Machine Learning Algorithms

Machine learning is one of the most popular areas in artificial intelligence. Machine learning is just a set of techniques used to build systems that learn through data, utilizing a variety of machine learning algorithms. Many of these algorithms have their roots in statistics. It's important to note that each algorithm functions as a specific tool.

For instance, A credit card company come up with customer promotions. They started out using supervised machine learning to classify the customers into two different groups. (using Binary classification)

Customers were classified into two groups:

1. Those who used promotions

2. Those who did not use promotions.

The company applied unsupervised machine learning to segment customers who utilized promotions, allowing the system to autonomously create clusters of these specific customers. It's important to note that unsupervised machine learning enables the system to form its own clusters by identifying patterns within the data.

The company discovered that among the customers who utilized promotions, there was a distinct subset that consistently took advantage of these offers. These individuals have been categorized as 'promotion super users.'.

So, the company tweak the algorithm to offer more promotions to these 'promotion super users'. that small change helped boost their overall success rate.

Now this organization used both supervised and unsupervised machine learning, some algorithms work best with supervised classifying and other algorithms that work best with unsupervised clustering.

Find the right algorithm for each task, each one has its own strengths and weaknesses. some take up more processing power while others are lighter and less accurate.

Each algorithm is primarily used for either supervised or unsupervised learning, but there are algorithms that can be used for both. These machine learning algorithms are available in most of the machine learning software toolkits