Machine Learning Modelling Flow – Part 1 | Step-by-Step Guide to ML Models


Machine Learning is one of the most exciting and powerful tools in the tech world today. From voice assistants to movie recommendations, ML is used in many things we use daily. But how exactly does machine learning work? And how do you build a machine learning model?

In this article (Part 1 of our ML Modelling Flow series), we will walk you through the basic steps to create a machine learning model. Whether you’re a student, working professional, or just curious about ML, this guide will help you understand the process in a simple way.

Step 1: Understand the Problem

Before doing anything else, you need to know what problem you are trying to solve. For example, do you want to predict house prices or identify if an email is spam? Knowing your goal helps you choose the right kind of machine learning model.

Step 2: Collect Data

Machine Learning models learn from data. You need to collect data that is related to the problem. You can find data from websites, company records, or public sources. The more useful data you have, the better your model will be.

Step 3: Explore the Data

Once you have the data, take time to understand it. Look for:

  • Missing information

  • Unusual values

  • Patterns or trends

You can use simple graphs and charts to make this step easier. This step helps you prepare the data properly.

Step 4: Clean the Data

Real-world data is often messy. You need to:

  • Fill in or remove missing values

  • Convert text into numbers (like turning "Yes" or "No" into 1 or 0)

  • Make sure all values are in the same format

Clean data helps the model learn better and give accurate results.

Step 5: Choose a Model

There are different types of machine learning models. The model you choose depends on the problem:

  • Use a classification model to sort things into groups (e.g., spam vs. not spam)

  • Use a regression model to predict numbers (e.g., sales next month)

Step 6: Train the Model

Training means teaching the model using the data. It finds patterns in the data and learns how to make predictions. This is done using special software or programming languages like Python.

Step 7: Test the Model

Once the model is trained, you test it using new data it hasn’t seen before. This shows how well it can make correct predictions. If it doesn’t do well, you may need to improve your data or try a different model.

Want to Learn Machine Learning the Right Way?

If you are serious about learning ML and building a career in it, you need proper training. Choosing the best machine learning course will help you gain real skills that companies want.

One highly recommended option is the Machine Learning and Artificial Intelligence course by Imarticus Learning.

Why Choose Imarticus Learning?

  • Learn from expert instructors

  • Get hands-on training with real projects

  • Learn tools used in top companies

  • Get career support and placement help

  • Flexible learning – live classes + self-paced videos

Conclusion

In this first part of the Machine Learning Modelling Flow, we covered the basic steps to build an ML model—from understanding the problem to testing your model. These steps are important for anyone who wants to start a journey in machine learning.

In Part 2, we will explore advanced topics like improving model performance and deploying it in real life.

If you’re looking to get started with the best machine learning course, Imarticus Learning offers a complete program that prepares you for real jobs in this fast-growing field.



 

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