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Machine Learning Algorithms List and Cheatsheet

Before in one of our earlier posts, we covered the difference between machine learning and it's “parent” category artificial intelligence as we noticed how many sources use the terms interchangeably. Going forward with the revolutionary technology and key aspects of it, today we will go deep into what makes machine learning development tick and talk about algorithms. Want to know more about nuts and bolts of ML algorithms?

If you have a quick look at machine learning concept, you could see that it uses algorithms that were programmed to process the input data fed to them at the start. The main idea behind the process is to obtain a result that would fall into a predefined range. With learning process iterations, the machines learning development “smarter” with each stage since they learn over time and refine the way they operate.


Different Kinds of Machine Learning Algorithms

Depending on the tasks you have to solve in your project, there’s a need to choose among several types of algorithms. Do you want to learn how to distinguish between those?

Supervised learning

To start off, if you consider this type of algorithm in machine learning development, it’s a situation when a computer learns from a certain example. In other words, you have a dataset with preferred input and output. The challenge for the machine to tackle is to come up with the way to connect the dots and figure out how to get from point A to point B.

Although an operator is fully aware of the solution, he lets the machine to “critically reason” on its own and come to a conclusion without any interruption from outside. The whole learning process drills down to an algorithm making predictions that are fine-tuned by the operator until the algorithm gets to a certain degree of performance accuracy.

Popular algorithms: Naïve Bayes Classifier, Support Vector Machine, Linear Regression, Logistic Regression, Decision Trees, Random Forests, Nearest Neighbours.

Semi-supervised learning

Then, semi-supervised learning might resemble supervised learning at first glance and therefore, not always you can see it as a separate category. However, the “food for thought” in machine learning development of this kind though deals both labeled and unlabeled data. On the one hand, you have labeled data that has a meaningful tag as an attribute e.g. among the driving school attendees list you can see:
Column 1: males and females;
Column 2: age of every attendee;
Column 3: test passed / failed.

Another list with unlabeled data might include a) and b) while missing the latter category c) – a tag indicating whether or not a specific attendee passed the test. By putting it all for processing, an algorithm can be taught how to label unlabelled data lacking a certain tag.

Popular algorithms: while the tasks solved by semi-supervised machine learning are mostly concerned classification and regression problems, the algorithms in use are the adaption of methods that can accommodate the training model that includes unlabelled data along with labeled sets.

Unsupervised learning

To top it off, another type of machine learning development can study certain data in order to define the patterns. It’s a sole journey that the machine undertakes to spot the patterns and the ways the data is arranged into.
At such an altitude, there is basically no one to guide the machine through the learning process, read “no hand-holding” at all. The challenge for the machine Is to work on its own to come up with a possible grouping of large data sets so that this data would make sense when arranged.

Popular algorithms: K Means Clustering

Reinforcement learning

Finally, reinforcement learning is based on the notion of an algorithm defining the rules and looking into different result to determine the most suitable one. Furthermore, reinforcement learning has at its core trial and error concept allowing for greater freedom for a machine to learn on its own mistakes.

So reinforcement learning in machine learning development is about figuring out the best case scenario based on the greatest reward. Think about it as a carrot and stick methodology applied to a technological landscape where an algorithm gets rewards by acting correctly and penalties for doing it wrong.

Popular algorithms: Artificial Neural Networks.


What machine learning algorithms are to choose from?

By now you’ll be wondering how to choose the right algorithm for your project. Indeed, it’s not easy if you don’t know which parameters to take into account. We suggest thinking of the following when picking the right type for you: size of the data, its quality and how distinct it should be, and the particular business case that needs an answer and tries to find it with the help of machine learning development techniques. Additional information to bear in mind is how much time allotted, certain criteria etc.

Lower you can check ML CheatSheet that can help you to determine the machine learning algorithm that will work for you the best depending on the data and goals you have.
There are listed the most popular algorithms that usually works in most situations without any mention of reinforcement learning or semi-supervised learning because you need these ML technics for very specific situations and in such case, you really need a help of professional ML developers.


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