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Many people have been interested in language usage since the release of well-known long language models (LLMs) such as ChatGPT. We are seeing the huge impact these LLMs are having on our daily lives, and some want to move into this booming field.
However, when looking for a new career, the first thing you think about is the steps you need to take to get started. Sometimes, these steps can be very costly. You may have to go back to school or enroll in accredited courses, etc.
It can be difficult to want to advance your career and improve your skills without considering the costs. That said, for those who are considering natural language processing (NLP), want to learn more about it, or want to steer their career in that direction, this blog is for you.
Specialization in Data Science Fundamentals
Link: Specialization in Data Science Fundamentals
Level: Beginner
Duration: 1 month at 10 hours per week
If you're new to data science or need to brush up on your industry basics, check out this beginner-friendly specialization course offered by the University of California, Irvine.
In this course, you will get an overview of the fundamentals of data science, with a deep dive into key data science skills, techniques, and concepts. The course starts with foundational concepts such as analytical taxonomy, the cross-industry standard process for data mining, and data diagnostics, and then moves on to compare data science to classical statistical techniques. The course also provides an overview of the most common techniques used in data science, including data analysis, statistical modeling, data engineering, data manipulation at scale (big data), algorithms for data mining, data quality, remediation, and consistency operations.
Introduction to Natural Language Processing in Python
Link: Introduction to Natural Language Processing in Python
Intermediate level
Duration: 4 hours
Another course if you need to learn the basics of NLP is this Introduction to NLP in Python provided by DataCamp.
In this course, you will learn the basics of NLP, such as identifying and separating words, extracting topics from text, and building your own fake news classifier. You will also learn how to use basic libraries such as NLTK, along with libraries that use deep learning to solve common NLP problems. This course will give you the foundation for processing and analyzing text as you progress through your Python learning.
How to create ai-powered chatbots without programming
Link: How to create ai-powered chatbots without programming
Level: Beginner
Duration: 12 hours (approximately)
If you're more interested in natural language processing as it relates to chatbots, this entry-level course provided by IBM will look at the benefits of chatbots and their usefulness in tasks such as customer support. You'll learn how to create a helpful chatbot without writing any code using Watson Assistant, as well as specify behavior and tone to improve your chatbot and make it easy to use. Develop, test, and deploy a chatbot on a WordPress website and interact with it.
This individual course is part of two specialization courses: IBM artificial intelligence Developer Professional Certificate and ai Fundamentals for the Specialization of AllIf you are interested in a more detailed course, check them out.
Specialization in Natural Language Processing
Link: Specialization in Natural Language Processing
Intermediate level
Duration: 3 months at 10 hours per week
If you have a basic understanding of NLP and are ready to hone your skills, check out this intermediate specialization course provided by DeepLearning.ai.
In this course, you’ll use logistic regression, naive Bayes, and word vectors to implement sentiment analysis, analogy completion, and word translation. You’ll also use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete, and identify part-of-speech tags for words. It doesn’t end there: you’ll use recurrent neural networks, LSTMs, GRUs, and Siamese networks in Trax for sentiment analysis, text generation, and named entity recognition, as well as encoder-decoder, causal, and self-attention to translate full sentences, summarize text, build chatbots, and answer questions.
Natural Language Processing on Google Cloud
Link: Natural Language Processing on Google Cloud
Advanced level
Duration: 13 hours (approximately)
If you're ready to take it to the next level and go one step further, check out this NLP course provided by Google. This course introduces the products and solutions for solving NLP problems on Google Cloud. It also explores the processes, techniques, and tools for developing an NLP project with neural networks using Vertex ai and TensorFlow.
You will also learn about NLP products and solutions on Google Cloud, build an end-to-end NLP workflow using AutoML with Vertex ai. Learn how to build different NLP models including DNN, RNN, LSTM, and GRU using TensorFlow. Learn about advanced NLP models such as Encoder-Decoder, Attention Mechanism, Transformers, and BERT, and understand transfer learning and apply pre-trained models to solve NLP problems.
This course is part of the Advanced Machine Learning Specialization on Google Cloud.
Ending
In this blog, I hope I have helped you navigate through the different courses you can take if you are interested in the NLP industry. If you know of any courses you would recommend, please leave them in the comments!
Nisha Arya Nisha is a data scientist, freelance technical writer, and a KDnuggets community editor and manager. She is especially interested in providing advice or tutorials on careers in data science and theoretical knowledge about data science. Nisha covers a wide range of topics and wishes to explore the different ways in which artificial intelligence can benefit the longevity of human life. Nisha is an enthusiastic learner and is looking to expand her technological knowledge and writing skills while helping to mentor others.
