What is Machine Learning? This is the understanding and difference with AI

 

Machine learning is a technology developed to adopt human learning abilities on computers. Along with its development, machine learning has helped humans to solve complex problems more efficiently.

Try to remember. Have you ever wondered how Netflix knows what movie genres you like? Or when you search for a product on Google and suddenly an advertisement for a similar product appears on Instagram? Well, the secret behind all of that is the use of machine learning algorithms.

In this article, we will invite you to explore what machine learning is, how it works, types of machine learning, and examples of its implementation in everyday life. Already curious? Come on, let's see the article!

What is Machine Learning?

Simply put, machine learning or machine learning is a technique for educating computers to learn certain programming algorithms so they can do some tasks on their own without human direction.

The main goal of developing machine learning is to mimic human learning, where humans can learn patterns and information from past experiences to make better predictions or decisions in the future.

It's like when you teach a child to recognize ducks by showing him lots of pictures of ducks and telling him,

"This you know the name of the duck".

Now, after seeing various pictures, children will begin to understand the characteristics of ducks.

Likewise with children, after being given a lot of data about ducks, the algorithm will use it to identify the characteristics of the animal. Like a child who can finally recognize ducks without being told, machine learning algorithms will also recognize ducks automatically without human assistance.

So, with machine learning, you can create machines or computers that are able to study existing data to make their own decisions without needing to be programmed again.

The difference between Machine Learning and AI

Perhaps, you often hear these two terms referred to together. It's not uncommon for some people to think of it as the same thing. However, basically, the difference between AI and machine learning is quite clear.

AI or Artificial Intelligence is a concept that covers everything related to making machines smarter, while machine learning is a branch of knowledge from AI.

In essence, machine learning focuses on developing algorithms so they can make their own decisions based on data. Meanwhile, AI is the main rationale for creating devices that can mimic human intelligence.

How Machine Learning Works

When you want to learn something new, maybe you'll read a book or watch a tutorial video. However, you only really understand when you practice it or get feedback from others.

That's how machine learning works, too. By continuously feeding it lots of data and feedback on its performance, you can teach an algorithm to solve a problem.

But actually, how did this process happen? Here comes the explanation.

Data collection. The first step in how machine learning works is to collect data that is relevant to the problem at hand, which can also be used to train the algorithm.

Data preparation. Once collected, the data must be prepared for analysis. For example, in this step, the data will be cleaned to remove errors or inconsistencies.

Model selection. The next step is to choose the appropriate machine learning model to solve a case.

Training. The machine learning model will be trained with the prepared data. The machine will identify the pattern from the data set provided by the programmer. If the patterns have been collected, the machine can make predictions and decisions mathematically.

Evaluation. The model must also be evaluated to determine how well it is performing. Usually, evaluation is done by separating the data into training and test sets. Then, the accuracy or other performance metrics of the models in this test set are assessed.

Furthermore, the machine can be used in a production environment after being trained and evaluated through the process above.

So, until here, you already know how machine learning works. In the following sections, we'll cover the different types of machine learning that you need to know about. Scroll down, come on!

Types of Machine Learning

From the stages of the work process above, machine learning is further divided into several types. This division is based on the methods used by machine learning in learning algorithms.

So, here are the types of machine learning that are currently being developed:

1. Supervised Learning

Supervised learning is a type of machine learning where algorithms are trained using data that has been categorized or has labels.

In supervised learning, the data used to train the algorithm consists of predetermined input and output pairs. Then, the algorithm learns from the data and tries to identify the relationship between input and output.

This type of machine learning is used in many applications such as facial recognition, spam email detection, and others.

2. Semi-supervised Learning

Semi-supervised machine learning is a type of machine learning that tries to make use of data that is only partially labeled. This type of machine learning is also a combination of supervised learning and unsupervised learning.

In semi-supervised learning, the algorithm analyzes patterns in data samples that have labels. After that, the algorithm uses these patterns to predict the category of unlabeled data.

An example of implementing this type of semi-supervised machine learning is a voice recognition system.

3. Unsupervised Learning

In contrast to supervised learning, unsupervised learning is a type of machine learning to study patterns in data without any information or category labels.

Later, the algorithm will learn to recognize patterns and structures in the data independently.

An example of the application of unsupervised learning is in grouping or clustering data. For example, an algorithm could be used to group a company's customers based on their similar characteristics or behavior.

4. Reinforcement Learning

Reinforcement machine learning is a type of machine learning in which an algorithm learns from its experiences to solve specific problems.

In reinforcement learning, algorithms learn by trying certain actions and observing the responses produced by the environment. That way, the algorithm can avoid errors from the previous process.

Reinforcement learning can be likened to a game. For example, to defeat the enemy, the player controls the controller which can give instructions to the character to complete the level. Then, the game character will respond to the actions given by the player.

So, if the actions taken succeed in achieving the goal of defeating the enemy, the player will get points. But if it fails, the player will lose points.

Example of Application of Machine Learning

Without you realizing it, you have used many examples of machine learning applications in your daily life. For example, chatbots are used for customer service. Not only that, there are still many examples of machine learning that you need to know about.

So, here are examples of machine learning in your daily life:

1. Google Search Results

The example of machine learning that you use most often in your daily life is Google's search engine. In fact, machine learning is a system that has an important role in how the Google search engine works.

The Google search engine uses complex algorithms to analyze and rank website pages based on their relevance to the keywords searched by users.

Well, machine learning is used to continuously improve algorithm understanding to ensure search results remain relevant and match the user's search intent.

2. E-commerce product recommendations

After searching for a product on e-commerce, have you ever noticed that the product you were looking for keeps showing up in the recommendation column?

E-commerce such as Shopee, Tokopedia, and others use machine learning algorithms to analyze users' recent purchases and search history to provide product recommendations.

The purpose of this recommendation feature is to improve the customer experience. This is because this can help users find new products that they might not have known before.

3. Image Detection

The next example of machine learning is image detection. You can find it on social media like Facebook which uses algorithms to detect and tag people in photos.

By analyzing facial features such as eyes, nose, and mouth, algorithms can recognize the people in the photos and match them to the user's profile.

Not only in social media but image detection is also often used in other things such as CCTV to medical imaging to diagnose disease.

4. Translator App

The most interesting example of machine learning is a language translation app. To create this machine learning application, programmers must train algorithms so they can translate text.

In this way, the algorithm is given a dataset in the form of a series of texts in two different languages. Then, the algorithm will study the data patterns and develop rules for translating text.

5. Virtual Personal Assistant

A final example of machine learning is its use in creating virtual assistants such as chatbots. Chatbots are computer programs that use AI, primarily machine learning, to communicate with humans.

Chatbots are commonly used to assist businesses in providing fast and efficient customer service. Well, chatbots require machine learning algorithms to analyze and learn from data so they can provide more accurate answers over time.

Advantages and Disadvantages of Machine Learning

In the previous section, you already know the meaning of machine learning, its types, and examples in everyday life. Next, we will discuss the advantages and disadvantages of machine learning, namely:

Excess:

Able to process large and complex data in a short time.

Can learn patterns and trends that are invisible to humans.

Able to automate tasks that are usually performed by humans so as to reduce costs and increase efficiency.

Machine learning will continue to learn and improve its performance as more data is received.

Minimizing human error.

Lack:

Requires data mining to provide accurate results. If the data used is incorrect or incomplete, the results will also be inaccurate.

Developing complex machine learning algorithms can take a long time.

High expertise in programming, mathematics, and statistics is required.

High machine learning development costs.

Difference between Machine Learning and Deep Learning

Ever heard of Deep Learning? Yes, besides machine learning, this term is also starting to be found a lot now. If you want to know the difference between machine learning and deep learning, we'll explain it.

Deep learning is a branch of machine learning that uses neural networks to learn complex data. The goal is to achieve a higher level of accuracy than traditional machine learning techniques.

The neural network in question is a system that mimics how the human brain works. As a result, deep learning can process both structured and unstructured data such as images, audio, and text.

In this way, deep learning can be used to develop image and sound detection which is difficult to do with traditional machine learning.

Tips for Learning Machine Learning

Learning machine learning is not about memorizing complex algorithms or mastering a programming language. Instead, it is closely related to the development of logic and analytical skills to identify patterns in data.

Well, the way to hone the logic of thinking can be by getting used to coding. For that, here are tips on machine learning stages that you can apply:

1. Master Multivariate Calculus and Linear Algebra

It is important to have a deep understanding of mathematics in the learning process of machine learning. While it's not necessary to have a degree in math, you should still have some basic knowledge of the basic concepts of calculus and linear algebra.

This is because calculus is the foundation for understanding how machine learning works. Concepts such as derivatives and integrals are often used to optimize algorithms and minimize errors. In addition, linear algebra is also important for processing data so that it is easily understood by computers.

2. Understand Probability and Statistics

The next tip in the machine learning process is to understand probability and statistics.

Probability is a measure of the likelihood that something will happen. So, programmers can identify the possibility of a relationship between two variables.

Then, statistics are the algorithms used in translating data patterns to be followed up. Not only that, statistics also serves to apply mathematical models to the appropriate variables.

By mastering these two concepts, you will understand all available data information. So, you can provide accurate data to train machine learning algorithms.

3. Learn Programming Fundamentals

The next tip that should not be missed before learning machine learning is to learn programming fundamentals. This is because machine learning is related to developing algorithms that can process and analyze data which requires programming skills.

So, you must understand the following concepts to learn machine learning:

Algorithm programming – You must master the algorithm because it is closely related to the logic that will be used during the programming process

Data structure – It is important to manage data in a computer system or database to make it more accessible. Not only that, but by studying data structures, problems in coding can be quickly resolved because they will be easy to find.

Syntax - Syntax is the rule for writing code to make a program. So, you need to ensure the accuracy of the syntax used to prevent errors from occurring.

4. Learn the Python Programming Language

The programming language that must be mastered before learning machine learning is Python. This is because this language supports data collection, analysis, and modeling.

It doesn't stop there, Python also has several other advantages that make it often used in machine learning, namely:

Python has an ecosystem of libraries suitable for machine learning such as TensorFlow and Scikit-Learn.

Python can be integrated with cloud computing, data analysis, and big data.

Python is open source, so you can find machine learning frameworks and algorithms in a variety of sources.

5. Have Database Management Skills

Database management is one of the skills that are also important to have before learning machine learning. Because, to develop effective machine learning algorithms, you need to understand how data is stored, accessed, and managed in database systems.

Apart from that, learning database management also helps you understand basic concepts in databases, such as data indexing, data normalization, and relationships between tables.

Well, this will help you process data in machine learning, because you can understand how to optimize data to produce better and more accurate machine learning algorithms.

After following the tips above and having sufficient understanding, you can immediately start learning machine learning.

Interested in Learning Machine Learning?

In this article, you have learned what machine learning is, examples of machine learning in everyday life, its advantages, and disadvantages, to study tips.

You also now know that machine learning is an advanced technology that has actually been applied to various things in everyday life. Starting from Google search results, and virtual assistants, to translator applications.

However, machine learning still has advantages and disadvantages that need to be considered. Despite its ability to solve problems efficiently, you still need to understand its flaws in order to avoid mistakes and still get accurate results.

What do you think? Come on, discuss machine learning in the comments column! Oh yes, don't forget to click Subscribe so you don't miss the latest article updates.