Natural Language Processing (NLP) lies at the intersection of computer science and computational linguistics. From sentiment analysis of customer reviews to drive marketing decisions to machine translation and chatbots, NLP is powering all sectors. If you’ve experience building machine learning models, you can add NLP to your toolbox to solve various problems: text summarization, question answering, natural language generation, and more. We’ll look at the broad skill requirements for NLP roles and then proceed to the curated list of resources to get started with natural language processing.
NLP Career Paths: NLP Engineer, NLP Developer, and More
Advances in research have fueled the development of modern NLP techniques. With an average salary of over 117K USD, NLP engineer and developer roles have recently gained popularity. The skill set is diverse, from data collection for downstream NLP tasks and a working knowledge of linguistics concepts, such as dependency parsing and Part-of-Speech (POS) tagging, to a working knowledge of transformer models. To get into NLP, proficiency in programming and machine learning is required. You should also have experience with deep learning frameworks such as PyTorch and TensorFlow and NLP libraries like spaCy and HuggingFace.
Natural Language Processing (NLP) Courses
Next, let’s go over some of the best courses available across popular learning platforms. We’ll also state the prerequisites you need to get the most out of these courses. 👩🏫
CS224n: NLP with Deep Learning
Taught by Prof. Chris Manning, CS224n: NLP with Deep Learning, offered at Stanford, is one of the best courses to learn natural language processing. The lectures are available on YouTube, and the lecture notes and exercise notebooks—from the current and previous offerings—are freely available on the course website. 📋 Prerequisites
Python programming Math: Statistics, Probability, Calculus, Linear Algebra Machine learning foundations
This is a semester-long course that covers a wide breadth of NLP topics:
Word vectors Recurrent neural networks Attention and subword models Transformers and applications
NLP Specialization: Coursera
The Natural Language Processing Specialization by DeepLearning.AI on Coursera is one of the popular learning resources. This specialization aims to teach traditional NLP techniques through four courses to the most recent advances, such as transformer and reformer models. 📋 Prerequisites
Intermediate Python Machine learning and knowledge of deep learning frameworks Calculus, Linear algebra, Statistics
The following are the courses in the specialization:
NLP with Classification and Vector Spaces NLP with Probabilistic models NLP with Sequence Models NLP with Attention Models
Each course in the specialization it takes over 30 hours to complete and takes a few months to complete the entire specialization. 👩🏽💻 Here are some of the projects you’ll build as you work through this specialization:
Text autocomplete model Question Answering using BERT Text summarization Chatbot using reformer model
NLP in TensorFlow: Coursera
If you’re already familiar with TensorFlow, you can take the NLP in TensorFlow by DeepLearning.AI on Coursera to build NLP models with TensorFlow. 📋 Prerequisites
Python and Math Working knowledge of TensorFlow
The course covers the following:
Use of TensorFlow APIs for text tokenization and preprocessing Word embeddings Natural language generation
Sequence Models: Coursera
The Sequence Models course by DeepLearning.AI on Coursera in the Deep Learning Specialization is designed to equip the learners with a working knowledge of NLP over a 4-week period. 📋 Prerequisites
Python Machine Learning and Linear Algebra
The course covers sequence models for NLP with a focus on the following:
Character-level recurrent neural networks (RNNs) for language modeling Intro to attention mechanism, self and multi-head attention Using Hugging Face transformers for question answering
NLP: Hugging Face
The Hugging Face team released a free NLP course, covering basic to advanced concepts, focusing on working with the Hugging Face ecosystem. 📋 Prerequisites
Proficiency in Python Working knowledge of deep learning Experience with PyTorch and TensorFlow (helpful but not required)
The course has 12 chapters and is divided into three sections covering the following:
Using Hugging Face transformers Understanding Datasets and Tokenizers libraries Advanced applications of transformers, optimizing models for production
You have access to short video lectures, text-based sections for concepts, and colab notebooks.
NLP on Google Cloud: Pluralsight
NLP on Google Cloud introduces the learners to building NLP solutions using Vertex AI on the Google Cloud platform. Prerequisite: Working knowledge of GCP This course introduces the learners to the following:
Text representation Working with the DialogFlow API Building neural networks, recurrent neural networks (RNNs), Long Short Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) Using Vertex AI Attention mechanism and large language models
Build an NLP Solution with Azure
Building an NLP Solution with Microsoft Azure is a project-based course on Pluralsight. In this project-based course, you’ll learn to build an NLP solution by processing tweet datasets of customer reviews. 📋 Prerequisites
Python programming Familiarity with the Azure portal
The key tasks you’ll perform along the way include the following:
Language detection Named entity recognition Key phrase extraction Sentiment analysis
NLP with PyTorch: Pluralsight
NLP with PyTorch on Pluralsight will help you get started with NLP. This course does not cover the more recent transformer architecture but covers a lot of ground on natural language processing with PyTorch. Prerequisite: Familiarity with PyTorch This course covers the following:
Recurrent neural networks (RNNs) Binary and multi-class text classification Word vector embeddings Sentiment analysis using word vectors Sequence-to-sequence models for language translation
Becoming an NLP Expert: Udacity
Becoming a NLP Expert is the official natural language processing nano degree offered by Udacity School of AI. This nano degree program will help you learn both traditional and modern NLP techniques, such as attention by building projects. 📋 Prerequisites
Python programming Statistics Machine learning and deep learning
Udacity’s programs consist of video lectures, coding exercises, and capstone projects. In this natural language processing course, you’ll build the following projects:
Part of Speech tagging (POS Tagging) The end-to-end machine translation model Speech recognition model
A Code-First Introduction to NLP
A Code-First Introduction to NLP is a great course by fast.ai if you’d like to gain familiarity with the realm of NLP. This course is taught by Rachel Thomas, and it covers traditional and neural network approaches to natural language processing. 📋 Prerequisites
Python Programming Machine learning concepts Neural networks with PyTorch (helpful but not required)
Here’s an overview of what the course covers:
NLP with Machine Learning: Educative
This NLP with Machine Learning, by Educative, focuses on getting the learners familiar with important concepts in NLP. From coding interview prep and system design to machine learning, Educative is one of the popular online learning platforms. The course covers the following:
Word embeddings Language models Text classification Seq2seq models
NLP in Python: DataCamp
Natural Language Processing in Python by Datacamp is a structured skill track of six courses. These courses introduce the learners to different aspects of natural language processing. 📋 Prerequisites
Proficiency in Python Understanding of machine learning
This track consists of the following courses:
NLP Course: Lena Voita
The NLP Course is an extension of the natural language processing course that the author, Lena Voita, teaches at the Yandex School of Data Analysis. The course is organized into sections and contains interactive lessons and blog posts. In addition, there are notebooks and summaries of research papers.
Text classification (both traditional and neural network approaches) Word embeddings Evaluation of language models Seq2seq models and attention Transfer learning for NLP
Conclusion
I hope you found this listicle of learning resources helpful. Based on the prerequisites and time commitment, you can choose the course or specialization that best aligns with your interests. Once you’ve gained foundational knowledge, be sure to build projects on real-world datasets to supplement and reinforce your understanding. Happy coding!👩🏽💻 Next, check out the list of data science notebooks you can use for your next NLP project!
