What Is Machine Learning?

Experience is the best teacher, they say. 

Computers get motivated by this cliché, too. We have a term for it – machine learning (ML) technology. 

What is machine learning, you ask?

Self-driving cars, talking robots, navigation apps – these are everyday scenes of Artificial Intelligence and ML in action. 

Remember when you asked Siri about the weather this morning? Or last night, when you browsed through your well-suited Netflix recommendations? 

Read on, there’s so much more to discover. 

What Is Machine Learning?

Plain and simple, machine learning is a branch of AI that enables artificial development. It enables computers to: 

• process information 
• make decisions 
• perform a series of tasks 
• improve outcomes

All on their own. 

Using a set of algorithms, machine learning models conduct automated statistical methods to learn from training data. This allows them to identify patterns and deliver reliable results. Just like us, they can solve a problem or respond to a specific situation. Then, they apply that info to the next one. 

Literally – it’s all about learning from experience. And take note - machine learning technologies involve little to no human intervention at all. 

To understand this a bit better, let’s take a trip down memory lane.

One of the guys at IBM, Arthur Samuel, actually started this whole thing. Back in the 50s, he developed an application for playing the game of checkers. It was part of his ML research. 

The computer got better as it played more games. It started to develop winning strategies from certain moves, which later shaped the minimax algorithm that we know today.

Would you believe that in the 60s, a human ‘checkers master’ tried to play against it, and the computer won? Yeah, it totally did. 

Flash forward to 2021, this basic machine learning feat has become the stepping stone for some of the most successful innovations today. It also gave birth to tech systems like data visualization, the Internet of Things, analytics tools, еtc.

All this makes you wonder, what else can machine learning do? 

We’ll get to that, but first, let’s take a look at how the magic happens. 

How Does Machine Learning Work?

ML dives into various stages of statistical learning. That’s a loaded term, so we’ll just try to put it in a nutshell.

It’s a constant cycle of pattern recognition and mathematical optimization to predict accurate estimates. It usually involves several tech components, including:

• Data sets
• Self-learning algorithms
• Models
• Feature engineering
• Training methods

Most machine learning techniques also use data mining to carry out this process. Through this, the computer is able to spot historical trends and use that to develop future systems. 

But how does machine learning work?

Let’s dive a bit deeper.

Machine Learning Phases

According to this study, the process is divided into three main parts:

• Decision phase - Generally, ML algorithms are trained to classify data so they can come up with a prediction. During the decision phase, they execute a series of computational statistics to produce a ‘smart guess’. This involves feature learning and processing various datasets. That’s why this part usually takes the longest to finish.
• Error function - When the system’s done producing a guess, an error function steps in. Algorithms use this to measure how accurate the prediction is. To do this, it compares the material to other known examples for proper evaluation. 
• Optimization step - This is the part where machine learning systems start to get better. When an error is spotted, the algorithm updates the decision phase. It makes adjustments to the parameters. As a result, the next guess turns out more accurate than the last one. 

Now, what exactly are the machine learning algorithms used in the process?

Machine learning logistic regression algorithms

Here are some of the most common ones: 

• Linear Regression - Typically used in data science, linear regression is a popular type of supervised algorithm. Here, the model estimate follows a constant slope. It studies the relationship between two variables - dependent and independent - to predict values instead of classifying them. 

Businesses often use this to quantify sales and prices, predict consumer behavior, or identify trends.

• Logistic Regression - Also among the common machine learning algorithms, logistic regression solves classification problems rather than regression problems. 

Using numerous independent variables, it predicts the discrete value of a categorical dependent variable. In other words, the result should either be ‘yes or no’, ‘true or false’, ‘0 or 1’, etc.

Let's go deeper

Other types of logistic regression algorithms can yield multiple answers, too. For example, multinomial logistic regression uses probabilistic theory to produce 3 or more answers with no order.

• Linear Discriminant Analysis - LDA works best in categorizing input data. It’s one of the more basic machine learning algorithms popularly used in classification projects. That includes image recognition, marketing predictive analysis, etc. It establishes the relationship between an object and a population. That’s why it’s effective in modeling varieties in multiple groups!
• Decision Tree Learning - A decision tree is typically used for classifying problems. It continuously splits the data into two or more homogeneous groups based on the given parameter. 

Like an actual tree, it consists of two components – the leaves and decision nodes. The leaves represent the decisions for the final prediction. The decision nodes refer to the area where the data is split. 

Decision trees have the ability to classify both categorical and continuous dependent variables.

These are just some of the widely known algorithms. There are many other types of machine learning algorithms used by programmers and developers today.

And how are these systems categorized?

That’s our next agenda.

We’ll talk about the different machine learning methods employed by various industries and businesses using ML technology. 

Machine Learning Methods

You saw the word ‘supervised’ thrown around quite a bit earlier, right?

If you’re wondering what this term means, we got you covered. It’s just one of the key machine learning concepts that facilitate the algorithms. 

Here they are: 

Supervised Learning

This method uses labeled data inputs to accurately classify and predict the results. 

The more data you feed into the model, the more it makes adjustments to the parameters! 

It does this by comparing the input to an existing correct output. If it finds a mistake, the model gets readjusted accordingly. Along that process, it uses these techniques: 

• Regression
• Classification
• Prediction
• Gradient Boosting

This helps the system come up with a structured prediction for the unlabeled data. It works for instances where historical data have the answers to predict the outcome.

In this case, a supervised machine learning algorithm is best used for: 

• fraud detections 
• insurance claims 
• weather and market forecasts 
• customer retention

And more!

Unsupervised Learning

Obviously, it’s the total opposite of the method above. 

But how, exactly?

These ML algorithms work with unlabelled inputs. The goal is to dive into exploratory data analysis, so it can form information clusters.

In this case, there is no correct output or known sample that will support the prediction process. It should be able to spot similarities and differences on the given datasets. It also aims to identify structures within the data for proper clustering.

Unsupervised learning would be great for analyzing transactional data. It can be used to segment customers with the same attributes, which is the basis for sending targeted marketing campaigns. This method is also widely used for image and pattern recognition projects.

Semi-supervised Learning

Information machines can find some middle ground in a semi-supervised method. 

It uses both labeled and unlabelled data inputs. Ideally, it involves a smaller amount of labeled and a larger chunk of unlabelled data. That’s because the latter doesn’t require many resources, like money and effort, to extract. It’s also proven to produce a more significant development when it comes to learning accuracy.

In processing data, it adapts some of the techniques from supervised learning. That includes regression, classification, and prediction.

Reinforcement Learning 

The last of the machine learning categories is the reinforcement method. It’s quite similar to supervised learning, but there's a key distinction. 

These algorithms aren’t trained with existing outputs or sample data. 

Instead, the systems learn from trial-and-error. Successful outcomes are reinforced and taken as the basis for the next predictions. This process involves three variables – the agent, the environment, and the action.

Reinforcement algorithms work well for industries such as gaming, robotics, navigation, and automation tools.

What Is Deep Learning?

Before we answer that, did you know that Google has already adapted the machine learning process?

An example – Google Brain. This project successfully infused deep learning into the company’s tech products, such as Google Assistant. The software is now able to recognize and execute spoken commands completely on its own.

But what is deep learning technology, anyway?

It’s a subfield of machine learning that imitates how the human brain works. It is powered by a neural network that contains multiple layers. Together, they simulate the whole cognitive process. 

Artificial neural networks have the ability to process huge volumes of data, and eventually learn from them. This allows them to refine their own systems and further increase their accuracy.

But that sounds a lot like ML, don’t you think?

Is deep learning and machine learning the same, then?

Not exactly. 

Deep learning is basically a special application of machine learning. The latter involves manual feature engineering from datasets, then creating a model that classifies the materials. The other one automates both the feature extraction and model generation process.

Another edge that deep learning has over ML is this – the bigger the data size gets, the faster it takes for its systems to improve!

Machine Learning Use Cases

Now you might be asking, what is machine learning really meant for? 

We’ve already established that ML makes significant impacts in our lives – both on a personal and industrial level. 

But how?

Machine learning features in many fields.

Customer Service

Notice how companies are starting to ditch the traditional means of customer support lately? They are now using chatbots to speed up the process. 

How does machine learning work for this technology? It uses Natural Language Processing (NLP), which allows computers to analyze language data. With this, chatbots can understand the context and tone of messages. They can use this to instantly address the concern, or redirect a customer to a human representative!

Transportation

Self-driving cars are the superstars of ML. They can hit the brakes, take turns, or switch gears without a human pilot. Google’s Waymo project is at the forefront of this innovation. Using advanced machine learning analysis, the system is able to observe different variables on the road and apply it to its driving behavior.

GPS navigation is also a product of ML, as they are able to track better routes or make traffic predictions.

Recommendation Engines

Open up your App Store or Play Store – notice anything interesting on your Recommended section? Apple and Google both use ML-enabled recommendation engines for this. It displays app selections based on your past downloads. Cool, right? 

How machine learning works for video streaming services is also the same. The titles displayed on your catalog are based on your watch list or history.

Speech Recognition 

Siri, Bixby, Alexa, and Cortana are all cut from the same tech. That is Automatic Speech Recognition (ASR). It is otherwise known as the speech-to-text feature. It enables these ‘virtual personal assistants’ to record human speech and translate it into text format. 

Why is machine learning important here? 

With it, machine translation is done instantly and more accurately. So, you can make faster voice searches, commands, texts, etc.

Education

Online learning systems also benefit from the applications of machine learning. Educational technology now involves learning and predictive analytics to address the needs of individual learners and teachers. Learning modes can also be automated. One example would be assessment evaluations. Through ML, computers are now able to check answers and grade assignments using algorithms.

Web Services

Machine learning for the web has many different uses. One of the more common ones is email filtering, which we see every day but don’t seem to notice it! 

Years ago, there was no escaping those random and spammy emails that flooded your inbox. Well now they’re well-organized, aren’t they? How does machine learning work here?

Let’s take Gmail, for example. It now has folders for different email types – Primary, Social, and Promotions. ML algorithms classify these emails and put them in the proper mailboxes. 

Marketing and Online Advertising

Aside from chatbots, businesses also maximize the uses of machine learning to carry out marketing, advertising, and sales operations. Automation programs can do proper segmentation for targeted campaigns, newsletters, ads, etc. Startups and owners also maximize content generators. They use this software to create product descriptions, blog posts, and social media copies for their goods and services.

Security

Do you know what else is machine learning used for? 

Cybersecurity. 

AI systems are now able to detect possible threats and malicious activities before they even happen. It also adds another layer of safety to your belongings. You know iPhone's face ID? That’s facial recognition, which is a product of ML development. Many sectors also use this, including government systems verifying people’s identities.

Clearly, machine learning uses are everywhere. ML is present in almost all tech pillars of our modern society, and that’s a good thing.

AI predictions even tell us that we’re potentially looking at a 40% rise in productivity. All thanks to machine learning and AI systems penetrating many sectors.

Speaking of AI...

Machine Learning vs AI - What’s The Difference?

We mentioned earlier that the magic behind ML is artificial intelligence.

As they have very similar features, machine learning and AI often get mistaken for each other.

Are they the same?

Let’s discuss what is AI and machine learning.

So – what’s an AI? It’s a broad concept, but it’s about making machines intelligent enough to solve complex problems. It mimics human cognitive behavior. So, it is able to perform tasks that usually require human intelligence. 

Then how does machine learning work? It’s a tool that enables machines to learn from data and deliver results without being programmed. For this, it uses specific algorithms to train its systems and create models that refine those systems.

Alright then, let’s settle it – is machine learning AI?

Definitely not.

The key distinction here is that machine learning is only a branch of AI.

Another difference is that ML is concerned more with accuracy and pattern recognition. AI leans more towards successfully accomplishing a given task.

And lastly, ML provides outcomes for tasks that it was trained to do, while AI extends far beyond that.

Wrap Up

Let’s run it back from the top.

What is machine learning?

The ability of computers to learn by example.

The machine learning process helps devices face data, analyze it, develop their systems, and produce desirable outcomes.

The different types of machine learning also make it possible for computers and software to adapt, and most importantly - to learn continuously. 

All of that – without being explicitly programmed by human hands.

We know, it sounds scary. 

But in essence, it only makes our relationship with technology easier, smoother, and smarter.