Elevate Your Tech Career With the Best NLP Courses Online

Apr 12, 2024 • 6 min read

Share on:

Natural language processing, or NLP for short, has been making waves for years, but as of late, it has caught the attention of even non-tech enthusiasts. Why is that? Simply put, natural language processing bridges human language and computer understanding to make interactions feel more natural and less like talking to machines.

For tech professionals, NLP skills have grown from “nice-to-have” status a few years ago to some of the most significant skills of the decade. The field is growing quickly, and many are interested in taking on the challenge. Luckily, many resources online can get you there and are flexible, in-depth, and practical.

Understanding Natural Language Processing

Natural language processing is an element of artificial intelligence (AI) that deals with the interaction between computers and humans using natural language.

Traditionally, human-computer interactions have largely been done through predefined commands within terminals or graphical user interfaces that obscure the “commands” behind graphical interactions. These interactions work well and aren’t going away. However, instructing a computer by “speaking” to it has been within the realm of science fiction for decades but has also been a research goal of many computer scientists for a long while.

The goal of natural language processing is to read, decipher, understand, and make sense of human language in a valuable way. Recently, it has been integrated into everything from search engines figuring out what users are searching for to translating languages on the fly. It’s even a part of predictive text that finishes sentences, particularly on mobile phone keyboards. Taking a course in NLP gives you a new skill for the CV that opens doors to many employment positions across sectors.

Choosing the Right NLP Course Online

When searching for the right NLP course, consider what you need. Specifically, focus on these:

Curriculum
Instructors
Recognition
Experience

Does the curriculum cover the latest in NLP technology? The field evolves fast, sometimes with several breakthroughs or at least advancements a year. Course material and curriculum that’s several years behind might miss some of the new developments.

Are the instructors seasoned professionals? The more experience one has in the field, the better equipped they are to pass that knowledge down.

Is the NLP course recognized by industry leaders? It isn’t a matter of appealing to authority but rather knowing that the course is of high enough quality to be considered valuable and useful.

And let’s not forget about the hands-on experience. You can’t really learn NLP just by reading about it. It would be best to try your hands in real-life projects and workshops.

Most NLP courses will walk you through the basics of machine learning, algorithms that power NLP, and hands-on projects that solidify your knowledge.

OPIT offers a full NLP course as a part of the Master of Science (MSc) in Responsible Artificial Intelligence program. The course gives you a solid theoretical foundation and plenty of hands-on experience, presented by instructors who are experts in the field. The degree teaches you how to use NLP and use it ethically and responsibly.

A List of the Best NLP Online Courses

Here are some standout NLP online courses:

Coursera’s Natural Language Processing Specialization is for intermediate learners and spans over four months. It covers logistic regression, naive Bayes, word vectors, sentiment analysis, and more. The program is a comprehensive one that combines theory with practical assignments.
Stanford Online’s Natural Language Processing with Deep Learning focuses on the cutting-edge intersection of NLP and deep learning. It’s an in-depth exploration of NLP’s fundamental concepts and its role in emerging technologies. This course is perfect for those looking to get a solid foundation in NLP from one of the leading institutions in the world.
edX Natural Language Processing Courses & Programs: edX provides an intro to NLP that covers core techniques and computational linguistics. Topics include text processing, text mining, sentiment analysis, and topic modeling. It’s a great starting point for beginners and offers a broad overview of what NLP entails.
DeepLearning.AI’s Natural Language Processing in TensorFlow on Coursera was designed by one of the pioneers in AI education. It offers practical insights into implementing NLP techniques using TensorFlow. The course covers processing text, representing sentences as vectors, and building NLP models.
Udacity’s Natural Language Processing Nanodegree is project-focused with hands-on NLP learning. It covers foundational NLP concepts, including text processing, part-of-speech tagging, and sentiment analysis.

While these are the best natural language processing courses online, OPIT’s MSc in Responsible Artificial Intelligence, including NLP as part of its curriculum, is unique. This program teaches you NLP and embeds it within the broader context of artificial intelligence development, AI ethics, and responsible use. It’s excellent for those who want to go beyond the technical aspects and consider the societal impacts of their work in AI.

Benefits of Enrolling in an NLP Online Course

Attending an NLP course online might give you more than traditional on-site education. One of the biggest advantages is flexibility. You can learn at your own pace and on your schedule.

Online courses also open up networking opportunities with peers and mentors from around the globe. These are connections that on-site education would not have the scope to provide. Moreover, completing an NLP course can significantly boost your career prospects, potentially leading to job promotions and salary increases.

NLP Certification and Career Opportunities

With this certification, you’re proving your ability to understand and manipulate natural language data, making you invaluable in roles from data science and AI development to UX/UI design and content strategy.

Companies, from tech giants to startups, are on the lookout for professionals who can bridge the gap between human communication and machine understanding. The demand also translates into diverse job opportunities, competitive salaries, and the potential to work on groundbreaking projects in AI, machine learning, marketing, research, finance, and customer experience, among others.

Natural Machine Communication

NLP leads many of today’s technological advancements, making skills in this area more valuable than ever. Natural language processing courses that equip students with skills in natural language processing, AI, and related fields are growing in both offer and popularity. Completing one of these NLP courses sets you on a course for a financially promising career path within one of the most prestigious tech fields.

Get the right education and get ready for the future. Check out OPIT’s NLP course offerings within the MSc in Responsible Artificial Intelligence program or as a subject within our other computer science degrees.

Share this post:

Search inside The Magazine
From the same author
CCN: Australia Tightens Crypto Oversight as Exchanges Expand, Testing Industry’s Appetite for Regulation
OPIT - Open Institute of Technology

March 31, 2025
Source:
- CCN, published on March 29th, 2025
By Kurt Robson

Over the past few months, Australia’s crypto industry has undergone a rapid transformation following the government’s proposal to establish a stricter set of digital asset regulations.

A series of recent enforcement measures and exchange launches highlight the growing maturation of Australia’s crypto landscape.

Experts remain divided on how the new rules will impact the country’s burgeoning digital asset industry.

New Crypto Regulation

On March 21, the Treasury Department said that crypto exchanges and custody services will now be classified under similar rules as other financial services in the country.

“Our legislative reforms will extend existing financial services laws to key digital asset platforms, but not to all of the digital asset ecosystem,” the Treasury said in a statement.

The rules impose similar regulations as other financial services in the country, such as obtaining a financial license, meeting minimum capital requirements, and safeguarding customer assets.

The proposal comes as Australian Prime Minister Anthony Albanese’s center-left Labor government prepares for a federal election on May 17.

Australia’s opposition party, led by Peter Dutton, has also vowed to make crypto regulation a top priority of the government’s agenda if it wins.

Australia’s Crypto Growth

Triple-A data shows that 9.6% of Australians already own digital assets, with some experts believing new rules will push further adoption.

Europe’s largest crypto exchange, WhiteBIT, announced it was entering the Australian market on Wednesday, March 26.

The company said that Australia was “an attractive landscape for crypto businesses” despite its complexity.

In March, Australia’s Swyftx announced it was acquiring New Zealand’s largest cryptocurrency exchange for an undisclosed sum.

According to the parties, the merger will create the second-largest platform in Australia by trading volume.

“Australia’s new regulatory framework is akin to rolling out the welcome mat for cryptocurrency exchanges,” Alexander Jader, professor of Digital Business at the Open Institute of Technology, told CCN.

“The clarity provided by these regulations is set to attract a wave of new entrants,” he added.

Jader said regulatory clarity was “the lifeblood of innovation.” He added that the new laws can expect an uptick “in both local and international exchanges looking to establish a foothold in the market.”

However, Zoe Wyatt, partner and head of Web3 and Disruptive Technology at Andersen LLP, believes that while the new rules will benefit more extensive exchanges looking for more precise guidelines, they will not “suddenly turn Australia into a global crypto hub.”

“The Web3 community is still largely looking to the U.S. in anticipation of a more crypto-friendly stance from the Trump administration,” Wyatt added.

Read the full article below:
- CCN
Check out OPIT degrees

BSc in Digital Business MSc in Digital Business and Innovation MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI
- Career aligned
- Fully Online
- EU-accredited institution
Read the article
Agenda Digitale: Generative AI in the Enterprise – A Guide to Conscious and Strategic Use
OPIT - Open Institute of Technology

March 31, 2025
Source:
- Agenda Digitale, published on March 28th, 2025
By Zorina Alliata, Professor of Responsible Artificial Intelligence e Digital Business & Innovation at OPIT – Open Institute of Technology

Integrating generative AI into your business means innovating, but also managing risks. Here’s how to choose the right approach to get value

The adoption of generative AI in the enterprise is growing rapidly, bringing innovation to decision-making, creativity and operations. However, to fully exploit its potential, it is essential to define clear objectives and adopt strategies that balance benefits and risks.

Over the course of my career, I have been fortunate to experience firsthand some major technological revolutions – from the internet boom to the “renaissance” of artificial intelligence a decade ago with machine learning.

However, I have never seen such a rapid rate of adoption as the one we are experiencing now, thanks to generative AI. Although this type of AI is not yet perfect and presents significant risks – such as so-called “hallucinations” or the possibility of generating toxic content – it fills a real need, both for people and for companies, generating a concrete impact on communication, creativity and decision-making processes.

Defining the Goals of Generative AI in the Enterprise

When we talk about AI, we must first ask ourselves what problems we really want to solve. As a teacher and consultant, I have always supported the importance of starting from the specific context of a company and its concrete objectives, without inventing solutions that are as “smart” as they are useless.

AI is a formidable tool to support different processes: from decision-making to optimizing operations or developing more accurate predictive analyses. But to have a significant impact on the business, you need to choose carefully which task to entrust it with, making sure that the solution also respects the security and privacy needs of your customers .

Understanding Generative AI to Adopt It Effectively

A widespread risk, in fact, is that of being guided by enthusiasm and deploying sophisticated technology where it is not really needed. For example, designing a system of reviews and recommendations for films requires a certain level of attention and consumer protection, but it is very different from an X-ray reading service to diagnose the presence of a tumor. In the second case, there is a huge ethical and medical risk at stake: it is necessary to adapt the design, control measures and governance of the AI to the sensitivity of the context in which it will be used.

The fact that generative AI is spreading so rapidly is a sign of its potential and, at the same time, a call for caution. This technology manages to amaze anyone who tries it: it drafts documents in a few seconds, summarizes or explains complex concepts, manages the processing of extremely complex data. It turns into a trusted assistant that, on the one hand, saves hours of work and, on the other, fosters creativity with unexpected suggestions or solutions.

Yet, it should not be forgotten that these systems can generate “hallucinated” content (i.e., completely incorrect), or show bias or linguistic toxicity where the starting data is not sufficient or adequately “clean”. Furthermore, working with AI models at scale is not at all trivial: many start-ups and entrepreneurs initially try a successful idea, but struggle to implement it on an infrastructure capable of supporting real workloads, with adequate governance measures and risk management strategies. It is crucial to adopt consolidated best practices, structure competent teams, define a solid operating model and a continuous maintenance plan for the system.

The Role of Generative AI in Supporting Business Decisions

One aspect that I find particularly interesting is the support that AI offers to business decisions. Algorithms can analyze a huge amount of data, simulating multiple scenarios and identifying patterns that are elusive to the human eye. This allows to mitigate biases and distortions – typical of exclusively human decision-making processes – and to predict risks and opportunities with greater objectivity.

At the same time, I believe that human intuition must remain key: data and numerical projections offer a starting point, but context, ethics and sensitivity towards collaborators and society remain elements of human relevance. The right balance between algorithmic analysis and strategic vision is the cornerstone of a responsible adoption of AI.

Industries Where Generative AI Is Transforming Business

As a professor of Responsible Artificial Intelligence and Digital Business & Innovation, I often see how some sectors are adopting AI extremely quickly. Many industries are already transforming rapidly. The financial sector, for example, has always been a pioneer in adopting new technologies: risk analysis, fraud prevention, algorithmic trading, and complex document management are areas where generative AI is proving to be very effective.

Healthcare and life sciences are taking advantage of AI advances in drug discovery, advanced diagnostics, and the analysis of large amounts of clinical data. Sectors such as retail, logistics, and education are also adopting AI to improve their processes and offer more personalized experiences. In light of this, I would say that no industry will be completely excluded from the changes: even “humanistic” professions, such as those related to medical care or psychological counseling, will be able to benefit from it as support, without AI completely replacing the relational and care component.

Integrating Generative AI into the Enterprise: Best Practices and Risk Management

A growing trend is the creation of specialized AI services AI-as-a-Service. These are based on large language models but are tailored to specific functionalities (writing, code checking, multimedia content production, research support, etc.). I personally use various AI-as-a-Service tools every day, deriving benefits from them for both teaching and research. I find this model particularly advantageous for small and medium-sized businesses, which can thus adopt AI solutions without having to invest heavily in infrastructure and specialized talent that are difficult to find.

Of course, adopting AI technologies requires companies to adopt a well-structured risk management strategy, covering key areas such as data protection, fairness and lack of bias in algorithms, transparency towards customers, protection of workers, definition of clear responsibilities regarding automated decisions and, last but not least, attention to environmental impact. Each AI model, especially if trained on huge amounts of data, can require significant energy consumption.

Furthermore, when we talk about generative AI and conversational models , we add concerns about possible inappropriate or harmful responses (so-called “hallucinations”), which must be managed by implementing filters, quality control and continuous monitoring processes. In other words, although AI can have disruptive and positive effects, the ultimate responsibility remains with humans and the companies that use it.

Read the full article below (in Italian):
- Agenda Digitale
Check out OPIT degrees

MSc in Digital Business and Innovation MSc in Responsible Artificial Intelligence MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI
- Career aligned
- Fully Online
- EU-accredited institution
Read the article
Categories
Authors

Francesco Profumo

John Loewen

Professor @OPIT, PhD in Computer Science, Former Professor @Chulalongkorn University and @Vancouver Island University. Location: Italy Teaches Computer Architectures (BSc).

Karim Bouzoubaa

Professor @ OPIT, Professor of Data Science & AI @ Mohammed V University in Rabat, PhD @ Laval University. Location: Canada, Morocco. Teaches Programming courses (BSc).

Khaled Elbehiery

Professor @ OPIT, Senior Director & Network Engineer @ Charter Communications, Professor @ DeVry University and @ Park University. Location: USA. Teaches: Computer Networks (BSc).

Lokesh Vij

Professor @ OPIT, Software Engineer @ Symantec, Faculty @ Seneca College. Location: Canada. Teaches Cloud Computing courses (BSc), Big Data and Cloud Computing Infrastructure (MSc).

Go to all authors

CCN: Australia Tightens Crypto Oversight as Exchanges Expand, Testing Industry’s Appetite for Regulation

OPIT - Open Institute of Technology

Mar 31, 2025 • 3 min read

Source:

CCN, published on March 29th, 2025

By Kurt Robson

Over the past few months, Australia’s crypto industry has undergone a rapid transformation following the government’s proposal to establish a stricter set of digital asset regulations.

A series of recent enforcement measures and exchange launches highlight the growing maturation of Australia’s crypto landscape.

Experts remain divided on how the new rules will impact the country’s burgeoning digital asset industry.

New Crypto Regulation

On March 21, the Treasury Department said that crypto exchanges and custody services will now be classified under similar rules as other financial services in the country.

“Our legislative reforms will extend existing financial services laws to key digital asset platforms, but not to all of the digital asset ecosystem,” the Treasury said in a statement.

The rules impose similar regulations as other financial services in the country, such as obtaining a financial license, meeting minimum capital requirements, and safeguarding customer assets.

The proposal comes as Australian Prime Minister Anthony Albanese’s center-left Labor government prepares for a federal election on May 17.

Australia’s opposition party, led by Peter Dutton, has also vowed to make crypto regulation a top priority of the government’s agenda if it wins.

Australia’s Crypto Growth

Triple-A data shows that 9.6% of Australians already own digital assets, with some experts believing new rules will push further adoption.

Europe’s largest crypto exchange, WhiteBIT, announced it was entering the Australian market on Wednesday, March 26.

The company said that Australia was “an attractive landscape for crypto businesses” despite its complexity.

In March, Australia’s Swyftx announced it was acquiring New Zealand’s largest cryptocurrency exchange for an undisclosed sum.

According to the parties, the merger will create the second-largest platform in Australia by trading volume.

“Australia’s new regulatory framework is akin to rolling out the welcome mat for cryptocurrency exchanges,” Alexander Jader, professor of Digital Business at the Open Institute of Technology, told CCN.

“The clarity provided by these regulations is set to attract a wave of new entrants,” he added.

Jader said regulatory clarity was “the lifeblood of innovation.” He added that the new laws can expect an uptick “in both local and international exchanges looking to establish a foothold in the market.”

However, Zoe Wyatt, partner and head of Web3 and Disruptive Technology at Andersen LLP, believes that while the new rules will benefit more extensive exchanges looking for more precise guidelines, they will not “suddenly turn Australia into a global crypto hub.”

“The Web3 community is still largely looking to the U.S. in anticipation of a more crypto-friendly stance from the Trump administration,” Wyatt added.

Read the full article below:

CCN

Read the article

Agenda Digitale: Generative AI in the Enterprise – A Guide to Conscious and Strategic Use

OPIT - Open Institute of Technology

Mar 31, 2025 • 6 min read

Source:

Agenda Digitale, published on March 28th, 2025

By Zorina Alliata, Professor of Responsible Artificial Intelligence e Digital Business & Innovation at OPIT – Open Institute of Technology

Integrating generative AI into your business means innovating, but also managing risks. Here’s how to choose the right approach to get value

The adoption of generative AI in the enterprise is growing rapidly, bringing innovation to decision-making, creativity and operations. However, to fully exploit its potential, it is essential to define clear objectives and adopt strategies that balance benefits and risks.

Over the course of my career, I have been fortunate to experience firsthand some major technological revolutions – from the internet boom to the “renaissance” of artificial intelligence a decade ago with machine learning.

However, I have never seen such a rapid rate of adoption as the one we are experiencing now, thanks to generative AI. Although this type of AI is not yet perfect and presents significant risks – such as so-called “hallucinations” or the possibility of generating toxic content – it fills a real need, both for people and for companies, generating a concrete impact on communication, creativity and decision-making processes.

Defining the Goals of Generative AI in the Enterprise

When we talk about AI, we must first ask ourselves what problems we really want to solve. As a teacher and consultant, I have always supported the importance of starting from the specific context of a company and its concrete objectives, without inventing solutions that are as “smart” as they are useless.

AI is a formidable tool to support different processes: from decision-making to optimizing operations or developing more accurate predictive analyses. But to have a significant impact on the business, you need to choose carefully which task to entrust it with, making sure that the solution also respects the security and privacy needs of your customers .

Understanding Generative AI to Adopt It Effectively

A widespread risk, in fact, is that of being guided by enthusiasm and deploying sophisticated technology where it is not really needed. For example, designing a system of reviews and recommendations for films requires a certain level of attention and consumer protection, but it is very different from an X-ray reading service to diagnose the presence of a tumor. In the second case, there is a huge ethical and medical risk at stake: it is necessary to adapt the design, control measures and governance of the AI to the sensitivity of the context in which it will be used.

The fact that generative AI is spreading so rapidly is a sign of its potential and, at the same time, a call for caution. This technology manages to amaze anyone who tries it: it drafts documents in a few seconds, summarizes or explains complex concepts, manages the processing of extremely complex data. It turns into a trusted assistant that, on the one hand, saves hours of work and, on the other, fosters creativity with unexpected suggestions or solutions.

Yet, it should not be forgotten that these systems can generate “hallucinated” content (i.e., completely incorrect), or show bias or linguistic toxicity where the starting data is not sufficient or adequately “clean”. Furthermore, working with AI models at scale is not at all trivial: many start-ups and entrepreneurs initially try a successful idea, but struggle to implement it on an infrastructure capable of supporting real workloads, with adequate governance measures and risk management strategies. It is crucial to adopt consolidated best practices, structure competent teams, define a solid operating model and a continuous maintenance plan for the system.

The Role of Generative AI in Supporting Business Decisions

One aspect that I find particularly interesting is the support that AI offers to business decisions. Algorithms can analyze a huge amount of data, simulating multiple scenarios and identifying patterns that are elusive to the human eye. This allows to mitigate biases and distortions – typical of exclusively human decision-making processes – and to predict risks and opportunities with greater objectivity.

At the same time, I believe that human intuition must remain key: data and numerical projections offer a starting point, but context, ethics and sensitivity towards collaborators and society remain elements of human relevance. The right balance between algorithmic analysis and strategic vision is the cornerstone of a responsible adoption of AI.

Industries Where Generative AI Is Transforming Business

As a professor of Responsible Artificial Intelligence and Digital Business & Innovation, I often see how some sectors are adopting AI extremely quickly. Many industries are already transforming rapidly. The financial sector, for example, has always been a pioneer in adopting new technologies: risk analysis, fraud prevention, algorithmic trading, and complex document management are areas where generative AI is proving to be very effective.

Healthcare and life sciences are taking advantage of AI advances in drug discovery, advanced diagnostics, and the analysis of large amounts of clinical data. Sectors such as retail, logistics, and education are also adopting AI to improve their processes and offer more personalized experiences. In light of this, I would say that no industry will be completely excluded from the changes: even “humanistic” professions, such as those related to medical care or psychological counseling, will be able to benefit from it as support, without AI completely replacing the relational and care component.

Integrating Generative AI into the Enterprise: Best Practices and Risk Management

A growing trend is the creation of specialized AI services AI-as-a-Service. These are based on large language models but are tailored to specific functionalities (writing, code checking, multimedia content production, research support, etc.). I personally use various AI-as-a-Service tools every day, deriving benefits from them for both teaching and research. I find this model particularly advantageous for small and medium-sized businesses, which can thus adopt AI solutions without having to invest heavily in infrastructure and specialized talent that are difficult to find.

Of course, adopting AI technologies requires companies to adopt a well-structured risk management strategy, covering key areas such as data protection, fairness and lack of bias in algorithms, transparency towards customers, protection of workers, definition of clear responsibilities regarding automated decisions and, last but not least, attention to environmental impact. Each AI model, especially if trained on huge amounts of data, can require significant energy consumption.

Furthermore, when we talk about generative AI and conversational models , we add concerns about possible inappropriate or harmful responses (so-called “hallucinations”), which must be managed by implementing filters, quality control and continuous monitoring processes. In other words, although AI can have disruptive and positive effects, the ultimate responsibility remains with humans and the companies that use it.

Read the full article below (in Italian):