Have you ever played chess or checkers against a computer? If you have, news flash – you’ve watched artificial intelligence at work. But what if the computer could get better at the game on its own just by playing more and analyzing its mistakes? That’s the power of machine learning, a type of AI that lets computers learn and improve from experience.

In fact, machine learning is becoming increasingly important in our daily lives. According to a report by Statista, revenues from the global market for AI software are expected to reach 126 billion by 2025, up from just 10.1 billion in 2018. From personalized recommendations on Netflix to self-driving cars, machine learning is powering some of the most innovative and exciting technologies of our time.

But how does it all work? In this article, we’ll dive into the concepts of machine learning and explore how it’s changing the way we interact with technology.

What is Machine Learning?

Machine learning is a subset of artificial intelligence (AI) that focuses on building algorithms that can learn from data and then make predictions or decisions and recognize patterns. Essentially, it’s all about creating computer programs that can adapt and improve on their own without being explicitly programmed for every possible scenario.

It’s like teaching a computer to see the world through a different lens. From the data, the machine identifies patterns and relationships within it. Based on these patterns, the algorithm can make predictions or decisions about new data it hasn’t seen before.

Because of these qualities, machine learning has plenty of practical applications. We can train computers to make decisions, recognize speech, and even generate art. We can use it in fraud detection in financial transactions or to improve healthcare outcomes through personalized medicine.

Machine learning also plays a large role in fields like computer vision, natural language processing, and robotics, as they require the ability to recognize patterns and make predictions to complete various tasks.

Concepts of Machine Learning

Machine learning might seem magical, but the concepts of machine learning are complex, with many layers of algorithms and techniques working together to get to an end goal.

From supervised and unsupervised learning to deep neural networks and reinforcement learning, there are many base concepts to understand before diving into the world of machine learning. Get ready to explore some machine learning basics!

Supervised Learning

Supervised learning involves training the algorithm to recognize patterns or make predictions using labeled data.

• Classification: Classification is quite straightforward, evident by its name. Its goal is to predict which category or class new data belongs to based on existing data.
• Logistic Regression: Logistic regression aims to predict a binary outcome (i.e., yes or no) based on one or more input variables.
• Support Vector Machines: Support Vector Machines (SVMs) find the best way to separate data points into different categories or classes based on their features or attributes.
• Decision Trees: Decision trees make decisions by dividing data into smaller and smaller subsets from a number of binary decisions. You can think of it like a game of 20 questions where you’re narrowing things down.
• Naive Bayes: Naive Bayes uses Bayes’ theorem to predict how likely it is to end up with a certain result when different input variables are present or absent.

Regression

Regression is a type of machine learning that helps us predict numerical values, like prices or temperatures, based on other data that we have. It looks for patterns in the data to create a mathematical model that can estimate the value we are looking for.

• Linear Regression: Linear regression helps us predict numerical values by fitting a straight line to the data.
• Polynomial Regression: Polynomial regression is similar to linear regression, but instead of fitting a straight line to the data, it fits a curved line (a polynomial) to capture more complex relationships between the variables. Linear regression might be used to predict someone’s salary based on their years of experience, while polynomial regression could be used to predict how fast a car will go based on its engine size.
• Support Vector Regression: Support vector regression finds the best fitting line to the data while minimizing errors and avoiding overfitting (becoming too attuned to the existing data).
• Decision Tree Regression: Decision tree regression uses a tree-like template to make predictions out of a series of decision rules, where each branch represents a decision, and each leaf node represents a prediction.

Unsupervised Learning

Unsupervised learning is where the computer algorithm is given a bunch of data with no labels and has to find patterns or groupings on its own, allowing for discovering hidden insights and relationships.

• Clustering: Clustering groups similar data points together based on their features.
• K-Means: K-Means is a popular clustering algorithm that separates the data into a predetermined number of clusters by finding the average of each group.
• Hierarchical Clustering: Hierarchical clustering is another way of grouping that creates a hierarchy of clusters by either merging smaller clusters into larger ones (agglomerative) or dividing larger clusters into smaller ones (divisive).
• Expectation Maximization: Expectation maximization is quite self-explanatory. It’s a way to find patterns in data that aren’t clearly grouped together by guessing what might be there and refining the guesses over time.
• Association Rule Learning: Association Rule Learning looks to find interesting connections between things in large sets of data, like discovering that people who buy plant pots often also buy juice.
• Apriori: Apriori is an algorithm for association rule learning that finds frequent itemsets (groups of items that appear together often) and makes rules that describe the relationships between them.
• Eclat: Eclat is similar to apriori, but it works by first finding which things appear together most often and then finding frequent itemsets out of those. It’s a method that works better for larger datasets.

Reinforcement Learning

Reinforcement learning is like teaching a computer to play a game by letting it try different actions and rewarding it when it does something good so it learns how to maximize its score over time.

• Q-Learning: Q-Learning helps computers learn how to take actions in an environment by assigning values to each possible action and using those values to make decisions.
• SARSA: SARSA is similar to Q-Learning but takes into account the current state of the environment, making it more useful in situations where actions have immediate consequences.
• DDPG (Deep Deterministic Policy Gradient): DDPG is a more advanced type of reinforcement learning that uses neural networks to learn policies for continuous control tasks, like robotic movement, by mapping what it sees to its next action.

Deep Learning Algorithms

Deep Learning is a powerful type of machine learning that’s inspired by how the human brain works, using artificial neural networks to learn and make decisions from vast amounts of data.

It’s more complex than other types of machine learning because it involves many layers of connections that can learn to recognize complex patterns and relationships in data.

• Neural Networks: Neural networks mimic the structure and function of the human brain, allowing them to learn from and make predictions about complex data.
• Convolutional Neural Networks: Convolutional neural networks are particularly good at image recognition, using specialized layers to detect features like edges, textures, and shapes.
• Recurrent Neural Networks: Recurrent neural networks are known to be good at processing sequential data, like language or music, by keeping track of previous inputs and using that information to make better predictions.
• Generative Adversarial Networks: Generative adversarial networks can generate new, original data by pitting two networks against each other. One tries to create fake data, and the other tries to spot the fakes until the generator network gets really good at making convincing fakes.

Conclusion

As we’ve learned, machine learning is a powerful tool that can help computers learn from data and make predictions, recognize patterns, and even create new things.

With basic concepts like supervised and unsupervised learning, regression and clustering, and advanced techniques like deep learning and neural networks, the possibilities for what we can achieve with machine learning are endless.

So whether you’re new to the subject or deeper down the iceberg, there’s always something new to learn in the exciting field of machine learning!

Data is the heartbeat of the digital realm. And when something is so important, you want to ensure you deal with it properly. That’s where data structures come into play.

But what is data structure exactly?

In the simplest terms, a data structure is a way of organizing data on a computing machine so that you can access and update it as quickly and efficiently as possible. For those looking for a more detailed data structure definition, we must add processing, retrieving, and storing data to the purposes of this specialized format.

With this in mind, the importance of data structures becomes quite clear. Neither humans nor machines could access or use digital data without these structures.

But using data structures isn’t enough on its own. You must also use the right data structure for your needs.

This article will guide you through the most common types of data structures, explain the relationship between data structures and algorithms, and showcase some real-world applications of these structures.

Armed with this invaluable knowledge, choosing the right data structure will be a breeze.

Types of Data Structures

Like data, data structures have specific characteristics, features, and applications. These are the factors that primarily dictate which data structure should be used in which scenario. Below are the most common types of data structures and their applications.

Primitive Data Structures

Take one look at the name of this data type, and its structure won’t surprise you. Primitive data structures are to data what cells are to a human body – building blocks. As such, they hold a single value and are typically built into programming languages. Whether you check data structures in C or data structures in Java, these are the types of data structures you’ll find.

• Integer (signed or unsigned) – Representing whole numbers
• Float (floating-point numbers) – Representing real numbers with decimal precision
• Character – Representing integer values as symbols
• Boolean – Storing true or false logical values

Non-Primitive Data Structures

Combine primitive data structures, and you get non-primitive data structures. These structures can be further divided into two types.

Linear Data Structures

As the name implies, a linear data structure arranges the data elements linearly (sequentially). In this structure, each element is attached to its predecessor and successor.

The most commonly used linear data structures (and their real-life applications) include the following:

• In arrays, multiple elements of the same type are stored together in the same location. As a result, they can all be processed relatively quickly. (library management systems, ticket booking systems, mobile phone contacts, etc.)
• Linked lists. With linked lists, elements aren’t stored at adjacent memory locations. Instead, the elements are linked with pointers indicating the next element in the sequence. (music playlists, social media feeds, etc.)
• These data structures follow the Last-In-First-Out (LIFO) sequencing order. As a result, you can only enter or retrieve data from one stack end (browsing history, undo operations in word processors, etc.)
• Queues follow the First-In-First-Out (FIFO) sequencing order (website traffic, printer task scheduling, video queues, etc.)

Non-Linear Data Structures

A non-linear data structure also has a pretty self-explanatory name. The elements aren’t placed linearly. This also means you can’t traverse all of them in a single run.

• Trees are tree-like (no surprise there!) hierarchical data structures. These structures consist of nodes, each filled with specific data (routers in computer networks, database indexing, etc.)
• Combine vertices (or nodes) and edges, and you get a graph. These data structures are used to solve the most challenging programming problems (modeling, computation flow, etc.)

Advanced Data Structures

Venture beyond primitive data structures (building blocks for data structures) and basic non-primitive data structures (building blocks for more sophisticated applications), and you’ll reach advanced data structures.

• Hash tables. These advanced data structures use hash functions to store data associatively (through key-value pairs). Using the associated values, you can quickly access the desired data (dictionaries, browser searching, etc.)
• Heaps are specialized tree-like data structures that satisfy the heap property (every tree element is larger than its descendant.)
• Tries store strings that can be organized in a visual graph and retrieved when necessary (auto-complete function, spell checkers, etc.)

Algorithms for Data Structures

There is a common misconception that data structures and algorithms in Java and other programming languages are one and the same. In reality, algorithms are steps used to structure data and solve other problems. Check out our overview of some basic algorithms for data structures.

Searching Algorithms

Searching algorithms are used to locate specific elements within data structures. Whether you’re searching for specific data structures in C++ or another programming language, you can use two types of algorithms:

• Linear search: starts from one end and checks each sequential element until the desired element is located
• Binary search: looks for the desired element in the middle of a sorted list of items (If the elements aren’t sorted, you must do that before a binary search.)

Sorting Algorithms

Whenever you need to arrange elements in a specific order, you’ll need sorting algorithms.

• Bubble sort: Compares two adjacent elements and swaps them if they’re in the wrong order
• Selection sort: Sorts lists by identifying the smallest element and placing it at the beginning of the unsorted list
• Insertion sort: Inserts the unsorted element in the correct position straight away
• Merge sort: Divides unsorted lists into smaller sections and orders each separately (the so-called divide-and-conquer principle)
• Quick sort: Also relies on the divide-and-conquer principle but employs a pivot element to partition the list (elements smaller than the pivot element go back, while larger ones are kept on the right)

Tree Traversal Algorithms

To traverse a tree means to visit its every node. Since trees aren’t linear data structures, there’s more than one way to traverse them.

• Pre-order traversal: Visits the root node first (the topmost node in a tree), followed by the left and finally the right subtree
• In-order traversal: Starts with the left subtree, moves to the root node, and ends with the right subtree
• Post-order traversal: Visits the nodes in the following order: left subtree, right subtree, the root node

Graph Traversal Algorithms

Graph traversal algorithms traverse all the vertices (or nodes) and edges in a graph. You can choose between two:

• Depth-first search – Focuses on visiting all the vertices or nodes of a graph data structure located one above the other
• Breadth-first search – Traverses the adjacent nodes of a graph before moving outwards

Applications of Data Structures

Data structures are critical for managing data. So, no wonder their extensive list of applications keeps growing virtually every day. Check out some of the most popular applications data structures have nowadays.

Data Organization and Storage

With this application, data structures return to their roots: they’re used to arrange and store data most efficiently.

Database Management Systems

Database management systems are software programs used to define, store, manipulate, and protect data in a single location. These systems have several components, each relying on data structures to handle records to some extent.

Let’s take a library management system as an example. Data structures are used every step of the way, from indexing books (based on the author’s name, the book’s title, genre, etc.) to storing e-books.

File Systems

File systems use specific data structures to represent information, allocate it to the memory, and manage it afterward.

Data Retrieval and Processing

With data structures, data isn’t stored and then forgotten. It can also be retrieved and processed as necessary.

Search Engines

Search engines (Google, Bing, Yahoo, etc.) are arguably the most widely used applications of data structures. Thanks to structures like tries and hash tables, search engines can successfully index web pages and retrieve the information internet users seek.

Data Compression

Data compression aims to accurately represent data using the smallest storage amount possible. But without data structures, there wouldn’t be data compression algorithms.

Data Encryption

Data encryption is crucial for preserving data confidentiality. And do you know what’s crucial for supporting cryptography algorithms? That’s right, data structures. Once the data is encrypted, data structures like hash tables also aid with value key storage.

Problem Solving and Optimization

At their core, data structures are designed for optimizing data and solving specific problems (both simple and complex). Throw their composition into the mix, and you’ll understand why these structures have been embraced by fields that heavily rely on mathematics and algorithms for problem-solving.

Artificial Intelligence

Artificial intelligence (AI) is all about data. For machines to be able to use this data, it must be properly stored and organized. Enter data structures.

Arrays, linked lists, queues, graphs, and stacks are just some structures used to store data for AI purposes.

Machine Learning

Data structures used for machine learning (MI) are pretty similar to other computer science fields, including AI. In machine learning, data structures (both linear and non-linear) are used to solve complex mathematical problems, manipulate data, and implement ML models.

Network Routing

Network routing refers to establishing paths through one or more internet networks. Various routing algorithms are used for this purpose and most heavily rely on data structures to find the best patch for the incoming data packet.

Data Structures: The Backbone of Efficiency

Data structures are critical in our data-driven world. They allow straightforward data representation, access, and manipulation, even in giant databases. For this reason, learning about data structures and algorithms further can open up a world of possibilities for a career in data science and related fields.

More and more companies are employing data scientists. In fact, the number has nearly doubled in recent years, indicating the importance of this profession for the modern workplace.

Additionally, data science has become a highly lucrative career. Professionals easily make over $120,000 annually, which is why it’s one of the most popular occupations.

This article will cover all you need to know about data science. We’ll define the term, its main applications, and essential elements.

What Is Data Science?

Data science analyzes raw information to provide actionable insights. Data scientists who retrieve this data utilize cutting-edge equipment and algorithms. After the collection, they analyze and break down the findings to make them readable and understandable. This way, managers, owners, and stakeholders can make informed strategic decisions.

Data Science Meaning

Although most data science definitions are relatively straightforward, there’s a lot of confusion surrounding this topic. Some people believe the field is about developing and maintaining data storage structures, but that’s not the case. It’s about analyzing data storage solutions to solve business problems and anticipate trends.

Hence, it’s important to distinguish between data science projects and those related to other fields. You can do so by testing your projects for certain aspects.

For instance, one of the most significant differences between data engineering and data science is that data science requires programming. Data scientists typically rely on code. As such, they clean and reformat information to increase its visibility across all systems.

Furthermore, data science generally requires the use of math. Complex math operations enable professionals to process raw data and turn it into usable insights. For this reason, companies require their data scientists to have high mathematical expertise.

Finally, data science projects require interpretation. The most significant difference between data scientists and some other professionals is that they use their knowledge to visualize and interpret their findings. The most common interpretation techniques include charts and graphs.

Data Science Applications

Many questions arise when researching data science. In particular, what are the applications of data science? It can be implemented for a variety of purposes:

• Enhancing the relevance of search results – Search engines used to take forever to provide results. The wait time is minimal nowadays. One of the biggest factors responsible for this improvement is data science.
• Adding unique flair to your video games – All gaming areas can gain a lot from data science. High-end games based on data science can analyze your movements to anticipate and react to your decisions, making the experience more interactive.
• Risk reduction – Several financial giants, such as Deloitte, hire data scientists to extract key information that lets them reduce business risks.
• Driverless vehicles – Technology that powers self-driving vehicles identifies traffic jams, speed limits, and other information to make driving safer for all participants. Data science-based cars can also help you reach your destination sooner.
• Ad targeting – Billboards and other forms of traditional marketing can be effective. But considering the number of online consumers is over 2.6 billion, organizations need to shift their promotion activities online. Data science is the answer. It lets organizations improve ad targeting by offering insights into consumer behaviors.
• AR optimization – AR brands can take a number of approaches to refining their headsets. Data science is one of them. The algorithms involved in data science can improve AR machines, translating to a better user experience.
• Premium recognition features – Siri might be the most famous tool developed through data science methods.

Learn Data Science

If you want to learn data science, understanding each stage of the process is an excellent starting point.

Data Collection

Data scientists typically start their day with data collection – gathering relevant information that helps them anticipate trends and solve problems. There are several methods associated with collecting data.

Data Mining

Data mining is great for anticipating outcomes. The procedure correlates different bits of information and enables you to detect discrepancies.

Web Scraping

Web scraping is the process of collecting data from web pages. There are different web scraping techniques, but most professionals utilize computer bots. This technique is faster and less prone to error than manual data discovery.

Remember that while screen scraping and web scraping are often used interchangeably, they’re not the same. The former merely copies screen pixels after recognizing them from various user interface components. The latter is a more extensive procedure that recovers the HTML code and any information stored within it.

Data Acquisition

Data acquisition is a form of data collection that garners information before storing it on your cloud-based servers or other solutions. Companies can collect information with specialized sensors and other devices. This equipment makes up their data acquisition systems.

Data Cleaning

You only need usable and original information in your system. Duplicate and redundant data can be a major obstacle, which is why you should use data cleaning. It removes contradictory information and helps you separate the wheat from the chaff.

Data Preprocessing

Data preprocessing prepares your data sets for other processes. Once it’s done, you can move on to information transformation, normalization, and analysis.

Data Transformation

Data transformation turns one version of information into another. It transforms raw data into usable information.

Data Normalization

You can’t start your data analysis without normalizing the information. Data normalization helps ensure that your information has uniform organization and appearance. It makes data sets more cohesive by removing illogical or unnecessary details.

Data Analysis

The next step in the data science lifecycle is data analysis. Effective data analysis provides more accurate data, improves customer insights and targeting, reduces operational costs, and more. Following are the main types of data analysis:

Exploratory Data Analysis

Exploratory data analysis is typically the first analysis performed in the data science lifecycle. The aim is to discover and summarize key features of the information you want to discuss.

Predictive Analysis

Predictive analysis comes in handy when you wish to forecast a trend. Your system uses historical information as a basis.

Statistical Analysis

Statistical analysis evaluates information to discover useful trends. It uses numbers to plan studies, create models, and interpret research.

Machine Learning

Machine learning plays a pivotal role in data analysis. It processes enormous chunks of data quickly with minimal human involvement. The technology can even mimic a human brain, making it incredibly accurate.

Data Visualization

Preparing and analyzing information is important, but a lot more goes into data science. More specifically, you need to visualize information using different methods. Data visualization is essential when presenting your findings to a general audience because it makes the information easily digestible.

Data Visualization Tools

Many tools can help you expedite your data visualization and create insightful dashboards.

Here are some of the best data visualization tools:

• Zoho Analytics
• Datawrapper
• Tableau
• Google Charts
• Microsoft Excel

Data Visualization Techniques

The above tools contain a plethora of data visualization techniques:

• Line chart
• Histogram
• Pie chart
• Area plot
• Scatter plot
• Hexbin plots
• Word clouds
• Network diagrams
• Highlight tables
• Bullet graphs

Data Storytelling

You can’t have effective data presentation without next-level storytelling. It contextualizes your narrative and gives your audience a better understanding of the process. Data dashboards and other tools can be an excellent way to enhance your storytelling.

Data Interpretation

The success of your data science work depends on your ability to derive conclusions. That’s where data interpretation comes in. It features a variety of methods that let you review and categorize your information to solve critical problems.

Data Interpretation Tools

Rather than interpret data on your own, you can incorporate a host of data interpretation tools into your toolbox:

• Layer – You can easily step up your data interpretation game with Layer. You can send well-designed spreadsheets to all stakeholders for improved visibility. Plus, you can integrate the app with other platforms you use to elevate productivity.
• Power Bi – A vast majority of data scientists utilize Power BI. Its intuitive interface enables you to develop and set up customized interpretation tools, offering a tailored approach to data science.
• Tableau – If you’re looking for another straightforward yet powerful platform, Tableau is a fantastic choice. It features robust dashboards with useful insights and synchronizes well with other applications.
• R – Advanced users can develop exceptional data interpretation graphs with R. This programming language offers state-of-the-art interpretation tools to accelerate your projects and optimize your data architecture.

Data Interpretation Techniques

The two main data interpretation techniques are the qualitative method and the quantitative method.

The qualitative method helps you interpret qualitative information. You present your findings using text instead of figures.

By contrast, the quantitative method is a numerical data interpretation technique. It requires you to elaborate on your data with numbers.

Data Insights

The final phase of the data science process involves data insights. These give your organization a complete picture of the information you obtained and interpreted, allowing stakeholders to take action on company problems. That’s especially true with actionable insights, as they recommend solutions for increasing productivity and profits.

Climb the Data Science Career Ladder, Starting From the Basics

The first step to becoming a data scientist is understanding the essence of data science and its applications. We’ve given you the basics involved in this field – the rest is up to you. Master every stage of the data science lifecycle, and you’ll be ready for a rewarding career path.

Recommender systems are AI-based algorithms that use different information to recommend products to customers. We can say that recommender systems are a subtype of machine learning because the algorithms “learn from their past,” i.e., use past data to predict the future.

Today, we’re exposed to vast amounts of information. The internet is overflowing with data on virtually any topic. Recommender systems are like filters that analyze the data and offer the users (you) only relevant information. Since what’s relevant to you may not interest someone else, these systems use unique criteria to provide the best results to everyone.

In this article, we’ll dig deep into recommender systems and discuss their types, applications, and challenges.

Types of Recommender Systems

Learning more about the types of recommender systems will help you understand their purpose.

Content-Based Filtering

With content-based filtering, it’s all about the features of a particular item. Algorithms pick up on specific characteristics to recommend a similar item to the user (you). Of course, the starting point is your previous actions and/or feedback.

Sounds too abstract, doesn’t it? Let’s explain it through a real-life example: movies. Suppose you’ve subscribed to a streaming platform and watched The Notebook (a romance/drama starring Ryan Gosling and Rachel McAdams). Algorithms will sniff around to investigate this movie’s properties:

• Genre
• Actors
• Reviews
• Title

Then, algorithms will suggest what to watch next and display movies with similar features. For example, you may find A Walk to Remember on your list (because it belongs to the same genre and is based on a book by the same author). But you may also see La La Land on the list (although it’s not the same genre and isn’t based on a book, it stars Ryan Gosling).

Some of the advantages of this type are:

• It only needs data from a specific user, not a whole group.
• It’s ideal for those who have interests that don’t fall into the mainstream category.

A potential drawback is:

• It recommends only similar items, so users can’t really expand their interests.

Collaborative Filtering

In this case, users’ preferences and past behaviors “collaborate” with one another, and algorithms use these similarities to recommend items. We have two types of collaborative filtering: user-user and item-item.

User-User Collaborative Filtering

The main idea behind this type of recommender system is that people with similar interests and past purchases are likely to make similar selections in the future. Unlike the previous type, the focus here isn’t just on only one user but a whole group.

Collaborative filtering is popular in e-commerce, with a famous example being Amazon. It analyzes the customers’ profiles and reviews and offers recommended products using that data.

The main advantages of user-user collaborative filtering are:

• It allows users to explore new interests and stay in the loop with trends.
• It doesn’t need information about the specific characteristics of an item.

The biggest disadvantage is:

• It can be overwhelmed by data volume and offer poor results.

Item-Item Collaborative Filtering

If you were ever wondering how Amazon knows you want a mint green protective case for the phone you just ordered, the answer is item-item collaborative filtering. Amazon invented this type of filtering back in 1998. With it, the e-commerce platform can make quick product suggestions and let users purchase them with ease. Here, the focus isn’t on similarities between users but between products.

Some of the advantages of item-item collaborative filtering are:

• It doesn’t require information about the user.
• It encourages users to purchase more products.

The main drawback is:

• It can suffer from a decrease in performance when there’s a vast amount of data.

Hybrid Recommender Systems

As we’ve seen, both collaborative and content-based filtering have their advantages and drawbacks. Experts designed hybrid recommender systems that grab the best of both worlds. They overcome the problems behind collaborative and content-based filtering and offer better performance.

With hybrid recommender systems, algorithms take into account different factors:

• Users’ preferences
• Users’ past purchases
• Users’ product ratings
• Similarities between items
• Current trends

A classic example of a hybrid recommender system is Netflix. Here, you’ll see the recommended content based on the TV shows and movies you’ve already watched. You can also discover content that users with similar interests enjoy and can see what’s trending at the moment.

The biggest strong points of this system are:

• It offers precise and personalized recommendations.
• It doesn’t have cold-start problems (poor performance due to lack of information).

The main drawback is:

• It’s highly complex.

Machine Learning Techniques in Recommender Systems

It’s fair to say that machine learning is like the foundation stone of recommender systems. This sub-type of artificial intelligence (AI) represents the process of computers generating knowledge from data. We understand the “machine” part, but what does “learning” implicate? “Learning” means that machines improve their performance and enhance capabilities as they learn more information and become more “experienced.”

The four machine learning techniques recommender systems love are:

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Deep learning

Supervised Learning

In this case, algorithms feed off past data to predict the future. To do that, algorithms need to know what they’re looking for in the data and what the target is. The data in which we know the target label are named labeled datasets, and they teach algorithms how to classify data or make predictions.

Supervised learning has found its place in recommender systems because it helps understand patterns and offers valuable recommendations to users. It analyzes the users’ past behavior to predict their future. Plus, supervised learning can handle large amounts of data.

The most obvious drawback of supervised learning is that it requires human involvement, and training machines to make predictions is no walk in the park. There’s also the issue of result accuracy. Whether or not the results will be accurate largely depends on the input and target values.

Unsupervised Learning

With unsupervised learning, there’s no need to “train” machines on what to look for in datasets. Instead, the machines analyze the information to discover hidden patterns or similar features. In other words, you can sit back and relax while the algorithms do their magic. There’s no need to worry about inputs and target values, and that is one of the best things about unsupervised learning.

How does this machine learning technique fit into recommender systems? The main application is exploration. With unsupervised learning, you can discover trends and patterns you didn’t even know existed. It can discover surprising similarities and differences between users and their online behavior. Simply put, unsupervised learning can perfect your recommendation strategies and make them more precise and personal.

Reinforcement Learning

Reinforcement learning is another technique used in recommender systems. It functions like a reward-punishment system, where the machine has a goal that it needs to achieve through a series of steps. The machine will try a strategy, receive back, change the strategy as necessary, and try again until it reaches the goal and gets a reward.

The most basic example of reinforcement learning in recommender systems is movie recommendations. In this case, the “reward” would be the user giving a five-star rating to the recommended movie.

Deep Learning

Deep learning is one of the most advanced (and most fascinating) subcategories of AI. The main idea behind deep learning is building neural networks that mimic and function similarly to human brains. Machines that feature this technology can learn new information and draw their own conclusions without any human assistance.

Thanks to this, deep learning offers fine-tuned suggestions to users, enhances their satisfaction, and ultimately leads to higher profits for companies that use it.

Challenges and Future Trends in Recommender Systems

Although we may not realize it, recommender systems are the driving force of online purchases and content streaming. Without them, we wouldn’t be able to discover amazing TV shows, movies, songs, and products that make our lives better, simpler, and more enjoyable.

Without a doubt, the internet would look very different if it wasn’t for recommender systems. But as you may have noticed, what you see as recommended isn’t always what you want, need, or like. In fact, the recommendations can be so wrong that you may be shocked how the internet could misinterpret you like that. Recommender systems aren’t perfect (at least not yet), and they face different challenges that affect their performance:

• Data sparsity and scalability – If users don’t leave a trace online (don’t review items), the machines don’t have enough data to analyze and make recommendations. Likewise, the datasets change and grow constantly, which can also represent an issue.
• Cold start problem – When new users become a part of a system, they may not receive relevant recommendations because algorithms don’t “know” their preferences, past purchases, or ratings. The same goes for new items introduced to a system.
• Privacy and security concerns – Privacy and security are always at the spotlight of recommender systems. The situation is a paradox. The more a system knows about you, the better recommendations you’ll get. At the same time, you may not be willing to let a system learn your personal information if you want to maintain your privacy. But then, you won’t enjoy great recommendations.
• Incorporating contextual information – Besides “typical” information, other data can help make more precise and relevant recommendations. The problem is how to incorporate them.
• Explainability and trust – Can a recommender system explain why it made a certain recommendation, and can you trust it?

Discover New Worlds with Recommender Systems

Recommender systems are growing smarter by the day, thanks to machine learning and technological advancements. The recommendations were introduced to allow us to save time and find exactly what we’re looking for in a jiff. At the same time, they let us experiment and try something different.

While recommender systems have come a long way, there’s still more than enough room for further development.

As one of the world’s fastest-growing industries, with a predicted compound annual growth rate of 16.43% anticipated between 2022 and 2030, data science is the ideal choice for your career. Jobs will be plentiful. Opportunities for career advancement will come thick and fast. And even at the most junior level, you’ll enjoy a salary that comfortably sits in the mid-five figures.

Studying for a career in this field involves learning the basics (and then the complexities) of programming languages including C+, Java, and Python. The latter is particularly important, both due to its popularity among programmers and the versatility that Python brings to the table. Here, we explore the importance of Python for data science and how you’re likely to use it in the real world.

Why Python for Data Science?

We can distill the reasons for learning Python for data science into the following five benefits.

Popularity and Community Support

Statista’s survey of the most widely-used programming languages in 2022 tells us that 48.07% of programmers use Python to some degree. Leftronic digs deeper into those numbers, telling us that there are 8.2 million Python developers in the world. As a prospective developer yourself, these numbers tell you two things – Python is in demand and there’s a huge community of fellow developers who can support you as you build your skills.

Easy to Learn and Use

You can think of Python as a primer for almost any other programming language, as it takes the fundamental concepts of programming and turns them into something practical. Getting to grips with concepts like functions and variables is simpler in Python than in many other languages. Python eventually opens up from its simplistic use cases to demonstrate enough complexity for use in many areas of data science.

Extensive Libraries and Tools

Given that Python was first introduced in 1991, it has over 30 years of support behind it. That, combined with its continued popularity, means that novice programmers can access a huge number of tools and libraries for their work. Libraries are especially important, as they act like repositories of functions and modules that save time by allowing you to benefit from other people’s work.

Integration With Other Programming Languages

The entire script for Python is written in C, meaning support for C is built into the language. While that enables easy integration between these particular languages, solutions exist to link Python with the likes of C++ and Java, with Python often being capable of serving as the “glue” that binds different languages together.

Versatility and Flexibility

If you can think it, you can usually do it in Python. Its clever modular structure, which allows you to define functions, modules, and entire scripts in different files to call as needed, makes Python one of the most flexible programming languages around.

Setting Up Python for Data Science

Installing Python onto your system of choice is simple enough. You can download the language from the Python.org website, with options available for everything from major operating systems (Windows, macOS, and Linux) to more obscure devices.

However, you need an integrated development environment (IDE) installed to start coding in Python. The following are three IDEs that are popular with those who use Python for data science:

• Jupyter Notebook – As a web-based application, Jupyter easily allows you to code, configure your workflows, and even access various libraries that can enhance your Python code. Think of it like a one-stop shop for your Python needs, with extensions being available to extend its functionality. It’s also free, which is never a bad thing.
• PyCharm – Where Jupyter is an open-source IDE for several languages, PyCharm is for Python only. Beyond serving as a coding tool, it offers automated code checking and completion, allowing you to quickly catch errors and write common code.
• Visual Studio Code – Though Visual Studio Code alone isn’t compatible with Python, it has an extension that allows you to edit Python code on any operating system. Its “Linting” feature is great for catching errors in your code, and it comes with an integrated debugger that allows you to test executables without physically running them.

Setting up your Python virtual environment is as simple as downloading and installing Python itself, and then choosing an IDE in which to work. Think of Python as the materials you use to build a house, with your IDE being both the blueprint and the tools you’ll need to patch those materials together.

Essential Python Libraries for Data Science

Just as you’ll go to a real-world library to check out books, you can use Python libraries to “check out” code that you can use in your own programs. It’s actually better than that because you don’t need to return libraries when you’re done with them. You get to keep them, along with all of their built-in modules and functions, to call upon whenever you need them. In Python for data science, the following are some essential libraries:

• NumPy – We spoke about integration earlier, and NumPy is ideal for that. It brings concepts of functionality from Fortran and C into Python. By expanding Python with powerful array and numerical computing tools, it helps transform it into a data science powerhouse.
• pandas – Manipulating and analyzing data lies at the heart of data sciences, and pandas give you a library full of tools to allow both. It offers modules for cleaning data, plotting, finding correlations, and simply reading CSV and JSON files.
• Matplotlib – Some people can look at reams of data and see patterns form within the numbers. Others need visualization tools, which is where Matplotlib excels. It helps you create interactive visual representations of your data for use in presentations or if you simply prefer to “see” your data rather than read it.
• Scikit-learn – The emerging (some would say “exploding) field of machine learning is critical to the AI-driven future we’re seemingly heading toward. Scikit-learn is a library that offers tools for predictive data analysis, built on what’s available in the NumPy and Matplotlib libraries.
• TensorFlow and Keras – Much like Scikit-learn, both TensorFlow and Keras offer rich libraries of tools related to machine learning. They’re essential if your data science projects take you into the realms of neural networks and deep learning.

Data Science Workflow in Python

A Python programmer without a workflow is like a ship’s captain without a compass. You can sail blindly onward, and you may even get lucky and reach your destination, but the odds are you’re going to get lost in the vastness of the programming sea. For those who want to use Python for data science, the following workflow brings structure and direction to your efforts.

Step 1 – Data Collection and Preprocessing

You need to collect, organize, and import your data into Python (as well as clean it) before you can draw any conclusions from it. That’s why the first step in any data science workflow is to prepare the data for use (hint – the pandas library is perfect for this task).

Step 2 – Exploratory Data Analysis (EDA)

Just because you have clean data, that doesn’t mean you’re ready to investigate what that data tells you. It’s like washing ingredients before you make a dish – you need to have a “recipe” that tells you how to put everything together. Data scientists use EDA as this recipe, allowing them to combine data visualization (remember – the Matplotlib library) with descriptive statistics that show them what they’re looking at.

Step 3 – Feature Engineering

This is where you dig into the “whats” and “hows” of your Python program. You’ll select features for the code, which define what it does with the data you import and how it’ll deliver outcomes. Scaling is a key part of this process, with scope creep (i.e., constantly adding features as you get deeper into a project) being the key thing to avoid.

Step 4 – Model Selection and Training

Decision trees, linear regression, logistic regression, neural networks, and support vector machines. These are all models (with their own algorithms) you can use for your data science project. This step is all about selecting the right model for the job (your intended features are important here) and training that model so it produces accurate outputs.

Step 5 – Model Evaluation and Optimization

Like a puppy that hasn’t been house trained, an unevaluated model isn’t ready for release into the real world. Classification metrics, such as a confusion matrix and classification report, help you to evaluate your model’s predictions against real-world results. You also need to tune the hyperparameters built into your model, similar to how a mechanic may tune the nuts and bolts in a car, to get everything working as efficiently as possible.

Step 6 – Deployment and Maintenance

You’ve officially deployed your Python for data science model when you release it into the wild and let it start predicting outcomes. But the work doesn’t end at deployment, as constant monitoring of what your model does, outputs, and predicts is needed to tell you if you need to make tweaks or if the model is going off the rails.

Real-World Data Science Projects in Python

There are many examples of Python for data science in the real world, some of which are simple while others delve into some pretty complex datasets. For instance, you can use a simple Python program to scrap live stock prices from a source like Yahoo! Finance, allowing you to create a virtual ticker of stock price changes for investors.

Alternatively, why not create a chatbot that uses natural language processing to classify and respond to text? For that project, you’ll tokenize sentences, essentially breaking them down into constituent words called “tokens,” and tag those tokens with meanings that you could use to prompt your program toward specific responses.

There are plenty of ideas to play around with, and Python is versatile enough to enable most, so consider what you’d like to do with your program and then go on the hunt for datasets. Great (and free) resources include The Boston House Price Dataset, ImageNet, and IMDB’s movie review database.

Try Python for Data Science Projects

By combining its own versatility with integrations and an ease of use that makes it welcoming to beginners, Python has become one of the world’s most popular programming languages. In this introduction to data science in Python, you’ve discovered some of the libraries that can help you to apply Python for data science. Plus, you have a workflow that lends structure to your efforts, as well as some ideas for projects to try. Experiment, play, and tweak models. Every minute you spend applying Python to data science is a minute spent learning a popular programming language in the context of a rapidly-growing industry.

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.

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.

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.