Here’s a listicle of NLP courses and specializations to help you get started on your natural language processing journey!
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 learningis 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.
Math: Statistics, Probability, Calculus, Linear Algebra
Machine learning foundations
This is a semester-long course that covers a wide breadth of NLP topics:
Recurrent neural networks
Attention and subword models
Transformers and applications
💲 Pricing: Free ✅
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.
Machine learning and knowledge of deep learning frameworks
Calculus, Linear algebra, Statistics
The following are the courses in the specialization:
This course introduces the learners to the following:
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.
Familiarity with the Azure portal
The key tasks you’ll perform along the way include the following:
Named entity recognition
Key phrase extraction
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.
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.
Machine learning concepts
Neural networks with PyTorch (helpful but not required)
Here’s an overview of what the course covers:
Traditional NLP: This section covers text processing using regular expressions, matrix factorization techniques such as Singular Value Decomposition(SVD), and naive Bayes for text classification.
Neural network approaches to NLP: The course then covers recurrent neural networks, seq2seq models, attention mechanism, and transformer models
Ethical issues in NLP: This course also has lectures highlighting some ethical issues that stem from using natural language processing, such as bias and this disinformation.
💲 Pricing: Free
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 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)
Evaluation of language models
Seq2seq models and attention
Transfer learning for NLP
💲 Pricing: Free
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!👩🏽💻
Bala Priya is a developer and technical writer from India with over three years of experience in the technical content writing space. She shares her learning with the developer community by authoring tech tutorials, how-to guides, and more…. read more
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