10 Key Skills You Need to Become an NLP Engineer

Pete Ford
Jul 24, 2023
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10 Key Skills You Need to Become an NLP Engineer

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Welcome to our comprehensive guide on “Key Skills Required to Become an NLP Engineer” But before we delve into the specifics, let's understand the basics. Natural Language Processing, or NLP, is a fascinating subfield of artificial intelligence that focuses on the interaction between humans and machines through language. 

It involves teaching machines to understand, interpret, and generate human language in a valuable way. An NLP Engineer, therefore, is a specialist who leverages their knowledge of machine learning, AI, linguistics, and computer science to design and develop systems or algorithms that can comprehend, analyze, and even mimic human language. 

They play a vital role in creating intelligent applications such as virtual assistants, chatbots, sentiment analysis tools, etc. This blog will provide a roadmap of essential skills you need to become a successful NLP engineer. So, whether you're a beginner or an experienced professional looking to switch careers, stay tuned for some invaluable insights!

10 Key Skills Required to Become an NLP Engineer for Professionals

Now that we've covered why NLP engineering holds such importance in the present-day technological landscape, it's time to shift our focus to the core of this guide - the essential skills you need to establish a successful career in NLP. 

These skills, which encompass technical knowledge, programming acumen, mathematical understanding, and soft skills, form the bedrock of an NLP Engineer's toolkit. Let's delve into the 10 skills required to become an NLP Engineer and provide insights on acquiring and nurturing them.

1. Proficiency in Python

Python's ease of use and readability has made it popular among programmers, especially those in data analysis and machine learning domains. In NLP, Python is highly valuable due to the rich ecosystem of libraries and tools it offers for text processing and analysis, such as NLTK, SpaCy, and Gensim. As an employee, gaining proficiency in Python equips you with the ability to implement NLP solutions efficiently.

Proficiency in Python

Python's syntax is designed to be readable and straightforward, making it a great language for beginners and experienced developers. It's also particularly well-suited for scripting and rapid application development in many areas on most platforms. A strong command of Python can help you optimize your code and extract the most performance from your algorithms.

Moreover, Python is a dynamic, high-level language that offers dynamic typing capabilities which can make your code more readable and user-friendly. Python's flexibility and ease of use can be a significant advantage in NLP tasks that require complex text processing and machine learning algorithms.

Statistics: According to a JetBrains survey, Python is the most popular language among data scientists, with 57% using it for data analysis.

Benefits: Proficiency in Python can help employees easily develop NLP models, handle large datasets, and streamline the data analysis process.

2. Understanding of Linguistics

In the context of NLP, understanding the complexities of language is just as important as understanding the technologies used to process and analyze it. Linguistics is the scientific study of language and its structure, including sounds (phonetics and phonology), word construction (morphology), sentence structure (syntax), and meaning (semantics). As an employee aspiring to become an NLP engineer, an understanding of linguistics can significantly enhance the efficacy of your models.

A solid grounding in linguistics can help you better understand the intricacies and nuances of human language. For instance, you'll be better equipped to understand why certain words are used in certain contexts or a particular phrase carries a particular meaning. This can be particularly valuable when working on tasks such as sentiment analysis or machine translation, where a deep understanding of language can lead to more accurate and nuanced models.

Furthermore, linguistics can offer insights into how to structure your NLP models. By understanding the underlying rules and structures of language, you can design your models to capture these structures better, leading to improved performance. From tokenization to handling ambiguities, a good grounding in linguistics provides the necessary framework for language processing tasks.

Statistics: In a linkedIn survey of data scientists, nearly 14% reported that their work involved NLP or text analytics, underscoring the importance of linguistic understanding in the field.

Benefits: A solid grounding in linguistics enables employees to understand and model human language's intricacies, facilitating more accurate and nuanced NLP models.

3. Knowledge of Machine Learning and Deep Learning

Machine Learning and Deep Learning form the backbone of many NLP tasks. Machine Learning is the concept where a machine can learn from examples and experiences without being explicitly programmed. Deep Learning, a subset of Machine Learning, takes inspiration from the human brain's working mechanism to process data.

Knowledge of Machine Learning and Deep Learning

With the help of machine learning and deep learning algorithms, NLP tasks like text classification, sentiment analysis, and machine translation can be performed. For example, machine learning algorithms such as logistic regression or support vector machines can classify text into categories. On the other hand, deep learning models, especially recurrent neural networks (RNNs) and transformers, have shown great promise in tasks like text generation and machine translation.

As an employee transitioning to an NLP role, understanding the nuances of these algorithms, from the mathematics that underpin them to the real-world issues they can face, such as overfitting or bias, is vital. Selecting the right model for a particular task, tuning its parameters effectively, and evaluating its performance are all skills greatly facilitated by a strong understanding of machine learning and deep learning.

Statistics: According to a Gartner report, 37% of organizations have implemented AI in some form, demonstrating the increasing relevance of machine learning skills.

Benefits: With machine learning and deep learning skills, employees can create sophisticated NLP models that improve over time, leading to more accurate predictions and analyses.

4. Familiarity with NLP Libraries

 In the rapidly evolving field of NLP, libraries, and frameworks that provide pre-built functionalities for common tasks can be a great boon. Libraries such as NLTK, SpaCy, and Gensim offer a wide range of functionalities for text processing, feature extraction, and model building, saving you the time and effort of building these from scratch.

NLTK, or Natural Language Toolkit, is a library that provides tools for symbolic and statistical natural language processing. It's a powerful tool for scripting Python and prototyping and easily carries out tasks like tokenization, stemming, and tagging. SpaCy, on the other hand, is designed to handle large-scale information extraction tasks and offers robust solutions for text processing.

As an employee, being familiar with these libraries speeds up the development process and allows you to focus more on problem-solving and less on low-level implementation details. Additionally, these libraries are often optimized for performance and include features for integrating with other parts of your data processing pipeline, making them highly beneficial for use in production environments.

Benefits: Familiarity with NLP libraries can significantly speed up the development process, allowing employees to focus more on problem-solving and less on low-level implementation details.

5. Ability to Work with Large Datasets

NLP algorithms typically require large amounts of data to train effectively. This data is often unstructured and comes in various forms, such as text documents, web pages, or social media posts. Working with such data - cleaning, preprocessing, and extracting useful features - is a key skill for an NLP engineer.

Ability to Work with Large Datasets

When working with large datasets, understanding how to use tools and frameworks that can handle the data efficiently is crucial. This includes knowledge of databases, querying languages, and big data frameworks like Hadoop or Spark. Additionally, writing efficient code that can process the data in a reasonable amount of time is an important skill.

Finally, more is needed to process the data simply - you must understand it. This involves exploratory data analysis, where you visualize the data and compute statistics to gain insights. In the context of NLP, this might include understanding the distribution of words in your text, identifying common themes or topics, or recognizing the sentiment of the text.

Benefits: Employees handling large datasets are more likely to develop robust, accurate NLP models that can perform well in real-world conditions.

6. Strong Mathematics Foundation

A strong foundation in mathematics, particularly linear algebra, calculus, and statistics, is crucial for understanding and implementing NLP algorithms. Linear algebra provides the mathematical framework for representing and manipulating data in high dimensions, which is common in NLP tasks.

Strong Mathematics Foundation

Calculus, particularly the concept of a derivative, is essential for understanding how machine learning models are trained. Training a model often involves finding the values of parameters that minimize a certain cost function, and this is typically done using a method called gradient descent, which relies on derivatives.

Statistics is vital for understanding the data you're working with and interpreting your models' results. Concepts such as probability distributions, hypothesis testing, and confidence intervals play a major role in understanding and working with the uncertainty inherent in data and models.

Benefits: A strong mathematical foundation allows employees to understand and modify NLP algorithms more effectively, leading to better performing models.

7. Experience with Deep Learning Frameworks

Deep learning frameworks such as TensorFlow and PyTorch provides a high-level interface for designing and training neural networks central to many NLP tasks. These frameworks handle the low-level details of computation, such as how to distribute the computation across multiple CPUs or GPUs, allowing you to focus on designing your model.

TensorFlow, developed by Google, is one of the most popular deep learning frameworks and has a large community and many resources available. On the other hand, PyTorch is known for its simplicity and ease of use, making it a popular choice for research and prototyping.

Experience with these frameworks is invaluable for an aspiring NLP engineer, as it allows for the efficient creation and testing of complex models. This includes the design of the models themselves and the data pipelines that feed data into the models, the training loops that adjust the model's parameters, and the evaluation procedures that assess the model's performance.

Statistics: In the kaggle machine learning and data science survey, TensorFlow and PyTorch were the most used deep learning frameworks.

Benefits: Experience with these frameworks enables employees to build and deploy robust, scalable NLP models more efficiently.

8. Problem-Solving Skills

Problem-solving skills are essential in NLP, as the field is about solving complex, real-world problems. This can involve everything from devising a novel algorithm to tackle a specific task to figuring out why a model is performing poorly and finding a way to improve it.

Problem-Solving Skills

In addition to being a key part of the job, problem-solving skills are crucial in learning. As the field of NLP is rapidly evolving, there's often not a straightforward or pre-defined answer to a problem. Being able to experiment, iterate, and learn from failures is critical to staying at the forefront of the field.

Problem-solving in NLP also often involves dealing with ambiguity and uncertainty. Human language is full of nuances and complexities, and an effective NLP engineer needs to be comfortable working in situations where there's only sometimes a clear right answer.

Benefits: Problem-solving skills enable employees to navigate obstacles and devise innovative solutions in their NLP projects.

9. Good Communication Skills

In any technical role, particularly in NLP, good communication skills are vital. As an NLP engineer, you'll likely be working as part of a team and must be able to communicate your ideas and findings effectively to your colleagues.

Good Communication Skills

Good communication is also essential when working with stakeholders, such as project managers or clients. They might need a technical background, and explaining complex concepts in a way that's easy for them to understand is an invaluable skill.

Finally, good communication skills can also help in the problem-solving process. By effectively communicating your ideas and listening to others, you can collectively develop more innovative solutions and avoid misunderstandings that could lead to problems.

Statistics: According to the national association of colleges and employers, verbal communication skills are the most important candidate attribute, according to 70% of employers.

Benefits: Good communication skills help employees work better in teams, facilitate understanding among stakeholders, and can lead to more effective problem-solving.

10. Continuous Learning

The field of NLP is rapidly evolving, with new research and technologies always emerging. A commitment to continuous learning is crucial to stay at the forefront of the field. This can involve keeping current with the latest research papers, learning new tools and technologies, or taking courses to deepen your understanding of certain topics.

Continuous Learning

Continuous learning not only helps you to improve your skills and knowledge, but it also helps you to adapt to changes in the field. With the pace of innovation in NLP, the tools and techniques that are popular today need to be updated in a few years, and learning and adapting to new things quickly is a valuable skill.

Additionally, continuous learning can also be a source of inspiration and motivation. By learning about new developments in the field, you can find new ideas and approaches to help you keep excited and engaged in your work.

Benefits: Employees engaged in continuous learning can stay at the cutting edge of NLP technology, drive innovation, and add more value to their organizations.

Pathway to Become an NLP Engineer

Now that we've deep-dived into the essential skills required for a successful NLP engineering career, it's time to discuss the roadmap. How do you acquire these skills and put them into practice? Whether you're a complete novice or looking to transition from another IT specialty, this section, "Pathway to Become an NLP Engineer," will outline various routes to get you to your goal. Let's explore the traditional and non-traditional pathways to becoming an NLP engineer.

Transitioning to a career in Natural Language Processing (NLP) as a working professional can be exciting. While the path may be challenging, it's achievable with strategic planning and dedication. Here is a step-by-step guide tailored for working professionals:

1. Leverage Your Current Skills

Your current skills can be a strong asset if you're already a working professional, especially in a related field such as IT, programming, or data analysis. Your knowledge in these areas can be a great foundation for the additional learning you'll need to undertake.

2. Enroll in Online Courses

Online courses are flexible for working professionals. Websites such as Edstellar, Coursera, edX, and Udacity offer comprehensive programming, machine learning, and NLP courses. The benefit of these courses is that they are self-paced and can fit around your existing commitments.

3. Learn Python and Java

Proficiency in Python and Java is critical for NLP. If you're unfamiliar with these languages, consider taking an online course or using coding practice websites.

4. Understand Machine Learning and Deep Learning

ML and Deep Learning form the backbone of NLP. Online courses like Andrew Ng's Machine Learning course on Coursera and the Deep Learning Specialization can provide you with a solid foundation in these areas.

5. Strengthen Your Mathematics

NLP relies heavily on mathematical concepts such as statistics, probability, and linear algebra. You may need to refresh or strengthen your understanding of these areas, which can be done through online resources.

6. Get Hands-On Experience

Practical experience is key. Use platforms like Kaggle to work on real-world datasets and NLP problems. This can also help you build a portfolio of projects you can show potential employers.

7. Earn Certifications

Earning certifications can validate your skills and make you stand out to employers. Consider enrolling in an NLP certification program, such as Edstellar's NLP Engineer Certification or the Natural Language Processing Specialization by deeplearning.ai on Coursera.

8. Network with NLP Professionals

Networking can open doors to opportunities and provide valuable insights into the field. Join NLP groups on LinkedIn or attend AI or NLP meetups in your areas.

Industry Certifications and Courses to Become NLP Engineers

Now that we've covered the pathway to becoming an NLP engineer, the next vital step is enriching your knowledge and validating your skills through certifications and courses. While the pathway gives you a broader perspective on acquiring the necessary skills and experience, industry-recognized certifications can provide a more focused and in-depth understanding of specific areas in NLP. 

There are numerous courses and certifications available that can help you gain a competitive edge in the NLP field. Here are the top three that stand out:

1. Certified NLP Practitioner (Neuro-Linguistic Programming) by The American Board of NLP

While this certification is not directly related to natural language processing in AI, it's valuable for those interested in understanding the interplay between AI and human thought processes. The course covers the fundamentals of Neuro-Linguistic Programming, a psychological approach that relates thoughts and language.

Benefits: This course's unique perspective can benefit tasks like sentiment analysis, where understanding human thought processes is crucial. It adds more depth to an NLP engineer's conventional technical skills.

2. Edstellar: Natural Language Processing Engineer Training

Edstellar's NLP Engineer Training is highly esteemed in the industry. It is designed to provide a comprehensive understanding of NLP fundamentals and practical, hands-on experience. The course covers text processing, syntax and semantics, NLP libraries, machine learning and deep learning techniques for NLP, and real-world project implementations. Edstellar's reputation for quality, rigor, and industry relevance makes this an excellent choice for beginners and professionals seeking to specialize in NLP.


Benefits: One of the key advantages of Edstellar is its focus on industry-relevant skills and tools. The curriculum is frequently updated to keep up with the evolving trends in NLP, ensuring you stay ahead of the curve. The course offers career support services like resume building, interview preparation, and access to a vast network of industry professionals and alumni. Choosing Edstellar’s program is an investment in a future-proof career.

3. Coursera: Applied Text Mining in Python 

This course is a part of the Applied Data Science with Python Specialization offered by the University of Michigan. It introduces you to the basic text mining techniques, applying these techniques for natural language processing, information retrieval, and machine learning.

Coursera: Applied Text Mining in Python

Benefits: The course's hands-on approach helps learners understand how to apply these techniques in real-world scenarios, making it a practical choice for aspiring NLP engineers.

Before attending the above NLP training, attend the following:

  1. Machine Learning Training
  2. Artificial Intelligence Training
  3. Data Science with Python Training
  4. Deep Learning Training


In conclusion, becoming an NLP engineer is an exciting journey with tremendous potential in today's data-driven world. The key lies in mastering skills, including programming, machine learning, deep learning, and linguistics. 

While the pathway to this career involves academic pursuits, self-learning, and hands-on projects, certifications from leading platforms such as Edstellar will further strengthen your proficiency and credentials in the field. As you embark on this career, remember the field of NLP is continuously evolving. So, staying updated and engaging in lifelong learning will keep you ahead in this dynamic profession.

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Pete Ford

Pete Ford is a corporate training specialist with over 15 years of experience. His expertise lies in training methodologies, employee engagement, and performance improvement. Pete has a proven track record of developing and implementing effective training programs that enhance employee skills and productivity. His writings provide valuable insights into the world of corporate training, offering readers practical advice on how to design and deliver impactful training programs. His deep understanding of training dynamics makes him a trusted voice in the field.

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