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.

Today’s tech-driven world is governed by data – so much so that nearly 98% of all organizations are increasing investment in data.

However, company owners can’t put their feet up after improving their data capabilities. They also need a database management system (DBMS) – a program specifically designed for storing and organizing information efficiently.

When analyzing a DBMS, you need to be thorough like a detective investigating a crime. One of the elements you want to consider is DBMS architecture. It describes the structure of your database and how individual bits of information are related to each other. The importance of DBMS architecture is enormous, as it helps IT experts design and maintain fully functional databases.

But what exactly does a DBMS architecture involve? You’ll find out in this entry. Coming up is an in-depth discussion of database system concepts and architecture.

Overview of DBMS Architecture

Suppose you’re assembling your PC. You can opt for several configurations, such as those with three RAM slots and dual-fan coolers. The same principle applies to DBMS architectures.

Two of the most common architectures are three-level and two-level architectures.

Three-Level Architecture

Three-level architecture is like teacher-parent communication. More often than not, a teacher communicates with parents through children, asking them to convey certain information. In other words, there are layers between the two that don’t allow direct communication.

The same holds for three-level architecture. But instead of just one layer, there are two layers between the database and user: application client and application server.

And as the name suggests, a three-level DBMS architecture has three levels:

• External level – Also known as the view level, this section concerns the part of your database that’s relevant to the user. Everything else is hidden.
• Conceptual level – Put yourself in the position of a scuba diver exploring the ocean layer by layer. Once you reach the external level, you go one segment lower and find the conceptual level. It describes information conceptually and tells you how data segments interact with one another.
• Internal level – Another name for the internal level is the physical level. But what does it deal with? It mainly focuses on how data is stored in your system (e.g., using folders and files).

Two-Level Architecture

When you insert a USB into your PC, you can see the information on your interface. However, the source of the data is on the USB, meaning they’re separated.

Two-level architecture takes the same approach to separating data interface and data structure. Here are the two levels in this DBMS architecture:

• User level – Any application and interface in your database are stored on the user level in a two-level DBMS architecture.
• System level – The system level (aka server level) performs transaction management and other essential processes.

Comparison of the Two Architectures

Determining which architecture works best for your database is like buying a car. You need to consider how easy it is to use and the level of performance you can expect.

On the one hand, the biggest advantage of two-level architectures is that they’re relatively easy to set up. There’s just one layer between the database and the user, resulting in easier database management.

On the other hand, developing a three-level DBMS architecture may take a while since you need to include two layers between the database and the user. That said, three-level architectures are normally superior to two-level architectures due to higher flexibility and the ability to incorporate information from various sources.

Components of DBMS Architecture

You’ve scratched the surface of database system concepts and architecture, but don’t stop there. It’s time to move on to the basics to the most important elements of a DBMS architecture:

Data Storage

The fact that DBMS architectures have data storage solutions is carved in stone. What exactly are those solutions? The most common ones are as follows:

• Data files – How many files do you have on your PC? If it’s a lot, you’re doing exactly what administrators of DBMS architectures are doing. A large number of them store data in files, and each file is categorized into blocks.
• Indexes – You want your database operations to be like lightning bolts, i.e. super-fast. You can incorporate indexes to accomplish this goal. They point to data columns for quick retrieval.
• Data dictionary – Also known as system logs, data dictionaries contain metadata – information about your data.

Data Manipulation

A large number of companies still utilize manual data management methods. But using this format is like shooting yourself in the foot when there are advanced data manipulation methods are available. These allow you to process and retrieve data within seconds through different techniques:

• Query processor – Query processing refers to extracting data from your DBMS architecture. It operates like any other multi-stage process. It involves parsing, translation, optimization, and evaluation.
• Query optimizer – A DBMS architecture administrator can perform various query optimization tasks to achieve desired results faster.
• Execution engine – Whenever you want your architecture to do something, you send requests. But something needs to process the requests – that something is the execution engine.

Data Control

We’re continuing our journey through an average DBMS architecture. Our next stop is data control, which is comprised of these key elements:

• Transaction management – When carrying out multiple transactions, how does the system prioritize one over another? The answer lies in transaction management, which is also about processing multiple transactions side by side.
• Concurrency control – Database architecture is like an ocean teeming with life. Countless operations take place simultaneously. As a result, the system needs concurrency control to manage these concurrent tasks.
• Recovery management – What if your DBMS architecture fails? Do you give up on your project? No – the system has robust recovery management tools to retrieve your information and reduce downtime.

Database System Concepts

To give you a better understanding of a DBMS architecture, let’s describe the most important concepts regarding this topic.

Data Models

Data models do to information what your folders do to files – organize them. There are four major types of data models:

• Hierarchical model – Top-down and bottom-up storage solutions are known as hierarchical models. They’re characterized by tree-like structures.
• Network model – Hierarchical models are generally used for basic data relationships. If you want to analyze complex relationships, you need to kick things up a notch with network models. They enable you to represent huge quantities of complex information without a hitch.
• Relational model – Relations are merely tables with values. A relational model is a collection of these relations, indicating how data is connected to other data.
• Object-oriented model – Programming languages regularly use objects. An object-oriented model stores information as models and is usually more complex than other models.

Database Schema and Instances

Another concept you should familiarize yourself with is schemas and instances.

• Definition of schema and instance – Schemas are like summaries, providing a basic description of databases. Instances tell you what information is stored in a database.
• Importance of schema in DBMS architecture – Schemas are essential because they help organize data by providing a clear outline.

Data Independence

The ability of other pieces of information to remain unaffected after you change one bit of data is known as data independence. What are the different types of data independence, and what makes them so important?

• Logical data independence – If you can modify logical schemas without altering the rest of the system, your logical data is independent.
• Physical data independence – Physical data is independent if it remains unaffected when changing your hardware, such as SSD disks.
• Significance of data independence in DBMS architecture – Independent data is crucial for saving time in database management because it reduces the amount of information that needs to be processed.

Efficient Database Management Systems

Database management systems have a lot in common with other tech-based systems. For example, you won’t ignore problems that arise on your PC, be they CPU or graphics card issues. You’ll take action to optimize the performance of the device and solve those issues.

That’s exactly what 75% of developers and administrators of database management systems do. They go the extra mile to enhance the performance, scalability, flexibility, security, and integrity of their architecture.

Performance Optimization Techniques

• Indexing – By pointing to certain data in tables, indexes speed up database management.
• Query optimization – This process is about finding the most efficient method of executing queries.
• Caching – Frequently accessed information is cached to accelerate retrieval.

Scalability and Flexibility

• Horizontal scaling – Horizontal scaling involves increasing the number of servers.
• Vertical scaling – An administrator can boost the performance of the server to make the system more scalable.
• Distributed databases – Databases are like smartphones in that they can easily overload. Pressure can be alleviated with distributed databases, which store information in multiple locations.

Security and Integrity

• Access control – Restricting access is key to preventing cyber security attacks.
• Data encryption – Administrators often encrypt their DBMS architecture to protect sensitive information.
• Backup and recovery – A robust backup plan helps IT experts recover from shutdowns and other unforeseen problems.

Preparing for the Future Is Critical

DBMS architecture is the underlying structure of a database management system. It consists of several elements, all of which work together to create a fully functional data infrastructure.

Understanding the basic elements of DBMS architecture is vital for IT professionals who want to be well-prepared for future changes, such as hybrid environments. As the old saying goes – success depends upon preparation.

You may have heard the catchy phrase “data is the new oil” floating around. The implication is that data in the 21st century is what oil was in the 20th – the biggest industry around. And it’s true, as the sheer amount of data each person generates when they use the web, try out an app, or even buy from a store is digital “oil” for the companies collecting that data.

It’s also the fuel that powers the current (and growing) wave of artificial intelligence (AI) tools emerging in the market. From ChatGPT to the wave of text-to-speech tech flooding the market, everything hinges on information, and people who can harness that data through algorithms and machine learning practices are in high demand.

That’s where you can come in. By taking a Master’s degree in artificial intelligence online, you position yourself as one of the people who can help the new “digital oil” barons capitalize on their finds.

Factors to Consider When Choosing an Online AI Master’s Program

When choosing an artificial intelligence online Master’s, you have to consider more than the simple accessibility the course offers. These factors help you to weed out the also-ran programs from the ones that help you to advance your career:

• Accreditation – Checks for accreditation come in two flavors. First, you need to check the program provider’s credentials to ensure the degree you get from your studies is worth the paper on which it’s printed. Second, you have to confirm the accreditation you receive is something that employers actually want to see.
• Curriculum – What does your artificial intelligence online Master degree actually teach you? Answer that question and you can determine if the program serves the career goals you’ve set for yourself.
• Faculty Expertise – On the ground level, you want tutors with plenty of teaching experience and their own degrees in AI-related subjects. But dig beyond that to also discover if they have direct experience working with AI in industry.
• Program Format – A self-study artificial intelligence Master’s program’s online nature means they offer some degree of flexibility. But the course format plays a role in your decision, given that some rely solely on self-learning whereas others include examinations and live remote lectures.
• Tuition and Financial Aid – A Master’s degree costs quite a bit depending on area (prices range from €1,000 to €20,000 per year), so you need to be in the appropriate financial position. Many universities offer financial aid, such as scholarships, grants, and payment programs, that may help here.
• Career Support – You’re likely not studying for Master of artificial intelligence online for the joy of having a piece of paper on your wall. You want to build a career. Look for institutions that have strong alumni networks, connections within industry, and dedicated careers offices or services.

Top Online AI Master’s Programs Ranked

In choosing the best Master’s in artificial intelligence online programs, we looked at the above factors in addition to the key features of each program. That examination results in three online courses, each offering something a little different, that give you a solid grounding in AI.

Master in Applied Data Science & AI (OPIT)

Flexibility is the name of the game with OPIT’s program, as it’s fully remote and you get a choice between an 18-month course and a fast-tracked 12-month variant. The latter contains the same content as the former, with the student simply dedicating themselves to more intensive course requirements.

The program comes from an online institution that is accredited under both the Malta Qualification Framework and European Qualification Framework. As for the course itself, it’s the focus on real-life challenges in data science and AI that makes it so attractive. You don’t just learn theory. You discover how to apply that theory to the practical problems you’ll face when you enter the workforce.

OPIT has an admissions team who’ll guide you through getting onto the course, though you’ll need a BSc degree (in any field) and the equivalent of B2-level English proficiency to apply. If English isn’t your strong suit, OPIT also offers an in-house certification that you can take to get on the course. Financial aid is available through scholarships and funding, which you may need given that the program can cost up to €6,500, though discounts are available for those who apply early.

Master in Big Data, Artificial Intelligence, and Disruptive Technologies (Digital Age University)

If data is the new oil, Digital Age University’s program teaches you how to harness that oil and pump it in a way that makes you an attractive proposition for any employer. Key areas of study include the concept and utilization of Big Data (data analytics plays a huge role here), as well as the Python programming skills needed to create AI tools. You’ll learn more about machine learning models and get to grips with how AI is the big disruptor in modern business.

Tuition costs are reasonable, too, with this one-year course only costing €2,600. Digital Age University runs a tuition installment plan that lets you spread your costs out without worrying about being charged interest. Plus, your previous credentials may put you in line for a grant or scholarship that covers at least part of the cost. All first-year students are eligible for the 10% merit-based scholarship again, dependent on prior education). There’s also a 20% Global Scholarship available to students from Asia, Africa, the Middle East, and Latin American countries.

Speaking of credentials, you can showcase yours via the online application process or by scheduling a one-on-one call with one of the institution’s professors. The latter option is great if you’re conducting research and want to get a taste of what the faculty has to offer.

Master in Artificial Intelligence (Three Points Digital Business School)

Three Points Digital Business School sets its stall out early by pointing out that 83% of companies say they’ll create new jobs due to AI in the coming years. That’s its way of telling you that its business-focused AI course is the right choice for getting one of those jobs. After teaching the fundamentals of AI, the course moves into showing you how to create AI and machine learning models and, crucially, how to apply those models in practical settings. By the end, you’ll know how to program chatbots, virtual assistants, and similar AI-driven tools.

It’s the most expensive program on this list, clocking in at €7,500 for a one-year course that delivers 60 ECTS credits. However, it’s a course targeted at mature students (half of the current students are 40 years old), and it’s very much career-minded. That’s exemplified by Three Points’ annual ThinkDigital Summit, which puts some of the leading minds in AI and digital innovation in front of students.

Admission is tougher than for many other Master’s in artificial intelligence online programs as you go through an interview process in addition to submitting qualifications. Every candidate is manually assessed via committee, with your experience and business know-how playing as much of a role as any technical qualifications you have.

Tips for Success in an Online AI Master’s Program

Let’s assume you’ve successfully applied to an artificial intelligence online Master’s program. That’s the first step in a long, often complex, journey. Here are some tips to keep in mind and set up for the future:

• Manage your time properly by scheduling your study, especially given that online courses rely on students having the discipline needed for self-learning.
• Build relationships with faculty and peers who may be able to connect you to job opportunities or have ideas for starting their own businesses.
• Stay up-to-date on what’s happening with AI because this high-paced industry can leave people who assume what they know is enough behind.
• Pursue real-world experience wherever you can, both through the practical assessments a program offers and internship programs that you can add to your CV.

Career Opportunities With a Master’s in Artificial Intelligence

You need to know what sorts of roles are available on the digital “oil rigs” of today and the future. Those who have an artificial intelligence online Master degree take roles as varied as data analyst, software engineer, data scientist, and research scientist.

Better yet, those roles are spread across almost all industries. Grand View Research tells us that we can expect the AI market to enjoy a 37.3% compound annual growth rate between 2023 and 2030, with that growth making AI-based roles available on a near-constant basis. Salary expectations are likely to increase along with that growth, with the current average of around €91,000 for an artificial intelligence engineer (figures based on Germany’s job market) likely to be a baseline for future growth.

Find the Right Artificial Intelligence Master’s Programs Online

We’ve highlighted three online Master’s programs with a focus on AI in this article, each offering something different. OPIT’s course leans heavily into data science, giving you a specialization to go along with the foundational knowledge you’ll gain. Digital Age University’s program places more of a focus on Big Data, with Three Points Digital Business School living up to its name by taking a more business-oriented approach.

Whatever program you choose (and it could be one other than the three listed here), you must research the course based on the factors like credentials, course content, and quality of the faculty. Put plenty of time into this research process and you’re sure to find a program that aligns with your goals.

Machines that can learn on their own have been a sci-fi dream for decades. Lately, that dream seems to be coming true thanks to advances in AI, machine learning, deep learning, and other cutting-edge technologies.

Have you used Google’s search engine recently or admired the capabilities of ChatGPT? That means you’ve seen machine learning in action. Besides those renowned apps, the technology is widespread across many industries, so much so that machine learning experts are in increasingly high demand worldwide.

Chances are there’s never been a better time to get involved in the IT industry than today. This is especially true if you enter the market as a machine learning specialist. Fortunately, getting proficient in this field no longer requires enlisting in a college – now you can finish a Master in machine learning online.

Let’s look at the best online Masters in machine learning and data science that you can start from the comfort of your home.

Top MSc Programs in Machine Learning Online

Finding the best MSc machine learning online programs required us to apply certain strict criteria in the search process. The following is a list of programs that passed our research with flying colors. But first, here’s what we looked for in machine learning MSc courses.

Our Criteria

The criteria we applied include:

• The quality and reputation of the institution providing the course
• International degree recognition
• Program structure and curriculum
• Duration
• Pricing

Luckily, numerous world-class universities and organizations have a machine learning MSc online. Their degrees are accepted around the world, and their curricula count among the finest in the market. Take a look at our selection.

Imperial College London – Machine Learning and Data Science

The Machine Learning and Data Science postgraduate program from the Imperial College in London provides comprehensive courses on models applicable to real-life scenarios. The program features hands-on projects and lessons in deep learning, data processing, analytics, and machine learning ethics.

The complete program is online-based and relies mostly on independent study. The curriculum consists of 13 modules. With a part-time commitment, this program will last for two years. The fee is the same for domestic and overseas students: £16,200

European School of Data Science & Technology – MSc Artificial Intelligence and Machine Learning

If you need a Master’s program that combines the best of AI and machine learning, the European School of Data Science & Technology has an excellent offer. The MSc Artificial Intelligence and Machine Learning program provides a sound foundation of the essential concepts in both disciplines.

During the courses, you’ll examine the details of reinforcement learning, search algorithms, optimization, clustering, and more. You’ll also get the opportunity to work with machine learning in the R language environment.

The program lasts for 18 months and is entirely online. Applicants must cover a registration fee of €1500 plus monthly fees of €490.

European University Cyprus – Artificial Intelligence Master

The European University in Cyprus is an award-winning institution that excels in student services and engagement, as well as online learning. The Artificial Intelligence Master program from this university treats artificial intelligence in a broader sense. However, machine learning is a considerable part of the curriculum, being taught alongside NLP, robotics, and big data.

The official site of the European University Cyprus states the price for all computer science Master’s degrees at €8,460. However, it’s worth noting that there’s a program for financial support and scholarships. The duration of the program is 18 months, after which you’ll get an MSc in artificial intelligence.

Udacity – Computer Vision Nanodegree

Udacity has profiled itself as a leading learning platform. Its Nanodegree programs provide detailed knowledge on numerous subjects, such as this Computer Vision Nanodegree. The course isn’t a genuine MSc program, but it offers specialization for a specific field of machine learning that may serve for career advancement.

This program includes lessons on the essentials of image processing and computer vision, deep learning, object tracking, and advanced computer vision applications. As with other Udacity courses, learners will enjoy support in real-time as well as career-specific services for professional development after finishing the course.

This Nanodegree has a flexible schedule, allowing you to set a personalized learning pace. The course lasts for three months and has a fee of €944. Scholarship options are also available for this program, and there are no limitations in terms of applying for the course or starting the program.

Lebanese American University – MS in Applied Artificial Intelligence

Lebanese American University curates the MS in Applied Artificial Intelligence study program, led by experienced faculty members. The course is completely online and focuses on practical applications of AI programming, machine learning, data learning, and data science. During the program, learners will have the opportunity to try out AI solutions for real-life issues.

This MS program has a duration of two years. During that time, you can take eight core courses and 10 elective courses, including subjects like Healthcare Analytics, Big Data Analytics, and AI for Biomedical Informatics.

The price of this program is €6,961 per year. It’s worth noting that there’s a set application deadline and starting date for the course. The first upcoming application date is in July, with the program starting in September.

Data Science Degrees: A Complementary Path

Machine learning can be viewed as a subcategory of data science. While the former focuses on methods of supervised and unsupervised AI learning, the latter is a broad field of research. Data science deals with everything from programming languages to AI development and robotics.

Naturally, there’s a considerable correlation between machine learning and data science. In fact, getting familiar with the principles of data science can be quite helpful when studying machine learning. That’s why we compiled a list of degree programs for data science that will complement your machine learning education perfectly.

Top Online Data Science Degree Programs

Purdue Global – Online Bachelor of Science Degree in Analytics

Data analytics represents one of the essential facets of data science. The Online Bachelor of Science Degree in Analytics program is an excellent choice to get familiar with data science skills. To that end, the program may complement your machine learning knowledge or serve as a starting point for a more focused pursuit of data science.

The curriculum includes nine different paths of professional specialization. Some of those concentrations include cloud computing, network administration, game development, and software development in various programming languages.

Studying full-time, you should be able to complete the program within four years. Each course has a limited term of 10 weeks. The program in total requires 180 credits, and the price of one credit is $371 or its equivalent in euros.

Berlin School of Business and Innovation – MSc Data Analytics

MSc Data Analytics is a postgraduate program from the Berlin School of Business and Innovation (BSBI). As an MSc curriculum, the program is relatively complex and demanding, but will be more than worthwhile for anyone wanting to gain a firm grasp of data analytics.

This is a traditional on-campus course that also has an online variant. The program focuses on data analysis and extraction and predictive modeling. While it could serve as a complementary degree to machine learning, it’s worth noting that this course may be the most useful for those pursuing a multidisciplinary approach.

This MSc course lasts for 18 months. Pricing differs between EU and non-EU students, with the former paying €8,000 and the latter €12,600.

Imperial College London – Machine Learning and Data Science

It’s apparent from the very name that this Imperial College London program represents an ideal mix. Machine Learning and Data Science combines the two disciplines, providing a thorough insight into their fundamentals and applications.

The two-year program is tailored for part-time learners. It consists of core modules like Programming for Data Science, Ethics in Data Science and Artificial Intelligence, Deep Learning, and Applicable Mathematics.

This British-based program costs £16,200 yearly, both for domestic and overseas students. Some of the methods include lectures, tutorials, exercises, and reading materials.

Thriving Career Opportunities With a Masters in Machine Learning Online

Jobs in machine learning require proper education. The chances of becoming a professional in the field without mastering the subject are small – the industry needs experts.

A Master’s degree in machine learning can open exciting and lucrative career paths. Some of the best careers in the field include:

• Data scientist
• Machine learning engineer
• Business intelligence developer
• NLP scientist
• Software engineer
• Machine learning designer
• Computational linguist
• Software developer

These professions pay quite well across the EU market. The median annual salary for a machine learning specialist is about €70,000 in Germany, €68,000 in the Netherlands, €46,000 in France, and €36,000 in Italy.

On the higher end, salaries in these countries can reach €98,000, €113,000, €72,000, and €65,000, respectively. To reach these more exclusive salaries, you’ll need to have a quality education in the field and a level of experience.

Become Proficient in Machine Learning Skills

Getting a Master’s degree in machine learning online is convenient, easily accessible, and represents a significant career milestone. With the pace at which the industry is growing today, it would be a wise choice.

Since the best programs offer a thorough education, great references, and a chance for networking, there’s no reason not to check out the courses on offer. Ideally, getting the degree could mark the start of a successful career in machine learning.