Machine learning (ML) models is the most commonly used in a data science project. In this post, you will learn about different definitions of a **machine learning model** to get a better understanding of what are machine learning models?

- A model is the relationship between features and the label. (Tensorflow – Getting Started for ML Beginners)
- An ML model is a mathematical model that generates predictions by finding patterns in your data. (AWS ML Models)
- ML Models generate predictions using the patterns extracted from the input data (Amazon Machine learning – Key concepts)
- Learning in the supervised model entails creating a function that can be trained by using a training data set, then applied to unseen data to meet some predictive performance. (IBM Cloud – Models for Machine Learning)
- In neural networks, the model consists of layers of neurons interconnected through weights that alter the importance of certain inputs over others. (BM Cloud – Models for Machine Learning])
- An algorithm is the general approach you will take. The model is what you get when you run the algorithm over your training data and what you use to make predictions on new data. You can generate a new model with the same algorithm with different data , or a different model from the same data with a different algorithm. (What is the difference between machine learning model and ML algorithm?)
- The “ML model” is the output generated when you train your “machine learning algorithm” with your training data-set. (What is the difference between machine learning model and ML algorithm?)
- The hypothesis set and the learning problem, together, can be referred to as “learning model”. (The Learning Problem).This is as shown in the diagram below. One needs to get a clear understanding on terminologies such as hypothesis, hypothesis class or set, learning algorithm. Note that its the learning algorithm and the optimization methods which result in the final hypothesis out of different hypothesis which are available in hypothesis space.
- The process of training an ML model involves providing an ML algorithm (that is, the learning algorithm) with training data to learn from. The term ML model refers to the model artifact that is created by the training process. (Training ML Models)

You may want to check out this post to learn the machine learning terminologies.

### Summary

In this post, you learned about different definitions of **machine learning model**. Did you find this article useful? Do you have any questions or suggestions about this article in relation to **understanding what are machine learning (ML) models**? Leave a comment and ask your questions and I shall do my best to address your queries.

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I found it very helpful. However the differences are not too understandable for me