns in the training data to perform classifications and future predictions.

Sometimes the deployment of the model can happen before finishing it at a hundred percent. For example, your model may not be completely working but still giving almost perfect predictions. After deployment, it can keep progressing and running training cycles.

The accuracy of your model will depend on bias and variance. High bias will result in a too simple model and performs poorly both on training and on the test data. Variance can cause the opposite - a model that is too complex. Why is it a problem? Because training data is usually a smaller sample from a large dataset, and it contains noise. An overly complex model starts capturing this notice and performs below average when you test it on out-of-sample data. This is because the model knows a lot about the sample training data but very little about everything else.  As with many things in life, we can't have everything. So having low bias and low variance is impossible. As a result, your goal should be to find the balance between both.

Here is how this process looks like:

Machine learing model graph

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Examples of Machine Learning Models

As follows, we will give you some simple examples of ML pipelines to make things more clear for you. Whether you are a beginner or advanced in creating AI models, you may find these examples helpful and informative.

Social Media Mining

The first example relates to social media platforms, where you can gather a lot of useful information and use it in your favor. We can call this model "Social Media Mining." As you can see from the name, you probably will mine in some of these platforms to collect data from particular sources or even track behavior. Marco Bonzanini created an interactive and simple tutorial on mining Twitter data with Python. It is ideal for all beginners who want to put something new into their portfolio.

Disease Prediction

We can relate our second example to improving health care. Nowadays, all the countries' health care systems are experiencing some crisis due to the COVID-19 pandemic. Also, the shortage of specialized staff, doctors, and nurses doesn't make things any better. We can find a suitable solution for artificial intelligence. The ML models standard in health care can help predict disease outbreaks, diagnose, classify image data (x-rays), indicate insurance risk factors, etc. The options with AI are countless and wait to be discovered. Here is a tutorial for a disease prediction model, which you can try to build.

Face Mask Detection

Now that the governments all over the world are reinforcing face masks in public areas and, in some countries, even outdoors, it is important to find a solution to detect people who are not wearing masks. At BroutonLab Data Science Company, we created this tutorial that even beginners can use to build a machine learning model to detect face masks. This model can be used in CCTV cameras for real-time detection and alerts.

Handwriting Recognition

The third example that we are about to give you may be a little challenging. The model refers to training a neural network to read handwriting. Here, you will need to gather a manageable dataset. The challenging part is that the data you will work with is image data, which is more demanding. Having said that, we can recommend a comprehensive tutorial for this model. So if you get a grip on machine learning and move to the intermediate level you can try building a handwriting recognition model that later can be used in apps to read bank cheques, legal documents, recognize and approve signatures, etc. There are many applications for this model in business and daily lives.

Do these examples seem complicated to you? If this is the case, we have saved an ordinary and straightforward model that everyone can build for the end. So if you always wanted to create a simple neural network in Python check out our article and give it a try.

Why Do You Need to Hire a Professional Data Scientist to Build More Complex Models?

We understand that not everybody can create ML pipelines. You can build the examples we've mentioned simply by following the necessary steps. However, things get complicated fast when starting larger and more complex projects. They might require significant knowledge in computer science, analytics, statistics, and maths.

For example, if you face a complex model, you can hire a team of data scientists to help you process and clear the gathered data. Additionally, this will optimize the operational flow, and the launching of the model will be faster. Having a team of data scientists or a data science company taking care of your machine learning projects will surely save you time and help deploy a properly working model.

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