9 Steps of Machine Learning: Complete Process

ChatGPT made its shocking debut in the world of modern data science In December 2022. The technology behind it may change our overall lives in the future. It’s time for us to unlock the world of machine learning.

But what do machine learning steps involve?  

In this article, we will walk you through each of those steps, so that you have a superior comprehension of how machine learning functions. Let us introduce you to the basics first!

What is Machine Learning? 

Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on building models that allow computers to make data-driven decisions. It involves algorithms and statistical models to enable computers to learn from data and make predictions or decisions without explicit programming. 

Implementing machine learning is not a straightforward process, and requires a series of steps to be taken. So, how does machine learning work?

To get a better idea, check out the machine learning flowchart below:

Applications of Machine Learning

Its primary aim is to allow machines to learn and adapt to new scenarios or situations.

Machine learning applications are diverse. Here I list some of the most popular ones and common in our daily life: 

  • Image recognition: Automatic friend tagging suggestions on Facebook is the most common use case for face and image recognition.
  • Speech recognition: The “Search by Voice” option on Google is a great example here. Virtual assistants, such as Siri, Alexa, Google Assistant, and Cortana, assist us in finding information through voice instruction as well.
  • Email spam filtering: All the emails you receive are usually divided into three categories: important, normal, and spam. What is more, machine learning technology can identify any potential malware threats/
  • Product recommendation: When you search for products or services on a particular website, Google “learns” this information and makes conclusions about your interests. As a result, related advertisements appear.
  • Online fraud detection: Luckily for you, the machine learning models used in online banking are able to recognize fraudulent transactions.
  • Spelling and grammar check: You can see it in the Autocorrect function of your smartphone, for instance.
  • Medical diagnosis: Machine learning is used to diagnose diseases in medical science. With this, clinical innovation is developing extremely quickly.

9 Steps of Machine Learning

Generally, machine learning workflows include data preparation, model creation, training, evaluation, and deployment. 

Here are the steps to follow to start the actual machine learning process.

#1 Data Pre-processing

Properly prepared information is the best way to set yourself up for successful learning outcomes. That’s why data preparation for machine learning is one of the most important machine learning phases. Find out the main actions you should start with:

1) Collecting Information

Gather all of the information you need, whether it comes from public, paid, or internal sources. The machine learning data sets must apply to the task at hand and be presented in a well-structured way. The higher quality of your information is, the more precisely your model functions.

2) Data Cleaning

The data you use must be free of errors, duplicates, and irrelevant values. This step ensures your model has all the reliable information needed to start learning. It is mandatory to format the data set as per requirements, and deal with missing values, too.

Additionally, cleaning your data can also help to reduce the likelihood of overfitting, which occurs when a model is overly tailored to the training data and performs poorly on new data.

3) Feature Engineering/Selection

The raw data is composed of observations, which are typically represented as rows in a spreadsheet or database, using tabular data. These attributes tell us something important or useful about the observation. 

When constructing a machine learning model, the labels are referred to as features. The features you select significantly affect the performance of your model.

Data preparation for machine learning is essential, but not an easy job at all! It is time-consuming and costly. However, if you need any service to save time and money in AI data collection, Data enrichment, Data entry, Data summarization, Data processing & formatting, Wordspath is here for help. We support any data types, including text, image, audio, video and the type you name it.

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#2 Design a Model

The model is the mathematical description of the problem you are trying to solve. Depending on your specific tasks, you can choose from popular algorithms, such as logistic regression, neural networks, support vector machines (SVM), or others.

#3 Add the Selected Data

Insert your training data set. As we have highlighted above, pre-processing your information  is a must. It has to be free from outliers and noise. Otherwise, it can affect your overall accuracy on any task or problem. 

Outliers are observations that lie far away from most of the data points and can skew the results of your model. On the other hand, noise refers to random fluctuations in the data that can obscure the underlying patterns and relationships.

#4 Tune the Hyperparameters

Let us explain the difference between parameters and hyperparameters. In brief, parameters are usually not set manually, and “a model learns them on its own.” On the other hand, hyperparameters are “parameters that are explicitly defined by the user to control the learning process.”

Finding the best hyperparameters for any given machine learning algorithm is known as hyperparameter tuning. This makes your models work better in terms of accuracy and speed compared to other techniques without tuning.

#5 Train Your ML Model

In this step, you train an algorithm with labeled data to identify patterns between different variables in your data set. By fine-tuning the model after each epoch, the validation split helps the model perform better.

After the process is complete, you should have an accurate model ready for production use. 

#6 Assess the Model’s Accuracy

Now that you have built your model, you should measure how good the model is at making predictions or informed decisions.

Metrics are a great way to measure your progress; metrics like mean squared error, precision score, and logarithmic loss can all help you analyze outcomes and determine where improvements need to be made.

If the model’s performance is not satisfactory, you may have to select a different algorithm.

#7 Test the Model

Once your model is trained, you can test it by running it against unseen data (the test data set).

You should also review the training documentation as part of the testing. That ensures the methodology is suitable for the organization and that the data used meets the needs of end users.

#8 Deploy

Integrate the model you selected into dashboards, applications, or any other software you require. This is particularly useful for visualizing your model’s outcomes in a clear and concise manner.

#9 Monitor and Improve

You must regularly evaluate and update your model with new training data. There might be issues with the model or how it works with the rest of your application. Based on the results of the routine testing, you should adjust the model parameters and hyperparameters. 

What Is the Next Step?

Whether you are just getting started or want to deepen your understanding of machine learning steps, our guide can help improve your skills and equip you with the general knowledge you need to succeed at machine learning. 

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