Help you win over your peers! A roundup of the top NLP skills, frameworks, platforms and languages ​​in 2023

Bosses are looking for specific skill sets, expertise, and workflows that are platform-agnostic.

The chart below shows 20 in-demand skills, including NLP fundamentals and broader data science expertise.

Natural Language Processing Basics (NLP)

As shown in the chart, the NLP skill that bosses value most is NLP basic knowledge. Employer demand for people with data analysis skills has surged compared to last year.

This means we can’t just understand the platform, but understand how NLP operates as a core skill. If you don’t know how to apply core NLP skills such as Transformer, classification, linguistics, question answering, sentiment analysis, topic modeling, machine translation, speech recognition, named entity recognition, etc., then there is no point in knowing how spaCy works. .

Machine learning and deep learning

Machine learning is the basic data science skill, while deep learning is NLP Base.

Mastering both will demonstrate that you understand data science and, by extension, NLP. Because most employers hope that talents can use pre-trained models and Transformers.

Research

NLP needs to keep up to date with the latest papers and models. Companies find NLP to be one of the best applications of AI, regardless of industry.

Thus, understanding or finding the right models, tools, and frameworks to apply to the many different use cases of NLP requires a strong research focus.

Data Science Fundamentals

After understanding the core skill of machine learning, learn the basics of programming and computer science , will show that you have a solid foundation in the field. Computer science, mathematics, statistics, programming and software development are all skills required in NLP projects.

Cloud Computing, APIs and Data Engineering

NLP experts don’t do it directly on their personal laptops emotion analysis. Employers are looking for NLP experts who can handle more of the full stack of data engineering, such as using APIs, building data pipelines, building workflow management, and doing it all on a cloud-based platform.

NLP Platforms and Tools

In addition to skills and expertise, there are also specific platforms, tools and languages ​​that bosses value particularly. The chart below shows the platforms that are popular right now. This list does not cover all platforms, so it is best to update and look for popular new tools and frameworks.

Machine learning framework

In addition to general machine learning and deep learning In addition, there are some frameworks that are the core of NLP projects.

TensorFlow is favored for its flexibility in ML and neural networks, PyTorch is popular for its ease of use and innate design in NLP, and scikit-learn is widely used for classification and clustering.

Although only knowing one platform is already very powerful, by understanding these three or more platforms, we can become more flexible and adaptable, so that we will More competitive.

Compared to last year, PyTorch is now the most popular machine learning framework and has slowly surpassed TensorFlow/Keras to become the first choice for ML tasks.

Natural Language Processing Framework

To understand NLP more specifically, here are several frameworks that are useful for professionals who want to become NLP it works.

NLTK is appreciated for its broader nature due to its ability to extract the right algorithm for almost any job.

Meanwhile, spaCy is appreciated for its ability to handle multiple languages ​​and its ability to support word vectors.

The new addition is Apache OpenNLP, which is mainly used for common NLP tasks and simple operations, and CoreNLP is used for Java.

Surprisingly, HuggingFace transformers with deep learning architecture were not on last year’s list.

BERT has remained extremely popular over the past few years, and even though Google’s last update was in late 2019, it is still widely used.

BERT stands out because of its strong affinity for question-answering and context-based similarity searches, allowing it to perform equally well in chatbots and other related applications. reliable.

BERT even considers the context of words, allowing for more accurate results relevant to individual queries and tasks.

Data Engineering Platform

Data pipelines facilitate the flow of textual data, especially for real-time data streaming and cloud-based applications. And in this area, even as other platforms continue to grow, Spark remains the leader in data pipelines.

It even has a more specific version, Spark NLP. It is a library specifically designed for language tasks. Spark NLP is heavily used especially in the healthcare field - a data-rich field with a lot of medical records and medical data.

NLP Programming Language

As the preferred programming language for NLP, Python has always been far ahead. This should not be surprising. .

Many popular NLP frameworks, such as NLTK and spaCy, are based on Python, so it makes sense to become an expert in Python's companion language.

Knowing some SQL is also essential. Java has many libraries designed for the language, including CoreNLP, OpenNLP, etc.

Natural Language Processing Cloud Platform

Cloud services are the norm in 2022, so we see some service providers becoming getting more popular.

AWS Cloud, Azure Cloud, and others are compatible with many other frameworks and languages, making them a must-have for all NLP skills. At the same time, Google Cloud was starting to make its mark.

Overall, if you want to light up a new skill point and acquire a rare, timeless, and widely used skill, then maybe now is the time to learn it NLP or other core data science skills!

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