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A lot of people have been interested in the use of language since the drop of well-known large language models (LLMs) such as ChatGPT. We are seeing how much impact these LLMs are having on our day-to-day lives, with some wanting to transition into the booming field.
However, when you’re looking to transition into a new career, the first thing you think about is the steps it takes to get your foot in the door. Sometimes these steps can be very expensive. You may have to go back to university, or enrol on accredited courses, etc.
It can be hard to want to elevate your career and upskill without looking at costs. With that being said, for those of you who are looking into Natural Language Processing (NLP), want to know more about it, or want to steer your career towards that direction, this blog is for you.
Data Science Fundamentals Specialization
Link: Data Science Fundamentals Specialization
Level: Beginner
Duration: 1 month at 10 hours a week
If you are new to the data science world or you need a touch up on your foundational knowledge of the sector, check out this beginner specialisation course provided by the University of California, Irvine.
In this course you will gain an overview of data science fundamentals, with a deep dive into key data science skills, techniques, and concepts. The course begins with foundational concepts such as analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, and then moves on to compare data science with 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, manipulation of data 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
Level: Intermediate
Duration: 4 hours
Another course if you need to get the fundamentals of NLP under your belt, check out this Intro to NLP in Python provided by DataCamp.
In this course, you’ll learn NLP basics, such as how to identify and separate words, how to extract topics in a text, and how to build your own fake news classifier. You’ll also learn how to use basic libraries such as NLTK, alongside libraries which utilize deep learning to solve common NLP problems. This course will give you the foundation to process and parse text as you move forward in your Python learning.
Building AI Powered Chatbots Without Programming
Link: Building AI Powered Chatbots Without Programming
Level: Beginner
Duration: 12 hours (approximately)
If you are more interested in NLP in respect of chatbots, this beginners level course provided by IBM will go through the benefits of chatbots and their usefulness in tasks such as customer support. You will learn how to create a useful chatbot without writing any code using Watson Assistant, as well as specify behaviour and tone to improve your chatbot and make it user-friendly. Develop, test and deploy a chatbot to a WordPress website and interact with it.
This individual course is part of two specialisation courses: IBM AI Developer Professional Certificate and AI Foundations for Everyone Specialization. If you are interested in a more in depth course, check them out.
Natural Language Processing Specialization
Link: Natural Language Processing Specialization
Level: Intermediate
Duration: 3 months at 10 hours a week
If you have a base understanding of NLP and you’re ready to refine your skills, check out this intermediate specialisation course provided by DeepLearning.AI.
In this course, you will use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.You will also use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words. It doesn’t stop there – you will use recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition as well as encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.
Natural Language Processing on Google Cloud
Link: Natural Language Processing on Google Cloud
Level: Advanced
Duration: 13 hours (approximately)
If you’re ready to take it to the next level and go a step further, check out this NLP course provided by Google. This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
You will also recognize the NLP products and the solutions on Google Cloud, create an end-to-end NLP workflow by using AutoML with Vertex AI. Learn how to build different NLP models including DNN, RNN, LSTM, and GRU by using TensorFlow. Recognize 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 on Google Cloud Specialization.
Wrapping Up
In this blog, I hope I was able to take you through a journey of the different courses you can take if you are interested in the NLP industry. If you know of any courses that you would recommend, drop them in the comments!
Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.
