In its most basic form, machine learning uses programmed algorithms that **receive and analyze** input data to predict the output values within an acceptable range.

As **new data** is **fed** into these algorithms, they learn and optimize their operations to improve performance, developing “intelligence” over time.

## The three groups of machine learning algorithms

Once you have understood what machine learning is , you should know the three types of machine learningalgorithms that exist: supervised, unsupervised and reinforcement.

### 1. Supervised learning

In supervised learning, the machine is taught by example. In this way, the operator provides the machine learning algorithm with a known dataset that includes the desired inputs and outputs, and the algorithm must find a method to determine how to get to those inputs and outputs.

As long as the operator knows the correct answers to the problem, the algorithm identifies patterns in the data, learns from the observations, and makes predictions. The algorithm makes predictions and is corrected by the operator, and this process continues until the algorithm reaches a high level of precision and performance.

### 2. Unsupervised learning

Here, the machine learning algorithm studies the data to identify patterns. There is no answer key or human operator to provide instruction. Instead, the machine determines correlations and relationships by analyzing the available data.

In an unsupervised learning process, the machine learning algorithm is left to interpret large data sets and steer that data accordingly. Thus, the algorithm tries to organize that data in some way to describe its structure. This could mean the need to group your data into groups or organize it in a way that makes it look more organized.

As you evaluate more data, your ability to make decisions about it gradually improves and becomes more refined.

### 3. Reinforcement learning

Reinforcement learning focuses on regulated learning processes, in which machine learning algorithms are provided with a set of actions, parameters, and end values.

When defining the rules, the machine learning algorithm tries to explore different options and possibilities, monitoring and evaluating each result to determine which is optimal.

Consequently, this system teaches the machine through trial and error. Learn from past experiences and begin to adapt your approach in response to the situation to achieve the best possible outcome.

## 7 types of machine learning algorithms

What are the most common and popular machine learning algorithms?

### 1. Regression algorithms

In regression tasks, the machine learning program must estimate and understand the relationships between variables. Regression analysis focuses on one dependent variable and a number of other changing variables, making it particularly useful for prediction and forecasting.

### 2. Bayesian algorithms

This type of classification algorithms are based on Bayes’ theorem and classify each value as independent of any other. Which allows predicting a class or category based on a given set of characteristics, using probability.

Despite its simplicity, the classifier works surprisingly well and is used often because it outperforms more sophisticated classification methods.

### 3. Clustering algorithms

They are used in unsupervised learning, and are used to **categorize unlabeled data** , that is, data without defined categories or groups.

The algorithm works by searching for groups within the data, with the number of groups represented by the variable K. It then works iteratively to assign each data point to one of the K groups based on the characteristics provided.

### 4. Decision tree algorithms

A decision tree is a tree structure similar to a flow chart that uses a branching method to illustrate each possible outcome of a decision. Each node within the tree represents a test on a specific variable, and each branch is the result of that test.

### 5. Neural network algorithms

An **artificial neural network (ANN)** comprises units arranged in a series of layers, each of which connects to adjacent layers. RNAs are inspired by biological systems, such as the brain, and how they process information.

Therefore, they are essentially a large number of interconnected processing elements, working in unison to solve specific problems.

They also learn by example and experience, and are extremely useful for modeling non-linear relationships in high-dimensional data, or where the relationship between the input variables is difficult to understand.

### 6. Dimension reduction algorithms

Dimension reduction reduces the number of variables that are considered to find the exact information required.

### 7. Deep Learning Algorithms

Deep learning algorithms run data through multiple layers of neural network algorithms, which pass a simplified representation of the data to the next layer.

Most work well on data sets that have up to a few hundred characteristics or columns. However, an unstructured data set, such as an image, has such a large number of characteristics that this process becomes **cumbersome** or completely unworkable.

Deep learning algorithms progressively learn more about the image as it passes through each neural network layer. The first layers learn to detect low-level features such as edges, and the subsequent layers combine the characteristics of the previous layers into a holistic representation.

**Ultimately** , it is easy to understand the enormous effects this can have on the economy and life in general. Automation in the workplace is causing changes that seem to be endless.