An Overview of Machine Learning Models: A Comprehensive Introduction

Machine Learning Models:

A machine learning model is a program that can find patterns or make decisions from a previously unseen dataset.

In natural language processing, machine learning models can parse and correctly recognize the intent behind previously unheard sentences or combinations of words.

?In image recognition, a machine learning model can be taught to recognize objects - such as cars or dogs.

A machine learning model can perform such tasks by having it 'trained' with a large dataset. During training, the machine learning algorithm is optimized to find certain patterns or outputs from the dataset, depending on the task.

The output of this process - often a computer program with specific rules and data structures - is called a machine learning model.

What is a machine learning Algorithm?

A machine learning algorithm is a mathematical method to find patterns in a set of data.

Machine Learning algorithms are often drawn from statistics, calculus, and linear algebra.

Some popular examples of machine learning algorithms include linear regression, decision trees, random forest, and XGBoost.

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What are the different machine learning models?

There are many machine learning models, and almost all of them are based on certain machine learning algorithms. Popular classification and regression algorithms fall under supervised machine learning, and clustering algorithms are generally deployed in unsupervised machine learning scenarios.

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Supervised Machine Learning

1. Logistic Regression: Logistic Regression is used to determine if an input belongs to a certain group or not.
2. SVM: SVM, or Support Vector Machines create coordinates for each object in an n-dimensional space and uses a hyperplane to group objects by common features.
3. Decision Trees: Decision trees are also classifiers that are used to determine what category an input falls into by traversing the leafs and nodes of a tree.
4. Linear Regression: Linear regression is used to identify relationships between the variable of interest and the inputs, and predict its values based on the values of the input variables.
5. Random Forest: Random forest is a collection of many decision trees from random subsets of the data, resulting in a combination of trees that may be more accurate in prediction than a single decision tree.
6. Boosting algorithms: Boosting algorithms, such as Gradient Boosting Machine, XGBoost, and LightGBM, use ensemble learning. They combine the predictions from multiple algorithms (such as decision trees) while considering the error from the previous algorithm.

Unsupervised Machine Learning

1. K-Means: The K-Means algorithm finds similarities between objects and groups them into K different clusters.
2. Hierarchical Clustering: Hierarchical clustering builds a tree of nested clusters without having to specify the number of clusters.

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What is the best/Ideal model for machine learning?

The machine learning model most suited for a specific situation depends on the desired outcome.

For example, to predict the number of vehicles purchases in a city from historical data, a supervised learning technique such as linear regression might be most useful.

?On the other hand, to identify if a potential customer in that city would purchase a vehicle, given their income and commuting history, a decision tree might work best.

What is model deployment in Machine Learning (ML)?

Model deployment is the process of making a machine learning model available for use on a target environment—for testing or production. The model is usually integrated with other applications in the environment (such as databases and UI) through APIs. Deployment is the stage after which an organization can make a return on the heavy investment made in model development.

Difference between Machine learning model and Algorithms

• an ML algorithm is like a procedure or method that runs on data to discover patterns from it and generate the model. At the same time, a machine learning model is like a computer program that generates output or makes predictions.
• More specifically, when we train an algorithm with data, it becomes a model.

How Are ML Models Built?

In business environments, the inception of a new model is almost always rooted in a problem in need of a solution, like making better-informed decisions, automating procedures, or finding patterns within vast amounts of data.

Once a solution to that problem is identified, it is translated into a business goal, such as predicting inventory shortfalls, or determining credit limits for banking customers. This can then be translated into a technical problem to be solved using ML/AI models.

Depending on the type of business problem, and the available data, an approach is determined that is best suited for the problem.

Once data is acquired, it is prepared as needed for the specific approach and may include removing unnecessary or duplicate data from the data set. The data scientist will then conduct experiments with different algorithms and compare the performance on a different set of data.

Once the performance meets the needs of the business problem, it will be ready for deployment.

Even after a model appears to be stable and is working within expected parameters, it still needs to be monitored.