Machine Learning Topic 3: Algorithms in Machine Learning: An Explanation

In this article, we will break down some common terminologies that we often come across during machine learning. First up is "algorithm." Algorithms are quite commonly confused by a lot of people. However, algorithms can do nothing but form the basis of AI and machine learning systems. Let's dive in and understand what an algorithm means and how it works in the world of machine learning.

What is an Algorithm?

More basically, an algorithm could be described as a set of rules or instructions that are designed for a particular problem or to perform some task. In machine learning, algorithms are used to make predictions or for classification decisions based on inputs received in the form of data. The instructions allow the AI system to learn from data and to produce useful outputs.

Example: Let’s take a simple example. Suppose we have a dataset with the following values:

In this case, the algorithm goes as follows: it multiplies the input value to the input value by 2.

Mathematically, it is expressed as:

y=2x

Here, y is output, and x represents input values. Upon entering any value of input x into the

algorithm, it will bring about the output y which is associated with the input value. Again, if we

assign an algorithm such that it multiplies x by 3, it can be described by the equation

y=3x

This illustrates how algorithms follow simple mathematical rules to process data.

Machine Learning Algorithms and Complexity

As machine learning models become more sophisticated, so do the algorithms used to define

them. For instance, instead of a single variable x, several variables can interaction together.

Consider the equation:

z=2x+3?4y

In this example, it is x and y variables, while the output z depends on the interaction of the two

by way of algorithm. This is the simple example when we extend more variables and

conditions; the machine learning algorithm develops.

Decision Tree: A Simple Example

For better understanding of machine learning algorithms, we now describe the Decision Tree algorithm, which is extremely widely used in classification and regression tasks.

Example:

Suppose we have the following dataset:

This algorithm will classify the gender based on the given features - hair length, height, and whether the person wears a hijab.

Decision Tree Algorithm Works:

A Decision tree splits the data regarding certain conditions and branches out based on the feature in which it decides. Here is how it might handle an example like this:

Condition 1: If the hair length is more than 1.5 feet, predict Female.

Condition 2: If the length of hair is less than 1.5 feet and the length of height is less than 150 cm then predict Female.

Condition 3: If the length of hair is less than 1.5 feet and the person is not in hijab, then predict Male.

A simple picture of this decision tree can be given as follows:

There are various types of algorithms in machine learning.

Algorithms are the backbones for machine learning models and vary with different

applications. Here is a summarized table of popular algorithms along with typical applications:

Understanding the Concept of Algorithms

At its heart, an algorithm itself constitutes what guides a machine-learning model to decide how the system learns the data. From simple algorithms such as multiplying two values together to highly advanced algorithms that establish decision boundaries in a high-dimensional space, an algorithm is at the base of any prediction or decision that the AI makes.

Important Terms to Remember:

Algorithm: A set of rules or mathematical expressions defining how data should be processed.

Model Training: The feed of data into an algorithm so that it can learn the patterns.

Prediction: The output generated by the model after training on a dataset.

Concept Map: Understanding Algorithms in Machine Learning

Below is a simplified concept map that links the key elements of machine learning algorithms:

Conclusion

An algorithm in machine learning is a set of instructions that helps an AI system learn from data. Understanding the procedure by which some algorithms, like Decision Trees or K-Nearest Neighbor, work allows for an appreciation of how AI systems make predictions, classify data, or even find hidden patterns. Algorithms are the backbone of machine learning models, and mastering their principles is key to understanding AI functionality.

Summarized, the basics of algorithms open up into more complex and advanced machine learning. Every algorithm that is known has strengths, hence suited for certain types of tasks, which means that the right algorithm has to be chosen for the desired outcomes to be achieved.