As computing technology evolved and the concept of linking multiple computers together into a “network” that could share data came into being, it was clear that a model was needed to define and enable those connections. Enter the OSI model in computer network idea.

This model allows various devices and software to “communicate” with one another by creating a set of universal rules and functions. Let’s dig into what the model entails.

History of the OSI Model

In the late 1970s, the continued development of computerized technology saw many companies start to introduce their own systems. These systems stood alone from others. For example, a computer at Retailer A has no way to communicate with a computer at Retailer B, with neither computer being able to communicate with the various vendors and other organizations within the retail supply chain.

Clearly, some way of connecting these standalone systems was needed, leading to researchers from France, the U.S., and the U.K. splitting into two groups – The International Organization for Standardization and the International Telegraph and Telephone Consultive Committee.

In 1983, these two groups merged their work to create “The Basic Reference Model for Open Systems Interconnection (OSI).” This model established industry standards for communication between networked devices, though the path to OSI’s implementation wasn’t as clear as it could have been. The 1980s and 1990s saw the introduction of another model – The TCP IP model – which competed against the OSI model for supremacy. TCP/IP gained so much traction that it became the cornerstone model for the then-budding internet, leading to the OSI model in computer network applications falling out of favor in many sectors. Despite this, the OSI model is still a valuable reference point for students who want to learn more about networking and still have some practical uses in industry.

The OSI Reference Model

The OSI model works by splitting the concept of computers communicating with one another into seven computer network layers (defined below), each offering standardized rules for its specific function. During the rise of the OSI model, these layers worked in concert, allowing systems to communicate as long as they followed the rules.

Though the OSI model in computer network applications has fallen out of favor on a practical level, it still offers several benefits:

• The OSI model is perfect for teaching network architecture because it defines how computers communicate.
• OSI is a layered model, with separation between each layer, so one layer doesn’t affect the operation of any other.
• The OSI model offers flexibility because of the distinctions it makes between layers, with users being able to replace protocols in any layer without worrying about how they’ll impact the other layers.

The 7 Layers of the OSI Model

The OSI reference model in computer network teaching is a lot like an onion. It has several layers, each standing alone but each needing to be peeled back to get a result. But where peeling back the layers of an onion gets you a tasty ingredient or treat, peeling them back in the OSI model delivers a better understanding of networking and the protocols that lie behind it.

Each of these seven layers serves a different function.

Layer 1: Physical Layer

Sitting at the lowest level of the OSI model, the physical layer is all about the hows and wherefores of transmitting electrical signals from one device to another. Think of it as the protocols needed for the pins, cables, voltages, and every other component of a physical device if said device wants to communicate with another that uses the OSI model.

Layer 2: Data Link Layer

With the physical layer in place, the challenge shifts to transmitting data between devices. The data layer defines how node-to-node transfer occurs, allowing for the packaging of data into “frames” and the correction of errors that may happen in the physical layer.

The data layer has two “sub-layers” of its own:

• MAC – Media Access Controls that offer multiplexing and flow control to govern a device’s transmissions over an OSI network.
• LLC – Logical Link Controls that offer error control over the physical media (i.e., the devices) used to transmit data across a connection.

Layer 3: Network Layer

The network layer is like an intermediary between devices, as it accepts “frames” from the data layer and sends them on their way to their intended destination. Think of this layer as the postal service of the OSI model in computer network applications.

Layer 4: Transport Layer

If the network layer is a delivery person, the transport layer is the van that the delivery person uses to carry their parcels (i.e., data packets) between addresses. This layer regulates the sequencing, sizing, and transferring of data between hosts and systems. TCP (Transmission Control Protocol) is a good example of a transport layer in practical applications.

Layer 5: Session Layer

When one device wants to communicate with another, it sets up a “session” in which the communication takes place, similar to how your boss may schedule a meeting with you when they want to talk. The session layer regulates how the connections between machines are set up and managed, in addition to providing authorization controls to ensure no unwanted devices can interrupt or “listen in” on the session.

Layer 6: Presentation Layer

Presentation matters when sending data from one system to another. The presentation layer “pretties up” data by formatting and translating it into a syntax that the recipient’s application accepts. Encryption and decryption is a perfect example, as a data packet can be encrypted to be unreadable to anybody who intercepts it, only to be decrypted via the presentation layer so the intended recipient can see what the data packet contains.

Layer 7: Application Layer

The application layer is a front end through which the end user can interact with everything that’s going on behind the scenes in the network. It’s usually a piece of software that puts a user-friendly face on a network. For instance, the Google Chrome web browser is an application layer for the entire network of connections that make up the internet.

Interactions Between OSI Layers

Though each of the OSI layers in computer networks is independent (lending to the flexibility mentioned earlier), they must also interact with one another to make the network functional.

We see this most obviously in the data encapsulation and de-encapsulation that occurs in the model. Encapsulation is the process of adding information to a data packet as it travels, with de-encapsulation being the method used to remove that data added data so the end user can read what was originally sent. The previously mentioned encryption and decryption of data is a good example.

That process of encapsulation and de-encapsulation defines how the OSI model works. Each layer adds its own little “flavor” to the transmitted data packet, with each subsequent layer either adding something new or de-encapsulating something previously added so it can read the data. Each of these additions and subtractions is governed by the protocols set within each layer. A perfect network can only exist if these protocols properly govern data transmission, allowing for communication between each layer.

Real-World Applications of the OSI Model

There’s a reason why the OSI model in computer network study is often called a “reference” model – though important, it was quickly replaced with other models. As a result, you’ll rarely see the OSI model used as a way to connect devices, with TCP/IP being far more popular. Still, there are several practical applications for the OSI model.

Network Troubleshooting and Diagnostics

Given that some modern computer networks are unfathomably complex, picking out a single error that messes up the whole communication process can feel like navigating a minefield. Every wrong step causes something else to blow up, leading to more problems than you solve. The OSI model’s layered approach offers a way to break down the different aspects of a network to make it easier to identify problems.

Network Design and Implementation

Though the OSI model has few practical purposes, as a theoretical model it’s often seen as the basis for all networking concepts that came after. That makes it an ideal teaching tool for showcasing how networks are designed and implemented. Some even refer to the model when creating networks using other models, with the layered approach helping understand complex networks.

Enhancing Network Security

The concept of encapsulation and de-encapsulation comes to the fore again here (remember – encryption), as this concept shows us that it’s dangerous to allow a data packet to move through a network with no interactions. The OSI model shows how altering that packet as it goes on its journey makes it easier to protect data from unwanted eyes.

Limitations and Criticisms of the OSI Model

Despite its many uses as a teaching tool, the OSI model in computer network has limitations that are the reasons why it sees few practical applications:

• Complexity – As valuable as the layered approach may be to teaching networks, it’s often too complex to execute in practice.
• Overlap – The very flexibility that makes OSI great for people who want more control over their networks can come back to bite the model. The failure to implement proper controls and protocols can lead to overlap, as can the layered approach itself. Each of the computer network layers needs the others to work.
• The Existence of Alternatives – The OSI model walked so other models could run, establishing many fundamental networking concepts that other models executed better in practical terms. Again, the massive network known as the internet is a great example, as it uses the TCP/IP model to reduce complexity and more effectively transmit data.

Use the OSI Reference Model in Computer Network Applications

Though it has little practical application in today’s world, the OSI model in computer network terms is a theoretical model that played a crucial role in establishing many of the “rules” of networking still used today. Its importance is still recognized by the fact that many computing courses use the OSI model to teach the fundamentals of networks.

Think of learning about the OSI model as being similar to laying the foundations for a house. You’ll get to grips with the basic concepts of how networks work, allowing you to build up your knowledge by incorporating both current networking technology and future advancements to become a networking specialist.

Computer architecture forms the backbone of computer science. So, it comes as no surprise it’s one of the most researched fields of computing.

But what is computer architecture, and why does it matter?

Basically, computer architecture dictates every aspect of a computer’s functioning, from how it stores data to what it displays on the interface. Not to mention how the hardware and software components connect and interact.

With this in mind, it isn’t difficult to realize the importance of this structure. In fact, computer scientists did this even before they knew what to call it. The first documented computer architecture can be traced back to 1936, 23 years before the term “architecture” was first used when describing a computer. Lyle R. Johnson, an IBM senior staff member, had this honor, realizing that the word organization just doesn’t cut it.

Now that you know why you should care about it, let’s define computer architecture in more detail and outline everything you need to know about it.

Basic Components of Computer Architecture

Computer architecture is an elaborate system where each component has its place and function. You’re probably familiar with some of the basic computer architecture components, such as the CPU and memory. But do you know how those components work together? If not, we’ve got you covered.

Central Processing Unit (CPU)

The central processing unit (CPU) is at the core of any computer architecture. This hardware component only needs instructions written as binary bits to control all its surrounding components.

Think of the CPU as the conductor in an orchestra. Without the conductor, the choir is still there, but they’re waiting for instructions.

Without a functioning CPU, the other components are still there, but there’s no computing.

That’s why the CPU’s components are so important.

Arithmetic Logic Unit (ALU)

Since the binary bits used as instructions by the CPU are numbers, the unit needs an arithmetic component to manipulate them.

That’s where the arithmetic logic unit, or ALU, comes into play.

The ALU is the one that receives the binary bits. Then, it performs an operation on one or more of them. The most common operations include addition, subtraction, AND, OR, and NOT.

Control Unit (CU)

As the name suggests, the control unit (CU) controls all the components of basic computer architecture. It transfers data to and from the ALU, thus dictating how each component behaves.

Registers

Registers are the storage units used by the CPU to hold the current data the ALU is manipulating. Each CPU has a limited number of these registers. For this reason, they can only store a limited amount of data temporarily.

Memory

Storing data is the main purpose of the memory of a computer system. The data in question can be instructions issued by the CPU or larger amounts of permanent data. Either way, a computer’s memory is never empty.

Traditionally, this component can be broken into primary and secondary storage.

Primary Memory

Primary memory occupies a central position in a computer system. It’s the only memory unit that can communicate with the CPU directly. It stores only programs and data currently in use.

There are two types of primary memory:

• RAM (Random Access Memory). In computer architecture, this is equivalent to short-term memory. RAM helps start the computer and only stores data as long as the machine is on and data is being used.
• ROM (Read Only Memory). ROM stores the data used to operate the system. Due to the importance of this data, the ROM stores information even when you turn off the computer.

Secondary Memory

With secondary memory, or auxiliary memory, there’s room for larger amounts of data (which is also permanent). However, this also means that this memory is significantly slower than its primary counterpart.

When it comes to secondary memory, there’s no shortage of choices. There are magnetic discs (hard disk drives (HDDs) and solid-state drives (SSDs)) that provide fast access to stored data. And let’s not forget about optical discs (CD-ROMs and DVDs) that offer portable data storage.

Input/Output (I/O) Devices

The input/output devices allow humans to communicate with a computer. They do so by delivering or receiving data as necessary.

You’re more than likely familiar with the most widely used input devices – the keyboard and the mouse. When it comes to output devices, it’s pretty much the same. The monitor and printer are at the forefront.

Buses

When the CPU wants to communicate with other internal components, it relies on buses.

Data buses are physical signal lines that carry data. Most computer systems use three of these lines:

• Data bus – Transmitting data from the CPU to memory and I/O devices and vice versa
• Address bus – Carrying the address that points to the location the CPU wants to access
• Control bus – Transferring control from one component to the other

Types of Computer Architecture

There’s more than one type of computer architecture. These types mostly share the same base components. However, the setup of these components is what makes them differ.

Von Neumann Architecture

The Von Neumann architecture was proposed by one of the originators of computer architecture as a concept, John Von Neumann. Most modern computers follow this computer architecture.

The Von Neumann architecture has several distinguishing characteristics:

• All instructions are carried out sequentially.
• It doesn’t differentiate between data and instruction. They’re stored in the same memory unit.
• The CPU performs one operation at a time.

Since data and instructions are located in the same place, fetching them is simple and efficient. These two adjectives can describe working with the Von Neumann architecture in general, making it such a popular choice.

Still, there are some disadvantages to keep in mind. For starters, the CPU is often idle since it can only access one bus at a time. If an error causes a mix-up between data and instructions, you can lose important data. Also, defective programs sometimes fail to release memory, causing your computer to crash.

Harvard Architecture

Harvard architecture was named after the famed university. Or, to be more precise, after an IBM computer called “Harvard Mark I” located at the university.

The main difference between this computer architecture and the Von Neumann model is that the Harvard architecture separates the data from the instructions. Accordingly, it allocates separate data, addresses, and control buses for the separate memories.

The biggest advantage of this setup is that the buses can fetch data concurrently, minimizing idle time. The separate buses also reduce the chance of data corruption.

However, this setup also requires a more complex architecture that can be challenging to develop and implement.

Modified Harvard Architecture

Today, only specialty computers use the pure form of Harvard architecture. As for other machines, a modified Harvard architecture does the trick. These modifications aim to soften the rigid separation between data and instructions.

RISC and CISC Architectures

When it comes to processor architecture, there are two primary approaches.

The CISC (Complex Instruction Set Computer) processors have a single processing unit and are pretty straightforward. They tackle one task at a time. As a result, they use less memory. However, they also need more time to complete an instruction.

Over time, the speed of these processors became a problem. This led to a processor redesign, resulting in the RISC architecture.

The new and improved RISC (Reduced Instruction Set Computer) processors feature larger registers and keep frequently used variables within the processor. Thanks to these handy functionalities, they can operate much more quickly.

Instruction Set Architecture (ISA)

Instruction set architecture (ISA) defines the instructions that the processor can read and act upon. This means ISA decides which software can be installed on a particular processor and how efficiently it can perform tasks.

There are three types of instruction set architecture. These types differ based on the placement of instructions, and their names are pretty self-explanatory. For stack-based ISA, the instructions are placed in the stack, a memory unit within the address register. The same principle applies for accumulator-based ISA (a type of register in the CPU) and register-based ISA (multiple registers within the system).

The register-based ISA is most commonly used in modern machines. You’ve probably heard of some of the most popular examples. For CISC architecture, there are x86 and MC68000. As for RISC, SPARC, MIPS, and ARM stand out.

Pipelining and Parallelism in Computer Architecture

In computer architecture, pipelining and parallelism are methods used to speed up processing.

Pipelining refers to overlapping multiple instructions and processing them simultaneously. This couldn’t be possible without a pipeline-like structure. Imagine a factory assembly line, and you’ll understand how pipelining works instantly.

This method significantly increases the number of processed instructions and comes in two types:

• Instruction pipelines – Used for fixed-point multiplication, floating-point operations, and similar calculations
• Arithmetic pipelines – Used for reading consecutive instructions from memory

Parallelism entails using multiple processors or cores to process data simultaneously. Thanks to this collaborative approach, large amounts of data can be processed quickly.

Computer architecture employs two types of parallelism:

• Data parallelism – Executing the same task with multiple cores and different sets of data
• Task parallelism – Performing different tasks with multiple cores and the same or different data

Multicore processors are crucial for increasing the efficiency of parallelism as a method.

Memory Hierarchy and Cache

In computer system architecture, memory hierarchy is essential for minimizing the time it takes to access the memory units. It refers to separating memory units based on their response times.

The most common memory hierarchy goes as follows:

• Level 1: Processor registers
• Level 2: Cache memory
• Level 3: Primary memory
• Level 4: Secondary memory

The cache memory is a small and fast memory located close to a processor core. The CPU uses it to reduce the time and energy needed to access data from the primary memory.

Cache memory can be further broken into levels.

• L1 cache (the primary cache) – The fastest cache unit in the system
• L2 cache (the secondary cache) – The slower but more spacious option than Level 1
• L3 cache (a specialized cache) – The largest and the slowest cache in the system used to improve the performance of the first two levels

When it comes to determining where the data will be stored in the cache memory, three mapping techniques are employed:

• Direct mapping – Each memory block is mapped to one pre-determined cache location
• Associative mapping – Each memory block is mapped to a single location, but it can be any location
• Set associative mapping – Each memory block is mapped to a subset of locations

The performance of cache memory directly impacts the overall performance of a computing system. The following cache replacement policies are used to better process big data applications:

• FIFO (first in, first out) ­– The memory block first to enter the primary memory gets replaced first
• LRU (least recently used) – The least recently used page is the first to be discarded
• LFU (least frequently used) – The least frequently used element gets eliminated first

Input/Output (I/O) Systems

The input/output or I/O systems are designed to receive and send data to a computer. Without these processing systems, the computer wouldn’t be able to communicate with people and other systems and devices.

There are several types of I/O systems:

• Programmed I/O – The CPU directly issues a command to the I/O module and waits for it to be executed
• Interrupt-Driven I/O – The CPU moves on to other tasks after issuing a command to the I/O system
• Direct Memory Access (DMA) – The data is transferred between the memory and I/O devices without passing through the CPU

There are three standard I/O interfaces used for physically connecting hardware devices to a computer:

• Peripheral Component Interconnect (PCI)
• Small Computer System Interface (SATA)
• Universal Serial Bus (USB)

Power Consumption and Performance in Computer Architecture

Power consumption has become one of the most important considerations when designing modern computer architecture. Failing to consider this aspect leads to power dissipation. This, in turn, results in higher operating costs and a shorter lifespan for the machine.

For this reason, the following techniques for reducing power consumption are of utmost importance:

• Dynamic Voltage and Frequency Scaling (DVFS) – Scaling down the voltage based on the required performance
• Clock gating – Shutting off the clock signal when the circuit isn’t in use
• Power gating – Shutting off the power to circuit blocks when they’re not in use

Besides power consumption, performance is another crucial consideration in computer architecture. The performance is measured as follows:

• Instructions per second (IPS) – Measuring efficiency at any clock frequency
• Floating-point operations per second (FLOPS) – Measuring the numerical computing performance
• Benchmarks – Measuring how long the computer takes to complete a series of test programs

Emerging Trends in Computer Architecture

Computer architecture is continuously evolving to meet modern computing needs. Keep your eye out on these fascinating trends:

• Quantum computing (relying on the laws of quantum mechanics to tackle complex computing problems)
• Neuromorphic computing (modeling the computer architecture components on the human brain)
• Optical computing (using photons instead of electrons in digital computation for higher performance)
• 3D chip stacking (using 3D instead of 2D chips as they’re faster, take up less space, and require less power)

A One-Way Ticket to Computing Excellence

As you can tell, computer architecture directly affects your computer’s speed and performance. This launches it to the top of priorities when building this machine.

High-performance computers might’ve been nice-to-haves at some point. But in today’s digital age, they’ve undoubtedly become a need rather than a want.

In trying to keep up with this ever-changing landscape, computer architecture is continuously evolving. The end goal is to develop an ideal system in terms of speed, memory, and interconnection of components.

And judging by the current dominant trends in this field, that ideal system is right around the corner!

As artificial intelligence and machine learning are becoming present in almost every aspect of life, it’s essential to understand how they work and their common applications. Although machine learning has been around for a while, many still portray it as an enemy. Machine learning can be your friend, but only if you learn to “tame” it.

Regression stands out as one of the most popular machine-learning techniques. It serves as a bridge that connects the past to the present and future. It does so by picking up on different “events” from the past and breaking them apart to analyze them. Based on this analysis, regression can make conclusions about the future and help many plan the next move.

The weather forecast is a basic example. With the regression technique, it’s possible to travel back in time to view average temperatures, humidity, and other variables relevant to the results. Then, you “return” to present and tailor predictions about the weather in the future.

There are different types of regression, and each has unique applications, advantages, and drawbacks. This article will analyze these types.

Linear Regression

Linear regression in machine learning is one of the most common techniques. This simple algorithm got its name because of what it does. It digs deep into the relationship between independent and dependent variables. Based on the findings, linear regression makes predictions about the future.

There are two distinguishable types of linear regression:

• Simple linear regression – There’s only one input variable.
• Multiple linear regression – There are several input variables.

Linear regression has proven useful in various spheres. Its most popular applications are:

• Predicting salaries
• Analyzing trends
• Forecasting traffic ETAs
• Predicting real estate prices

Polynomial Regression

At its core, polynomial regression functions just like linear regression, with one crucial difference – the former works with non-linear datasets.

When there’s a non-linear relationship between variables, you can’t do much with linear regression. In such cases, you send polynomial regression to the rescue. You do this by adding polynomial features to linear regression. Then, you analyze these features using a linear model to get relevant results.

Here’s a real-life example in action. Polynomial regression can analyze the spread rate of infectious diseases, including COVID-19.

Ridge Regression

Ridge regression is a type of linear regression. What’s the difference between the two? You use ridge regression when there’s high colinearity between independent variables. In such cases, you have to add bias to ensure precise long-term results.

This type of regression is also called L2 regularization because it makes the model less complex. As such, ridge regression is suitable for solving problems with more parameters than samples. Due to its characteristics, this regression has an honorary spot in medicine. It’s used to analyze patients’ clinical measures and the presence of specific antigens. Based on the results, the regression establishes trends.

LASSO Regression

No, LASSO regression doesn’t have anything to do with cowboys and catching cattle (although that would be interesting). LASSO is actually an acronym for Least Absolute Shrinkage and Selection Operator.

Like ridge regression, this one also belongs to regularization techniques. What does it regulate? It reduces a model’s complexity by eliminating parameters that aren’t relevant, thus concentrating the selection and guaranteeing better results.

Many choose ridge regression when analyzing a model with numerous true coefficients. When there are only a few of them, use LASSO. Therefore, their applications are similar; the real difference lies in the number of available coefficients.

Elastic Net Regression

Ridge regression is good for analyzing problems involving more parameters than samples. However, it’s not perfect; this regression type doesn’t promise to eliminate irrelevant coefficients from the equation, thus affecting the results’ reliability.

On the other hand, LASSO regression eliminates irrelevant parameters, but it sometimes focuses on far too few samples for high-dimensional data.

As you can see, both regressions are flawed in a way. Elastic net regression is the combination of the best characteristics of these regression techniques. The first phase is finding ridge coefficients, while the second phase involves a LASSO-like shrinkage of these coefficients to get the best results.

Support Vector Regression

Support vector machine (SVM) belongs to supervised learning algorithms and has two important uses:

• Regression
• Classification problems

Let’s try to draw a mental picture of how SVM works. Suppose you have two classes of items (let’s call them red circles and green triangles). Red circles are on the left, while green triangles are on the right. You can separate these two classes by drawing a line between them.

Things get a bit more complicated if you have red circles in the middle and green triangles wrapped around them. In that case, you can’t draw a line to separate the classes. But you can add new dimensions to the mix and create a circle (rectangle, square, or a different shape encompassing just the red circles).

This is what SVM does. It creates a hyperplane and analyzes classes depending on where they belong.

There are a few parameters you need to understand to grasp the reach of SVM fully:

• Kernel – When you can’t find a hyperplane in a dimension, you move to a higher dimension, which is often challenging to navigate. A kernel is like a navigator that helps you find the hyperplane without plummeting computational costs.
• Hyperplane – This is what separates two classes in SVM.
• Decision boundary – Think of this as a line that helps you “decide” the placement of positive and negative examples.

Support vector regression takes a similar approach. It also creates a hyperplane to analyze classes but doesn’t classify them depending on where they belong. Instead, it tries to find a hyperplane that contains a maximum number of data points. At the same time, support vector regression tries to lower the risk of prediction errors.

SVM has various applications. It can be used in finance, bioinformatics, engineering, HR, healthcare, image processing, and other branches.

Decision Tree Regression

This type of supervised learning algorithm can solve both regression and classification issues and work with categorical and numerical datasets.

As its name indicates, decision tree regression deconstructs problems by creating a tree-like structure. In this tree, every node is a test for an attribute, every branch is the result of a test, and every leaf is the final result (decision).

The starting point of (the root) of every tree regression is the parent node. This node splits into two child nodes (data subsets), which are then further divided, thus becoming “parents” to their “children,” and so on.

You can compare a decision tree to a regular tree. If you take care of it and prune the unnecessary branches (those with irrelevant features), you’ll grow a healthy tree (a tree with concise and relevant results).

Due to its versatility and digestibility, decision tree regression can be used in various fields, from finance and healthcare to marketing and education. It offers a unique approach to decision-making by breaking down complex datasets into easy-to-grasp categories.

Random Forest Regression

Random forest regression is essentially decision tree regression but on a much bigger scale. In this case, you have multiple decision trees, each predicting a certain output. Random forest regression analyzes the outputs of every decision tree to come up with the final result.

Keep in mind that the decision trees used in random forest regression are completely independent; there’s no interaction between them until their outputs are analyzed.

Random forest regression is an ensemble learning technique, meaning it combines the results (predictions) of several machine learning algorithms to create one final prediction.

Like decision tree regression, this one can be used in numerous industries.

The Importance of Regression in Machine Learning Is Immeasurable

Regression in machine learning is like a high-tech detective. It travels back in time, identifies valuable clues, and analyzes them thoroughly. Then, it uses the results to predict outcomes with high accuracy and precision. As such, regression found its way to all niches.

You can use it in sales to analyze the customers’ behavior and anticipate their future interests. You can also apply it in finance, whether to discover trends in prices or analyze the stock market. Regression is also used in education, the tech industry, weather forecasting, and many other spheres.

Every regression technique can be valuable, but only if you know how to use it to your advantage. Think of your scenario (variables you want to analyze) and find the best actor (regression technique) who can breathe new life into it.

Thanks to many technological marvels of our era, we’ve moved from writing important documents using pen and paper to storing them digitally.

Database systems emerged as the amount and complexity of information we need to keep have increased significantly in the last decades. They represent virtual warehouses for storing documents. Database management systems (DBMS) and relational database management systems (RDBMS) were born out of a burning need to easily control, organize, and edit databases.

Both DBMS and RDBMS represent programs for managing databases. But besides the one letter in the acronym, the two terms differ in several important aspects.

Here, we’ll outline the difference between DBMS and RDBMS, help you learn the ins and outs of both, and choose the most appropriate one.

Definition of DBMS (Database Management Systems)

While working for General Electric during the 1960s, Charles W. Bachman recognized the importance of proper document management and found that the solutions available at the time weren’t good enough. He did his research and came up with a database management system, a program that made storing, editing, and retrieving files a breeze. Unknowingly, Bachman revolutionized the industry and offered the world a convenient database management solution with amazing properties.

Key Features

Over the years, DBMSs have become powerful beasts that allow you to enhance performance and efficiency, save time, and handle huge amounts of data with ease.

One of the key features of DBMSs is that they store information as files in one of two forms: hierarchical or navigational. When managing data, users can use one of several manipulation functions the systems offer:

• Inserting data
• Deleting data
• Updating data

DBMSs are simple structures ideal for smaller companies that don’t deal with huge amounts of data. Only a single user can handle information, which can be a deal-breaker for larger entities.

Although fairly simple, DBMSs bring a lot to the table. They allow you to access, edit, and share data in the blink of an eye. Moreover, DBMSs let you unify your team and have accurate and reliable information on the record, ensuring nobody is left out. They also help you stay compliant with different security and privacy regulations and lower the risk of violations. Finally, having an efficient database management system leads to wiser decision-making that can ultimately save you a lot of time and money.

Examples of Popular DBMS Software

When DBMSs were just becoming a thing, you had software like Clipper and FoxPro. Today, the most popular (and simplest) examples of DBMS software are XML, Windows Registry, and file systems.

Definition of RDBMS (Relational Database Management Systems)

Not long after DBMS came into being, people recognized the need to keep data in the form of tables. They figured storing info in rows (tuples) and columns (attributes) allows a clearer view and easier navigation and information retrieval. This idea led to the birth of relational database management systems (RDBMS) in the 1970s.

Key Features

As mentioned, the only way RDBMSs store information is in the form of tables. Many love this feature because it makes organizing and classifying data according to different criteria a piece of cake. Many companies that use RDBMSs utilize multiple tables to store their data, and sometimes, the information in them can overlap. Fortunately, RDBMSs allow relating data from various tables to one another (hence the name). Thanks to this, you’ll have no trouble adding the necessary info in the right tables and moving it around as necessary.

Since you can relate different pieces of information from your tables to each other, you can achieve normalization. However, normalization isn’t the process of making your table normal. It’s a way of organizing information to remove redundancy and enhance data integrity.

In this technological day and age, we see data growing exponentially. If you’re working with RDBMSs, there’s no need to be concerned. The systems can handle vast amounts of information and offer exceptional speed and total control. Best of all, multiple users can access RDBMSs at a time and enhance your team’s efficiency, productivity, and collaboration.

Simply put, an RDBMS is a more advanced, powerful, and versatile version of DBMS. It offers speed, plenty of convenient features, and ease of use.

Examples of Popular RDBMS Software

As more and more companies recognize the advantages of using RDBMS, the availability of software grows by the day. Those who have tried several options agree that Oracle and MySQL are among the best choices.

Key Differences Between DBMS and RDBMS

Now that you’ve learned more about DBMS and RDBMS, you probably have an idea of the most significant differences between them. Here, we’ll summarize the key DBMS vs. RDBMS differences.

Data Storage and Organization

The first DBMS and RDBMS difference we’ll analyze is the way in which the systems store and organize information. With DBMS, data is stored and organized as files. This system uses either a hierarchical or navigational form to arrange the information. With DBMS, you can access only one element at a time, which can lead to slower processing.

On the other hand, RDBMS uses tables to store and display information. The data featured in several tables can be related to each other for ease of use and better organization. If you want to access multiple elements at the same time, you can; there are no constraints regarding this, as opposed to DBMS.

Data Integrity and Consistency

When discussing data integrity and consistency, it’s necessary to explain the concept of constraints in DBMS and RDBMS. Constraints are sets of “criteria” applied to data and/or operations within a system. When constraints are in place, only specific types of information can be displayed, and only specific operations can be completed. Sounds restricting, doesn’t it? The entire idea behind constraints is to enhance the integrity, consistency, and correctness of data displayed within a database.

DBMS lacks constraints. Hence, there’s no guarantee the data within this system is consistent or correct. Since there are no constraints, the risk of errors is higher.

RDBMS have constraints, resulting in the reliability and integrity of the data. Plus, normalization (removing redundancies) is another option that contributes to data integrity in RDBMS. Unfortunately, normalization can’t be achieved in DBMS.

Query Language and Data Manipulation

DBMS uses multiple query languages to manipulate data. However, none of these languages offer the speed and convenience present in RDBMS.

RDBMS manipulates data with structured query language (SQL). This language lets you retrieve, create, insert, or drop data within your relational database without difficulty.

Scalability and Performance

If you have a small company and/or don’t need to deal with vast amounts of data, a DBMS can be the way to go. But keep in mind that a DBMS can only be accessed by one person at a time. Plus, there’s no option to access more than one element at once.

With RDBMSs, scalability and performance are moved to a new level. An RDBMS can handle large amounts of information in a jiff. It also supports multiple users and allows you to access several elements simultaneously, thus enhancing your efficiency. This makes RDBMSs excellent for larger companies that work with large quantities of data.

Security and Access Control

Last but not least, an important difference between DBMS and RDBMS lies in security and access control. DBMSs have basic security features. Therefore, there’s a higher chance of breaches and data theft.

RDBMSs have various security measures in place that keep your data safe at all times.

Choosing the Right Database Management System

The first criterion that will help you make the right call is your project’s size and complexity. Small projects with relatively simple data are ideal for DBMSs. But if you’re tackling a lot of complex data, RDBMSs are the logical option.

Next, consider your budget and resources. Since they’re simpler, DBMSs are more affordable, in both aspects. RDBMSs are more complex, so naturally, the price of software is higher.

Finally, the factor that affects what option is the best for you is the desired functionality. What do you want from the program? Is it robust features or a simple environment with a few basic options? Your answer will guide you in the right direction.

Pros and Cons of DBMS and RDBMS

DBMS

Pros:

• Doesn’t involve complex query processing
• Cost-effective solution
• Ideal for processing small data
• Easy data handling via basic SQL queries

Cons:

• Doesn’t allow accessing multiple elements at once
• No way to relate data
• Doesn’t inherently support normalization
• Higher risk of security breaches
• Single-user system

RDBMS

Pros:

• Advanced, robust, and well-organized
• Ideal for large quantities of information
• Data from multiple tables can be related
• Multi-user system
• Supports normalization

Cons:

• More expensive
• Complex for some people

Examples of Use Cases

DBMS

DBMS is used in many sectors where more basic storing and management of data is required, be it sales and marketing, education, banking, or online shopping. For instance, universities use DBMS to store student-related data, such as registration details, fees paid, attendance, exam results, etc. Libraries use it to manage the records of thousands of books.

RDBMS

RDBMS is used in many industries today, especially those continuously requiring processing and storing large volumes of data. For instance, Airline companies utilize RDBMS for passenger and flight-related information and schedules. Human Resource departments use RDBMS to store and manage information related to employees and their payroll statistics. Manufacturers around the globe use RDBMS for operational data, inventory management and supply chain information.

Choose the Best Solution

An RDBM is a more advanced and powerful younger sibling of a DBMS. While the former offers more features, convenience, and the freedom to manipulate data as you please, it isn’t always the right solution. When deciding which road to take, prioritize your needs.

Tens of thousands of businesses go under every year. There are various culprits, but one of the most common causes is the inability of companies to streamline their customer experience. Many technologies have emerged to save the day, one of which is natural language processing (NLP).

But what is natural language processing? In simple terms, it’s the capacity of computers and other machines to understand and synthesize human language.

It may already seem like it would be important in the business world and trust us – it is. Enterprises rely on this sophisticated technology to facilitate different language-related tasks. Plus, it enables machines to read and listen to language as well as interact with it in many other ways.

The applications of NLP are practically endless. It can translate and summarize texts, retrieve information in a heartbeat, and help set up virtual assistants, among other things.

Looking to learn more about these applications? You’ve come to the right place. Besides use cases, this introduction to natural language processing will cover the history, components, techniques, and challenges of NLP.

History of Natural Language Processing

Before getting to the nuts and bolts of NLP basics, this introduction to NLP will first examine how the technology has grown over the years.

Early Developments in NLP

Some people revolutionized our lives in many ways. For example, Alan Turing is credited with several groundbreaking advancements in mathematics. But did you also know he paved the way for modern computer science, and by extension, natural language processing?

In the 1950s, Turing wanted to learn if humans could talk to machines via teleprompter without noticing a major difference. If they could, he concluded the machine would be capable of thinking and speaking.

Turin’s proposal has since been used to gauge this ability of computers and is known as the Turing Test.

Evolution of NLP Techniques and Algorithms

Since Alan Turing set the stage for natural language processing, many masterminds and organizations have built upon his research:

• 1958 – John McCarthy launched his Locator/Identifier Separation Protocol.
• 1964 – Joseph Wizenbaum came up with a natural language processing model called ELIZA.
• 1980s – IBM developed an array of NLP-based statistical solutions.
• 1990s – Recurrent neural networks took center stage.

The Role of Artificial Intelligence and Machine Learning in NLP

Discussing NLP without mentioning artificial intelligence and machine learning is like leaving a glass half empty. So, what’s the role of these technologies in NLP? It’s pivotal, to say the least.

AI and machine learning are the cornerstone of most NLP applications. They’re the engine of the NLP features that produce text, allowing NLP apps to turn raw data into usable information.

Key Components of Natural Language Processing

The phrase building blocks get thrown around a lot in the computer science realm. It’s key to understanding different parts of this sphere, including natural language processing. So, without further ado, let’s rifle through the building blocks of NLP.

Syntax Analysis

An NLP tool without syntax analysis would be lost in translation. It’s a paramount stage since this is where the program extracts meaning from the provided information. In simple terms, the system learns what makes sense and what doesn’t. For instance, it rejects contradictory pieces of data close together, such as “cold Sun.”

Semantic Analysis

Understanding someone who jumbles up words is difficult or impossible altogether. NLP tools recognize this problem, which is why they undergo in-depth semantic analysis. The network hits the books, learning proper grammatical structures and word orders. It also determines how to connect individual words and phrases.

Pragmatic Analysis

A machine that relies only on syntax and semantic analysis would be too machine-like, which goes against Turing’s principles. Salvation comes in the form of pragmatic analysis. The NLP software uses knowledge outside the source (e.g., textbook or paper) to determine what the speaker actually wants to say.

Discourse Analysis

When talking to someone, there’s a point to your conversation. An NLP system is just like that, but it needs to go through extensive training to achieve the same level of discourse. That’s where discourse analysis comes in. It instructs the machine to use a coherent group of sentences that have a similar or the same theme.

Speech Recognition and Generation

Once all the above elements are perfected, it’s blast-off time. The NLP has everything it needs to recognize and generate speech. This is where the real magic happens – the system interacts with the user and starts using the same language. If each stage has been performed correctly, there should be no significant differences between real speech and NLP-based applications.

Natural Language Processing Techniques

Different analyses are common for most (if not all) NLP solutions. They all point in one direction, which is recognizing and generating speech. But just like Google Maps, the system can choose different routes. In this case, the routes are known as NLP techniques.

Rule-Based Approaches

Rule-based approaches might be the easiest NLP technique to understand. You feed your rules into the system, and the NLP tool synthesizes language based on them. If input data isn’t associated with any rule, it doesn’t recognize the information – simple as that.

Statistical Methods

If you go one level up on the complexity scale, you’ll see statistical NLP methods. They’re based on advanced calculations, which enable an NLP platform to predict data based on previous information.

Neural Networks and Deep Learning

You might be thinking: “Neural networks? That sounds like something out of a medical textbook.” Although that’s not quite correct, you’re on the right track. Neural networks are NLP techniques that feature interconnected nodes, imitating neural connections in your brain.

Deep learning is a sub-type of these networks. Basically, any neural network with at least three layers is considered a deep learning environment.

Transfer Learning and Pre-Trained Language Models

The internet is like a massive department store – you can find almost anything that comes to mind here. The list includes pre-trained language models. These models are trained on enormous quantities of data, eliminating the need for you to train them using your own information.

Transfer learning draws on this concept. By tweaking pre-trained models to accommodate a particular project, you perform a transfer learning maneuver.

Applications of Natural Language Processing

With so many cutting-edge processes underpinning NLP, it’s no surprise it has practically endless applications. Here are some of the most common natural language processing examples:

• Search engines and information retrieval – An NLP-based search engine understands your search intent to retrieve accurate information fast.
• Sentiment analysis and social media monitoring – NLP systems can even determine your emotional motivation and uncover the sentiment behind social media content.
• Machine translation and language understanding – NLP software is the go-to solution for fast translations and understanding complex languages to improve communication.
• Chatbots and virtual assistants – A state-of-the-art NLP environment is behind most chatbots and virtual assistants, which allows organizations to enhance customer support and other key segments.
• Text summarization and generation – A robust NLP infrastructure not only understands texts but also summarizes and generates texts of its own based on your input.

Challenges and Limitations of Natural Language Processing

Natural language processing in AI and machine learning is mighty but not almighty. There are setbacks to this technology, but given the speedy development of AI, they can be considered a mere speed bump for the time being:

• Ambiguity and complexity of human language – Human language keeps evolving, resulting in ambiguous structures NLP often struggles to grasp.
• Cultural and contextual nuances – With approximately 4,000 distinct cultures on the globe, it’s hard for an NLP system to understand the nuances of each.
• Data privacy and ethical concerns – As every NLP platform requires vast data, the methods for sourcing this data tend to trigger ethical concerns.
• Computational resources and computing power – The more polished an NLP tool becomes, the greater the computing power must be, which can be hard to achieve.

The Future of Natural Language Processing

The final part of our take on natural language processing in artificial intelligence asks a crucial question: What does the future hold for NLP?

• Advancements in artificial intelligence and machine learning – Will AI and machine learning advancements help NLP understand more complex and nuanced languages faster?
• Integration of NLP with other technologies – How well will NLP integrate with other technologies to facilitate personal and corporate use?
• Personalized and adaptive language models – Can you expect developers to come up with personalized and adaptive language models to accommodate those with speech disorders better?
• Ethical considerations and guidelines for NLP development – How will the spearheads of NLP development address ethical problems if the technology requires more and more data to execute?

The Potential of Natural Language Processing Is Unrivaled

It’s hard to find a technology that’s more important for today’s businesses and society as a whole than natural language processing. It streamlines communication, enabling people from all over the world to connect with each other.

The impact of NLP will amplify if the developers of this technology can address the above risks. By honing the software with other platforms while minimizing privacy issues, they can dispel any concerns associated with it.

If you want to learn more about NLP, don’t stop here. Use these natural language processing notes as a stepping stone for in-depth research. Also, consider an NLP course to gain a deep understanding of this topic.

In a database, you have entities (which have attributes), and relationships between those entities. Managing them is key to preventing chaos from engulfing your database, which is where the concept of keys comes in. These unique identifiers enable you to pick specific rows in an entity set, as well as define their relationships to rows in other entity sets, allowing your database to handle complex computations.

Let’s explore keys in DBMS (database management systems) in more detail, before digging into everything you need to know about the most important keys – primary keys.

Understanding Keys in DBMS

Keys in DBMS are attributes that you use to identify specific rows inside a table, in addition to finding the relation between two tables. For example, let’s say you have a table for students, with that table recording each student’s “ID Number,” “Name,” “Address,” and “Teacher” as attributes. If you want to identify a specific student in the table, you’ll need to use one of these attributes as a key that allows you to pull the student’s record from your database. In this case “ID Number” is likely the best choice because it’s a unique attribute that only applies to a single student.

Types of Keys in DBMS

Beyond the basics of serving as unique identifiers for rows in a database, keys in DBMS can take several forms:

• Primary Keys – An attribute that is present in the table for all of the records it contains, with each instance of that attribute being unique to the record. The previously-mentioned “ID Number” for students is a great example, as no student can have the same number as another student.
• Foreign Key – Foreign keys allow you to define and establish relationships between a pair of tables. If Table A needs to refer to the primary key in Table B, you’ll use a foreign key in Table A so you have values in that table to match those in Table B.
• Unique Key – These are very similar to primary keys in that both contain unique identifiers for the records in a table. The only difference is that a unique key can contain a null value, whereas a primary key can’t.
• Candidate Key – Though you may have picked a unique attribute to serve as your primary key, there may be other candidates within a table. Coming back to the student example, you may record the phone numbers and email addresses of your students, which can be as unique as the student ID assigned to the individual. These candidate keys are also unique identifiers, allowing them to be used in tandem with a primary key to identify a specific row in a table.
• Composite Key – If you have attributes that wouldn’t be unique when taken alone, but can be combined to form a unique identifier for a record, you have a composite key.
• Super Key – This term refers to the collection of attributes that uniquely identify a record, meaning it’s a combination of candidate keys. Just like an employer sifting through job candidates to find the perfect person, you’ll sift through your super key set to choose the ideal primary key amongst your candidate keys.

So, why are keys in DBMS so important?

Keys ensure you maintain data integrity across all of the tables that make up your database. Without them, the relationships between each table become messy hodgepodges, creating the potential for duplicate records and errors that deliver inaccurate reports from the database. Having unique identifiers (in the form of keys) allows you to be certain that any record you pull, and the relationships that apply to that record, are accurate and unrepeated.

Primary Key Essentials

As mentioned, any unique attribute in a table can serve as a primary key, though this doesn’t mean that every unique attribute is a great choice. The following characteristics help you to define the perfect primary key.

Uniqueness

If your primary key is repeatable across records, it can’t serve as a unique identifier for a single record. For example, our student table may have multiple people named “John,” so you can’t use the “Name” attribute to find a specific student. You need something unique to that student, such as the previously mentioned ID number.

Non-Null Values

Primary keys must always contain a value, else you risk losing records in a table because you have no way of calling upon them. This need for non-null values can be used to eliminate some candidates from primary key content. For instance, it’s feasible (though unlikely) that a student won’t have an email address, creating the potential for null values that mean the email address attribute can’t be a primary key.

Immutability

A primary key that can change over time is a key that can cause confusion. Immutability is the term used for any attribute that’s unchanging to the point where it’s an evergreen attribute that you can use to identify a specific record forever.

Minimal

Ideally, one table should have one attribute that serves as its primary key, which is where the term “minimal” comes in. It’s possible for a table to have a composite or super key set, though both create the possibility of confusion and data integrity issues.

The Importance of a Primary Key in DBMS

We can distill the reason why having a primary key in DBMS for each of your tables is important into the following reasons:

• You can use a primary key to identify each unique record in a table, meaning no multi-result returns to your database searches.
• Having a primary key means a record can’t be repeated in the table.
• Primary keys make data retrieval more efficient because you can use a single attribute for searches rather than multiple.

Functions of Primary Keys

Primary keys in DBMS serve several functions, each of which is critical to your DBMS.

Data Identification

Imagine walking into a crowded room and shouting out a name. The odds are that several people (all of whom have the same name) will turn their heads to look at you. That’s basically what you’re doing if you try to pull records from a table without using a primary key.

A primary key in DBMS serves as a unique identifier that you can use to pull specific records. Coming back to the student example mentioned earlier, a “Student ID” is only applicable to a single student, making it a unique identifier you can use to find that student in your database.

Ensure Data Integrity

Primary keys protect data integrity in two ways.

First, they prevent duplicate records from building up inside a single table, ensuring you don’t get multiple instances of the same record. Second, they ensure referential integrity, which is the term used to describe what happens when one table in your database needs to refer to the records stored in another table.

For example, let’s say you have tables for “Students” and “Teachers” in your database. The primary keys assigned to your students and teachers allow you to pull individual records as needed from each table. But every “Teacher” has multiple “Students” in their class. So, your primary key from the “Students” table is used as a foreign key in the “Teachers” table, allowing you to denote the one-to-many relationship between a teacher and their class of students. That foreign key also ensures referential integrity because it contains the unique identifiers for students, which you can look up in your “Students” table.

Data Retrieval

If you need to pull a specific record from a table, you can’t rely on attributes that can repeat across several records in that table. Again, the “Name” example highlights the problem here, as several people could have the same name. You need a unique identifier for each record so you can retrieve a single record from a huge set without having to pore through hundreds (or even thousands) of records.

Best Practices for Primary Key Selection

Now that you understand how primary keys in DBMS work, here are some best practices for selecting the right primary key for your table:

• Choose Appropriate Attributes as Candidates – If the attribute isn’t unique to each record, or it can contain a null value (as is the case with email addresses and phone numbers), it’s not a good candidate for a primary key.
• Avoid Using Sensitive Information – Using personal or sensitive information as a primary key creates a security risk because anybody who cracks your database could use that information for other purposes. Make your primary keys unique, and only applicable, to your database, which allows you to encrypt any sensitive information stored in your tables.
• Consider Surrogate Keys – Some tables don’t have natural attributes that you can use as primary keys. In these cases, you can create a primary key out of thin air and assign it to each record. The “Student ID” referenced earlier is a great example, as students entering a school don’t come with their own ID numbers. Those numbers are given to the student (or simply used in the database that collects their data), making them surrogate keys.
• Ensure Primary Key Stability – Any attribute that can change isn’t suitable for use as a primary key because it causes stability issues. Names, email addresses, phone numbers, and even bank account details are all things that can change, making them unsuitable. Evergreen and unchanging is the way to go with primary keys.

Choose the Right Keys for Your Database

You need to understand the importance of a primary key in DBMS (or multiple primary keys when you have several tables) so you can define the relationships between tables and identify unique records inside your tables. Without primary keys, you’ll find it much harder to run reports because you won’t feel confident in the accuracy of the data returned. Each search may pull up duplicate or incorrect records because of a lack of unique identifiers.

Thankfully, many of the tables you create will have attributes that lend themselves well to primary key status. And even when that isn’t the case, you can use surrogate keys in DBMS to assign primary keys to your tables. Experiment with your databases, testing different potential primary keys to see what works best for you.

The human brain is among the most complicated organs and one of nature’s most amazing creations. The brain’s capacity is considered limitless; there isn’t a thing it can’t remember. Although many often don’t think about it, the processes that happen in the mind are fascinating.

As technology evolved over the years, scientists figured out a way to make machines think like humans, and this process is called machine learning. Like cars need fuel to operate, machines need data and algorithms. With the application of adequate techniques, machines can learn from this data and even improve their accuracy as time passes.

Two basic machine learning approaches are supervised and unsupervised learning. You can already assume the biggest difference between them based on their names. With supervised learning, you have a “teacher” who shows the machine how to analyze specific data. Unsupervised learning is completely independent, meaning there are no teachers or guides.

This article will talk more about supervised and unsupervised learning, outline their differences, and introduce examples.

Supervised Learning

Imagine a teacher trying to teach their young students to write the letter “A.” The teacher will first set an example by writing the letter on the board, and the students will follow. After some time, the students will be able to write the letter without assistance.

Supervised machine learning is very similar to this situation. In this case, you (the teacher) train the machine using labeled data. Such data already contains the right answer to a particular situation. The machine then uses this training data to learn a pattern and applies it to all new datasets.

Note that the role of a teacher is essential. The provided labeled datasets are the foundation of the machine’s learning process. If you withhold these datasets or don’t label them correctly, you won’t get any (relevant) results.

Supervised learning is complex, but we can understand it through a simple real-life example.

Suppose you have a basket filled with red apples, strawberries, and pears and want to train a machine to identify these fruits. You’ll teach the machine the basic characteristics of each fruit found in the basket, focusing on the color, size, shape, and other relevant features. If you introduce a “new” strawberry to the basket, the machine will analyze its appearance and label it as “strawberry” based on the knowledge it acquired during training.

Types of Supervised Learning

You can divide supervised learning into two types:

• Classification – You can train machines to classify data into categories based on different characteristics. The fruit basket example is the perfect representation of this scenario.
• Regression – You can train machines to use specific data to make future predictions and identify trends.

Supervised Learning Algorithms

Supervised learning uses different algorithms to function:

• Linear regression – It identifies a linear relationship between an independent and a dependent variable.
• Logistic regression – It typically predicts binary outcomes (yes/no, true/false) and is important for classification purposes.
• Support vector machines – They use high-dimensional features to map data that can’t be separated by a linear line.
• Decision trees – They predict outcomes and classify data using tree-like structures.
• Random forests – They analyze several decision trees to come up with a unique prediction/result.
• Neural networks – They process data in a unique way, very similar to the human brain.

Supervised Learning: Examples and Applications

There’s no better way to understand supervised learning than through examples. Let’s dive into the real estate world.

Suppose you’re a real estate agent and need to predict the prices of different properties in your city. The first thing you’ll need to do is feed your machine existing data about available houses in the area. Factors like square footage, amenities, a backyard/garden, the number of rooms, and available furniture, are all relevant factors. Then, you need to “teach” the machine the prices of different properties. The more, the better.

A large dataset will help your machine pick up on seemingly minor but significant trends affecting the price. Once your machine processes this data and you introduce a new property to it, it will be able to cross-reference its features with the existing database and come up with an accurate price prediction.

The applications of supervised learning are vast. Here are the most popular ones:

• Sales – Predicting customers’ purchasing behavior and trends
• Finance – Predicting stock market fluctuations, price changes, expenses, etc.
• Healthcare – Predicting risk of diseases and infections, surgery outcomes, necessary medications, etc.
• Weather forecasts – Predicting temperature, humidity, atmospheric pressure, wind speed, etc.
• Face recognition – Identifying people in photos

Unsupervised Learning

Imagine a family with a baby and a dog. The dog lives inside the house, so the baby is used to it and expresses positive emotions toward it. A month later, a friend comes to visit, and they bring their dog. The baby hasn’t seen the dog before, but she starts smiling as soon as she sees it.

Why?

Because the baby was able to draw her own conclusions based on the new dog’s appearance: two ears, tail, nose, tongue sticking out, and maybe even a specific noise (barking). Since the baby has positive emotions toward the house dog, she also reacts positively to a new, unknown dog.

This is a real-life example of unsupervised learning. Nobody taught the baby about dogs, but she still managed to make accurate conclusions.

With supervised machine learning, you have a teacher who trains the machine. This isn’t the case with unsupervised learning. Here, it’s necessary to give the machine freedom to explore and discover information. Therefore, this machine learning approach deals with unlabeled data.

Types of Unsupervised Learning

There are two types of unsupervised learning:

• Clustering – Grouping uncategorized data based on their common features.
• Dimensionality reduction – Reducing the number of variables, features, or columns to capture the essence of the available information.

Unsupervised Learning Algorithms

Unsupervised learning relies on these algorithms:

• K-means clustering – It identifies similar features and groups them into clusters.
• Hierarchical clustering – It identifies similarities and differences between data and groups them hierarchically.
• Principal component analysis (PCA) – It reduces data dimensionality while boosting interpretability.
• Independent component analysis (ICA) – It separates independent sources from mixed signals.
• T-distributed stochastic neighbor embedding (t-SNE) – It explores and visualizes high-dimensional data.

Unsupervised Learning: Examples and Applications

Let’s see how unsupervised learning is used in customer segmentation.

Suppose you work for a company that wants to learn more about its customers to build more effective marketing campaigns and sell more products. You can use unsupervised machine learning to analyze characteristics like gender, age, education, location, and income. This approach is able to discover who purchases your products more often. After getting the results, you can come up with strategies to push the product more.

Unsupervised learning is often used in the same industries as supervised learning but with different purposes. For example, both approaches are used in sales. Supervised learning can accurately predict prices relying on past data. On the other hand, unsupervised learning analyzes the customers’ behaviors. The combination of the two approaches results in a quality marketing strategy that can attract more buyers and boost sales.

Another example is traffic. Supervised learning can provide an ETA to a destination, while unsupervised learning digs a bit deeper and often looks at the bigger picture. It can analyze a specific area to pinpoint accident-prone locations.

Differences Between Supervised and Unsupervised Learning

These are the crucial differences between the two machine learning approaches:

• Data labeling – Supervised learning uses labeled datasets, while unsupervised learning uses unlabeled, “raw” data. In other words, the former requires training, while the latter works independently to discover information.
• Algorithm complexity – Unsupervised learning requires more complex algorithms and powerful tools that can handle vast amounts of data. This is both a drawback and an advantage. Since it operates on complex algorithms, it’s capable of handling larger, more complicated datasets, which isn’t a characteristic of supervised learning.
• Use cases and applications – The two approaches can be used in the same industries but with different purposes. For example, supervised learning is used in predicting prices, while unsupervised learning is used in detecting customers’ behavior or anomalies.
• Evaluation metrics – Supervised learning tends to be more accurate (at least for now). Machines still require a bit of our input to display accurate results.

Choose Wisely

Do you need to teach your machine different data, or can you trust it to handle the analysis on its own? Think about what you want to analyze. Unsupervised and supervised learning may sound similar, but they have different uses. Choosing an inadequate approach leads to unreliable, irrelevant results.

Supervised learning is still more popular than unsupervised learning because it offers more accurate results. However, this approach can’t handle larger, complex datasets and requires human intervention, which isn’t the case with unsupervised learning. Therefore, we may see a rise in the popularity of the unsupervised approach, especially as the technology evolves and enables more accuracy.

When you first get into modern computing, one of the terms that comes up most frequently is relational databases. These are clusters that are organized in such a way that they effortlessly find links between connected data points.

Relational databases are convenient, but what happens when you deal with vast amounts of information? You need something to act as your North Star, guiding you through the network and allowing you to stay on top of the data.

That something is an RDBMS. According to Google, RDBMS stands for a relational database management system – software that sets up and manages relational databases. In its full form, it’s been the light at the end of the tunnel for thousands of companies due to its accuracy, security, and ease of use.

The definition and importance of RDBMSs are the tip of the iceberg when it comes to these systems. This introduction to RDBMS will delve a bit deeper by taking a closer look at the concept of RDBMS, the history of this technology, use cases, and the most common examples.

History of RDBMS

The concept of RDBMS might be shrouded in mystery for some. Thus, several questions may come up when discussing the notion, including one as basic as “What is RDBMS?”

Knowing the RDBMS definition is a great starting point on your journey to understanding this concept. But let’s take a few steps back and delve into the history of this system.

Origins of the Relational Model

What if we told you that the RDBMS concepts are older than the internet? It may sound surprising, but it’s true.

The concept of RDBMS was developed by Edgar F. Codd 43 years ago. He aimed to propose a more efficient way to store information, a method that would consume drastically less memory than anything at the time. His model was groundbreaking, to say the least.

E.F. Codd’s Paper on Relational Model

Codd laid down his proposal in a 1970s paper called “A Relational Model of Data for Large Shared Data Banks.” He advocated a database solution comprised of intertwined tables. These tables enabled the user to keep their information compact, lowering the amount of disk space necessary for storage (which was scarce at the time).

The rest is history. The public welcomed Codd’s model with open arms since it optimized storage requirements and allowed people to answer practically any question using his principle.

Development of SQL

Codd’s research paved the way for relational database management systems, the most famous of which is SQL. This programming language was also developed in the ‘70s and was originally named SEQUEL (Structured English Query Language). It was quickly implemented across the computing industry and grew more powerful as the years went by.

Evolution of RDBMS Software

The evolution of RDBMS software has been fascinating.

Early RDBMS Software

The original RDBMS software was powerful, but it wasn’t a cure-all. It was a match made in heaven for users dealing with structured data, allowing them to organize it with minimal effort. However, pictures, music, and other forms of unstructured information were largely incompatible with this model.

Modern RDBMS Software

Today’s RDBMS solutions have come a long way from their humble beginnings. A modern relational DBMS can process different forms of information with ease. Programs like MySQL are versatile, adaptable, and easy to set up, helping database professionals spearhead the development of practically any application.

Key Concepts in RDBMS

Here’s another request you may have for an expert in RDBMS – explain the most significant relational database concepts. If that’s your question, your request has been granted. Coming up is an overview of RDBMS concepts that explain RDBMS in simple terms.

Tables and Relations

Tables and relations are the bread and butter of all relational database management systems. They sound straightforward, but they’re much different from, say, elements you come across in Microsoft Excel.

Definition of Tables

Tables are where data is stored in an RDBMS. They’re comprised of rows and columns for easier organization.

Definition of Relations

Relations are the links between tables. There can be several types of relations, such as one-to-one connections. This form means a data point from one table only matches one data point from another table.

Primary and Foreign Keys

No discussion about RDBMS solutions is complete without primary and foreign keys.

Definition of Primary Keys

A primary key is the unique element of each table that defines the table’s rows. The number of primary keys in a table is limited to one.

Definition of Foreign Keys

Foreign keys are used to form an inextricable bond between tables. They always refer to the primary key of another table.

Normalization

Much of database management is akin to separating wheat from the chaff. One of the processes that allow you to do so is normalization.

Purpose of Normalization

Normalization is about restoring (or creating) order in a database. It’s the procedure of eradicating unnecessary data for the purpose of cleaner tables and smoother management.

Normal Forms

Every action has its reaction. For example, the reaction of normalization is normal forms. These are forms of data that are free from redundant or duplicate information, making them easily accessible.

Popular RDBMS Software

This article has dissected basic relational database concepts, the RDBMS meaning, and RDBMS full form. To further shed light on the technology, take a look at the crème de la crème of RDBMS platforms.

Oracle Database

If you want to make headway in the database management industry, Oracle Database can be one of your best friends.

Overview of Oracle Database

Oracle Database is the most famous RDBMS around. The very database of this network is called Oracle, and the software comes in five different versions. Each rendition has a specific set of features and benefits, but some perks hold true for each one.

Key Features and Benefits

• Highly secure – Oracle employs top-grade security measures.
• Scalable – The system supports company growth with adaptable features.
• Available – You can tap into the architecture whenever necessary for seamless adjustments.

Microsoft SQL Server

Let’s see what another powerhouse – Microsoft SQL Server – brings to the table.

Overview of Microsoft SQL Server

Microsoft SQL Server is a reliable RDBMS with admirable capabilities. Like Oracle, it’s available in a range of editions to target different groups, including personal and enterprise users.

Key Features and Benefits

• Fast – Few systems rival the speed of Microsoft SQL Server.
• Versatile – The network supports on-premise and cloud applications.
• Affordable – You won’t burn a hole in your pocket if you buy the standard version.

MySQL

You can take your business to new heights with MySQL. The following section will explore what makes this RDBMS a go-to pick for Uber, Slack, and many other companies.

Overview of MySQL

MySQL is another robust RDBMS that enables fast data retrieval. It’s an open-source solution, making it less complex than some other platforms.

Key Features and Benefits

• Quick – Efficient memory use speeds up the MySQL environment.
• Secure – Bulletproof password systems safeguard against hacks.
• Scalable – You can use MySQL both for small and large data sets.

PostgreSQL

Last but not least, PostgreSQL is a worthy contender for the best RDBMS on the market.

Overview of PostgreSQL

If you need a long-running RDBMS, you can’t go wrong with PostgreSQL. It’s an open-source solution that’s received more than two decades’ worth of refinement.

Key Features and Benefits

• Nested transactions – These elements deliver higher concurrency control.
• Anti-hack environment – Advanced locking features keep cybercriminals at bay.
• Table inheritance – This feature makes the network more consistent.

RDBMS Use Cases

Now we get to what might be the crux of the RDBMS discussion: Where can you implement these convenient solutions?

Data Storage and Retrieval

• Storing large amounts of structured data – Use an RDBMS to keep practically unlimited structured data.
• Efficient data retrieval – Retrieve data in a split second with an RDBMS.

Data Analysis and Reporting

• Analyzing data for trends and patterns – Discover customer behavior trends with a robust RDBMS.
• Generating reports for decision-making – Facilitate smart decision-making with RDBMS-generated reports.

Application Development

• Backend for web and mobile applications – Develop a steady web and mobile backend architecture with your RDBMS.
• Integration with other software and services – Combine an RDBMS with other programs to elevate its functionality.

RDBMS vs. NoSQL Database

Many alternatives to RDBMS have sprung up, including NoSQL databases. But what makes these two systems different?

Overview of NoSQL Databases

A NoSQL database is the stark opposite of RDBMS solutions. It takes a non-relational approach, which is deemed more efficient by many.

Key Differences Between RDBMS and NoSQL Databases

• Data model – RDBMSs store structured data, whereas NoSQL databases store unstructured information.
• Scalability – NoSQL is more scalable because it doesn’t require a fixed schema (relation-based model).
• Consistency – RDBMSs achieve consistency through rules, while NoSQL models feature eventual consistency.

Choosing the Right Database for Your Needs

Keep these guidelines in mind when selecting your database platform:

• Use an RDBMS for centralized apps and NoSQL for decentralized solutions.
• Use an RDBMS for structured data and NoSQL for unstructured data.
• Use an RDBMS for moderate data activity and NoSQL for high data activity.

Exploring the Vast Utility of RDBMS

If you’re looking for a descriptive answer to the “what is relational database management system question,” here it is – it is the cornerstone of database management for countless enterprises. It’s ideal for structured data projects and gives the user the reins of data management. Plus, it’s as secure as it gets.

The future looks even more promising. Database professionals are expected to rely more on blockchain technology and cloud storage to elevate the efficacy of RDBMS.