What Machine Learning Can Do: A Complete Guide To Learn What It Is And How It Works
Tags: machinelearning, beginners
Published: 2024-04-30 11:23:38

**_If you want to learn more about what machine learning can do, how machine learning algorithms work, and how this method of data analysis can be used to improve our jobs and personal lives, this article is a must!_**

## What is machine learning?

Machine learning is a method of data analysis and a branch of artificial intelligence that focuses on algorithms and data usage to enable electronic devices to learn from their experiences, such as humans do, and gradually improve their accuracy without the involvement of any type of coding. 

Machine learning helps make the devices or software applications work accurately when it comes to predicting specific outcomes without any need for programming. Therefore, machines can learn automatically without a developer’s involvement or assistance.


## What Can Machine Learning Do?

Machine learning allows users to store the computer algorithm so that computers can analyze and interpret the data efficiently. Unlabeled and unorganized data can be arranged into patterns with the help of machine learning. Routine tasks can also be automated with the help of this method of data analysis. 


## Why Machine Learning Matters?

Machines can learn without any involvement in coding, so developers don’t have to spend time commanding the devices and operating them. How cool is that?

For instance, if you want data of a thousand people to be arranged by their name, you just have to feed an algorithm along with the unarranged data. The computer will arrange this data by itself. If you want to analyze this data and you want recommendations regarding it, the computer can perform this task for you – if it is enriched with machine learning. 

If any improvement in the data is required, then the computer will suggest some solutions to improve your data so that it can be used in the future efficiently.


## How can Machine Learning be Applied?


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/3npoknz9u34l4lqhe4mm.png)

Following are some examples of what machine learning can do and where can it be applied.


### Customer Service

You can use machine learning in business to provide services to customers, such as answering their questions and providing them with information regarding a specific product or service.


### Recognition Devices

Machine learning is being used in recognition devices as it makes most computers able to recognize texts, images, faces, videos, etc. Companies can also use it to detect the identity of people. Most smartphones have the feature of thumb or face recognition lock: the mobile phone recognizes the thumbprint or face of the specified person to whom the phone belongs. You can also implement this feature in your computer using machine learning.


### Automated Stock Trading

Machine learning is widely being used for automated stock trading as it has enabled the devices to optimize the portfolios of the stock. Computers can make millions of trades in a day with the help of machine learning.


## How does machine learning work?

You can divide the working of machine learning into three simple parts:


### Base Knowledge

It can be defined as the knowledge that is provided to the system at the initial phase. It helps the system or software to answer the specified questions. It enables the system to learn automatically while it trains the system.


### Features Variables

Feature variables are measurable properties of the objects under study. In a dataset, they are used to store values. Examples of it are name, sex, age, etc.

They can also be referred to simply as features, variables, or attributes.

Features are very important: the quality of it has a major impact on the quality of the insights you will obtain when you use that dataset for machine learning.


### Computational Algorithm

You can learn how machine learning algorithms work by understanding the computational algorithm and its working.

Basically, computational methods are used by machine learning algorithms so that the information can be extracted and learned from the data instead of depending upon the predetermined equation. The performance of the algorithms is improved with the increase in the number of samples. The output variable can be predicted with the help of machine learning algorithms if the input variables are given.


## What types of machine learning exist?


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/12gx506wofyvo63h53j5.png)

The types of machine learning have been enlisted below:


### Unsupervised Learning

Machine learning algorithms are used by unsupervised machine learning, which is helpful in analyzing and clustering unlabeled sets of data. You can also discover hidden patterns with the help of unsupervised learning. 

Your computer can discover the differences and similarities in the information provided to it without your intervention. It can be helpful for making your computer capable of performing different tasks such as pattern recognition, image recognition, customer segmentation, data analysis, etc.


### Reinforcement Learning

The observational method is used by the machines to get familiarized with the environment and take action accordingly. No labeled materials are used by the machines in reinforcement machine learning. It is a type of machine learning that results in minimizing the rate of the risks and maximizing the rate of rewards. It makes the machine capable of telling you which step is wrong and which step is correct.


### Supervised Learning

Supervised machine learning involves the usage of labeled datasets which play a vital role in training the algorithms. These algorithms are helpful for your computer in predicting the results or classifying the data accurately and reliably. 

It is a model which avoids underfitting or overfitting. This type of machine learning is helpful in solving different types of real-world problems. The methods that you can use in this type of machine learning include SVM, random forest, logistic regression, linear regression, etc. 


### Semi-supervised Learning

A smaller labeled set of data is involved in semi-supervised learning, which is helpful in providing your device with guidance regarding classification. It makes your computer capable of extracting specific data from unlabeled data. Different problems can be solved by semi-supervised machine learning even if the data has not been labeled. 


## What are the challenges associated with machine learning?

There are several challenges associated with machine learning which could be explained with the help of examples.

For instance, machine learning has made cars autonomous as they could be driven without the need of any driver. But it is unrealistic to think that a car with no driver could go around without any risk for humans – for now – as there are situations that developers weren’t able to teach machines about yet, and so it can put passengers and pedestrians in danger.

Machine learning has a great impact on people’s jobs. It is so because the devices based on machine learning have been replacing the need for humans. Most of the operations in the industries are being performed automatically by computers or other smart devices, which have resulted in reducing the need for human labor. 

Machine learning has also affected the privacy of data as well as its security. It is so because the access and the control of our personal data has been provided to specified organizations. Although there are mechanisms to protect personal data from being used when unauthorized, such as GDPR, there’s always a chance of it getting leaked or hacked. 

Finally, are you aware of bias in machine learning? Computational Biologist and Data Scientist Susana Paço wrote an article about the subject for our blog, take a look.


## Deep Learning vs Machine Learning

Machine learning, neural networks, and deep learning are all fields of artificial intelligence and are generally termed the same. However, there is a slight difference between machine learning and deep learning as machine learning is a subfield of AI while deep learning is the sub-field of machine learning.


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/mrv71rn33vsfwgklzzaq.png)


The major difference between deep learning and machine learning is the algorithm used. Deep learning can also be known as scalable machine learning. If we compare deep learning with machine learning, machine learning is more dependent on humans for the process of learning.


## What machine learning can do: final thoughts

Machine learning has been playing a vital role in this modern era, as it has made several advancements in different fields such as banks, offices, the medical sector, the educational sector, etc.

However, the machine learning revolution has just begun.

From the moment machines can learn from experience anything is possible: autonomous factory workers, smart cities, robot doctors – that’s not so far ahead in the future thanks to machine learning.

Of course, with great power comes great responsibility.

The risks of using machine learning should be a source of concern and debate among the tech community.

…we wouldn’t like our future to look like a Black Mirror episode, thank you!

Now, if machine learning and artificial intelligence fascinate you and you see yourself being a part of this revolution, get in touch and we’ll present you with the best opportunities in this field.

Let's connect on social media, [follow us on X](https://twitter.com/KwanCommunity)!

Article written by Lúcia Henriques, and originally published at https://kwan.com/blog/what-machine-learning-can-do-a-complete-guide-to-learn-what-it-is-how-it-works/ on August 26, 2021.

See you in the next article!