Machine learning algorithms sound very complex to the average person. To be fair, programmers are the ones who usually use them.

What is Machine Learning?

Machine learning is the way artificial intelligence does the things it does. It provides AI ways to learn and improve from experience without constantly being programmed.

Think of how you turn your Rumba on and it goes around your house on its own. When it bumps into walls, it turns away without you having to do it yourself.

These algorithms focus on the development of computer programs and can access data and use it to learn for themselves. They do this through observation. This could be direct experience, instructions, or looking for patterns in data.

The goals are for computers to learn automatically without human assistance and adjust to the needs of the program. One of many machine learning examples is the self-driving car.

Types of Machine Learning

There are different kinds of machine learning algorithms.

1. Supervised Machine Learning Algorithms

This type can apply what it’s learned in the past to new data using labeled examples to predict future events. It analyzes a known training set and can make predictions from that.

It can also compare its output to the answers from the training. This allows it to correct its answers.

2. Unsupervised Machine Learning Algorithms

This is the opposite of supervised machine learning algorithms. With this algorithm, the training is neither classified nor labeled. This is all about how much it can infer from the data it’s given.

The system doesn’t figure out the right output right away. Instead, it explores the data and draws inferences from datasets to see the hidden structures from the unlabeled data.

3. Semi-supervised Machine Learning Algorithms

This one falls in between the previous two. It uses both labeled and unlabeled data for training. It’s usually a small amount of labeled data and a large amount of unlabeled data.

It seems that systems that use this form are able to improve faster and their accuracy.

4. Reinforcement Machine Learning Algorithms

This is a learning method that interacts with its environment. It does this by taking action and learning errors or rewards.

Basically, it’s trial and error.

This method allows machines and software agents to automatically determine the ideal behavior in a specific scenario.

This method also needs to be rewarded to know that it’s doing a good job.

5. Random Forest

Decision trees are used to direct graphs to model decision making. Each node on the graph represents a question and the branches from each node represent possible answers.

Creating many of these trees is an ensemble method and called a random forest. This can be a highly efficient method, though some people think it’s too difficult to understand.

6. Neural Networks

This method’s goal is to mimic the way our brains organize and understand information. In an artificial network, info is passed through an input layer, a hidden layer, and an output layer.

The input and output layer can be made of raw features and predictions. The hidden layer is made of many highly interconnected neurons capable of complex meta-feature engineering.

This method excels in more humanistic endeavors like speech and object recognition.

7. Logistic Regression

This method is borrowed from the field of classical statistics. It’s one of the simpler algorithms.

It’s usually used for binary classification problems. This means the problem has two possible outcomes that are influenced by one or more variables. The algorithm guesses the probability of a certain outcome from what it observed.

This is usually used for customer churn, marketing click throughs, or fraud detection.

8. Kernel Methods

This method is a group of algorithms that analyze patterns. This means organizing data into rankings, clusters, or classifications.

This allows data scientists to apply their knowledge to a problem by building custom kernels that will improve the overall accuracy.

The most popular version of this method is the support vector machine (SVM) which builds a model that classifies new data as belonging to one category or another based on training examples.

Machine Learning Vs Artificial Intelligence

These two are often mistaken as the same thing but they are different. AI is the broader idea of a machine being able to do things in a way we consider smart. Consider it the body that can move.

While machine learning is the current way of making an AI do and learn things. Consider this the mind that controls the body.

Still Hard to Understand?

Don’t feel bad if you are still a little unsure about what machine learning algorithms are. These are complex programs and ideas.

Each person has a way of explaining them. Try reading other articles and soon the pieces should fall into place like a puzzle.