Machine Learning Algorithm vs Model

what’s difference between ML Algorithm and ML Model?

Machine Learning is branch of ARTIFICIAL INTELLIGENCE. It is an important element of developing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering the key insights within data mining projects.

Source : Google images

ML algorithm and ML models are both important parts of machine learning system. It is really confusing and beginners often use these terms inter-changeably, but they are definitely not same.

An algorithm is created which a computer later uses to find a model that best fits the data, and make an accurate prediction based on that.

let’s learn how is ML algorithm is different from ML model in detail.

source : Google images

What is Machine Learning Algorithm?

we provide a machine with an algorithm, a list of rules and descriptions that allows the machine to learn on its own. It’s a trial and error process where each trail is a learning step for a machine and improve itself.

1. It is a procedure that runs on data to create a ML “model”.
2. It is a set of rules that we follow to solve a problem.
3. It is used to train the model for a set of data.
4. It performs “pattern recognition” and learn from the data and try to fit the model on the datasets.

There are so many different Machine Learning algorithms,

• Linear Regression
• Logistic Regression
• Decision Tree
• SVM
• Naive Bayes
• kNN
• K-Means
• Random Forest

ML algorithm can eventually represent a ML model i.e., ML Algorithm = Ml Model.

What is Machine Learning Model?

1. A model is what we build by using an algorithm.
2. It is the output of ML algorithm which runs on data and represents what is learned by the ML algorithm.
3. When an algorithm is trained with some datasets it will become a “model”

ML Model = Model Data + Prediction Algorithm.

Source : Google images

we provide a model with a huge amount of data to train the model. As we provide the large datasets we are training the model for different scenarios, machine will learn how to tackle all these situations on its own over the trial and error process.

Unlike algorithms we do not provide a list of instructions or rules to train the model instead we give the final goal the model should achieve. To achieve this goal the model has be trained for many number of trials, over these trials we fine tune the hyper-parameters to achieve the best fit of the model for the given data set, we may call it objective function a measure of inaccuracy of the model. We use the optimization algorithms to improve the performance of the model.

Reference

1. https://www.ibm.com/cloud/learn/machine-learning
2. https://www.udemy.com/course/the-data-science-course-complete-data-science-bootcamp