# Machine Learning Algorithms: Everything You Need To Know

If you’re familiar with artificial intelligence or you’re interested in data science, you’ve probably heard about machine learning algorithms. The first algorithms of this kind were developed at the end of the 20th century, but the term itself has drawn a lot of attention only over the last few years. This can be explained by the increasing popularity of neural networks and AI, which would not exist without machine learning algorithms.

However, there is a lot more to machine learning, which can be used in a countless number of cases. You definitely need to pay attention to them if you want your project to succeed. In this article, we will answer fundamental questions about machine learning, like:

• What is machine learning?
• How can you use machine learning algorithms for business applications?
• What are the different types of these algorithms?
• How can you choose the machine learning algorithm that works best for you?
• What is the best way to implement this technology?

## How Can We Define Machine Learning?

Machine learning algorithms enable a computer to predict or pick the best data output to match the input data.

Right now, there are tens, if not hundreds, of different algorithms that enable this process. So it’s no wonder that it is very difficult to choose the best machine learning algorithms for your particular case. It is even more complex than you think, as there is no way to foresee whether the method is good enough – you would need to try it using a test data sample.

The applications of machine learning vary a lot. You can notice lots of them in your day-to-day life. Whether you’re using a product from a tech giant like Google or the services of a smaller online shop, your behavior is probably being tracked and analyzed by a machine learning program. The reason is simple – to enhance your user experience. So, the main reason for an app to use machine learning is to make the customer’s decision-making process simpler by finding patterns in the input datasets.

## Machine Learning Algorithms’ Categories

All machine learning algorithms can be divided into three main groups. The distinction is based on the amount of control a developer has over the process and what results are expected after their execution.

### 1. Supervised algorithms

Most supervised algorithms have a target outcome they should achieve with a certain level of precision. By analyzing test data points, the algorithm determines the number of ways to reach the outcome as many times as needed until the result is close enough to the target.

### 2. Unsupervised algorithms

These algorithms don’t have any target they need to reach. However, the datasets they work with can be described by a number of parameters with a set of values that differ between data points. The task these algorithms face is usually to group data based on the parameter values in the fastest and the most accurate way possible.

### 3. Reinforcement learning algorithms

Reinforcement learning algorithms are also known as trial and error. They make a series of decisions and learn about each one’s effectiveness and validity based on the results and their previous experience.

Now, let’s take a look at some of the most popular and competent machine learning algorithms out there.

## Top 10 Algorithms For Machine Learning

### 1. Decision Tree

This method is very easy to learn and implement, so it is one of the best and most popular choices for a beginner. The decision tree is a supervised algorithm used to classify the data based on a number of different parameters. Each leaf node represents a prediction on one parameter, and the tree branches out until it gets to the outcome.

This method is effective for a number of different tasks. Plus, you don’t need to make any changes in the dataset in order to use it.

### 2. Random Forest

This example of a machine learning algorithm uses a number of Decision Trees, earning it the name “Random Forest.” To simplify, each tree learns on a different test dataset. Then, the new dataset is analyzed by each tree. The outcome from each tree is then used to determine the final value, thus improving the accuracy of the final result.

Unfortunately, the Random Forest algorithm requires more resources than the individual tree. However, it is much more precise, and is therefore more popular at the moment. Image source: Medium

### 3. Linear Regression

This is one of the simplest learning algorithms, both in terms of understanding and implementing it.

Linear Regression is a supervised algorithm that determines the connection between the input data and the expected result. It uses this connection to predict the outcome for other input data points.

This method fits the data points using the simple linear function y = a*x+b. This equation is where the algorithm’s name comes from. However, you can actually use a combination of linear functions to improve your results. ### 4. Logistic Regression

This method is similar to the previous one as it also simplifies the connection between the input data and the output. However, it uses a different function, and it is meant for another class of assignments.

Logistic Regression is used for the tasks that require classification with only two possible outcomes (0 or 1 – basically, binary classes). It is implemented by the logarithmic function in order to determine the outcomes for each data point. Image source: Towards Data Science

### 5. K Nearest Neighbors

The K Nearest Neighbors (KNN) algorithm is rather simple, but it is nonetheless very effective and widely used. Its main disadvantage is that it needs the whole dataset in order to work.

It is based on analyzing the K points that are the most similar to a new input. The majority of the neighbors determine the class the new data point is assigned to. The main difficulty lies in defining the distance between the points – it varies a lot based on the parameter you’re analyzing. ### 6. K Means

This is an unsupervised algorithm that is used to determine the classes in a dataset that consists of several clusters. First, you need to choose the number of points for the center of a cluster (K). The densest groups of K points are chosen to be the center. Then each new point is grouped with the closest cluster center. Image source: Towards Data Science

### 7. Naive Bayes Algorithm

Although many of the algorithms mentioned above are connected with statistics in some way, this one is the most statistical in nature. This algorithm applies the Bayes’ theorem to determine the odds of a data point being in a specific class, based on the calculated probability for other test data points. It can be used for classifications based on different parameters, but only if these parameters are not interdependent.

### 8. Support Vector Machine

SVM is somewhat similar to the Logistic Regression algorithm because it is also used for binary classification. However, it is much more accurate and sophisticated.

In order to separate two classes, this algorithm sets up a surface. Its shape allows the points closest to it to have the longest distance possible. These points are known as support vectors. The classes for new data points are determined based on which side of the surface they are on. ### 9. Artificial Neural Networks

As its name suggests, this algorithm is based on the structure of our brain. This algorithm uses a number of separate processors that work on the same task at the same time. Artificial Neural Networks can be considered reinforcement learning algorithms, so the whole network is going through the trial-and-error learning path. Image source: Wikipedia

### 10. Dimensionality Reduction

This is a supporting algorithm for most of the machine learning algorithms described above. Its main task is to determine the parameters that impact the outcome the most. Too much information can be overwhelming, so the dimensionality reduction algorithm helps you streamline the process and remove redundancies. This algorithm saves a lot of time and resources, so don’t forget to use it before you start analyzing your dataset. Image source: ResearchGate

## How To Choose The Algorithm And The Programming Language

Choosing the right algorithm is one of the most important and difficult tasks in this line of work. There are a lot of different parameters that can be ascribed to each project, and each parameter can impact your algorithm choice.

The simplest way for you to determine the perfect algorithm for your app is to try some of them out by using a test array of data.

The last major question we haven’t answered yet is which programming language is the most suitable for implementing machine learning algorithms. It doesn’t really matter – if you know how to do your job, the tools don’t make a big difference. However, you should know that the most common languages used in data science are Python and R.

## Conclusion

Machine learning algorithms are a multifunctional tool. With their help, you can make many tasks a lot simpler. Furthermore, these algorithms could help you determine the outcomes of all the possible choices and save you from making the wrong decision.

All in all, whether you believe machine learning is the future or you’re afraid of an AI uprising, you should agree that machine learning is already changing the world. So, if you don’t want your project to be outdated, contact us to start implementing this technology right now!