10 Best Courses to learn Natural Language Processing in 2022
What is Natural Language Processing? Why should you learn Natural Language Processing? It is a question that often comes from laymen. Natural language processing is a field of computer science that gives machines the ability to understand human language.
It is an application of Artificial Intelligence that studies how humans speak and how computers should process information to determine the best way to process it. In other words, it helps us give machines instructions in an easily understandable format.
Therefore, I have created this list of the best Natural Language Processing courses for beginners and experts to learn about NLP. I have curated this list from leading platforms such as Udemy, Coursera, Codecademy, edX, Pluralsight, etc.
1. NLP — Natural Language Processing with Python — [Udemy]
This course will teach you how to become a world-class NLP practitioner using Python. You will learn how to conduct Natural Language Processing using Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more.
In this NLP course, you will learn the following:
- Learn to work with Text Files with Python.
- Learn how to work with PDF files in Python.
- Utilize Regular Expressions for pattern searching in text.
- Use Spacy for ultra-fast tokenization.
- Learn about Stemming and Lemmatization.
- Understand Vocabulary Matching with Spacy.
- Use Part of Speech Tagging to process raw text files automatically.
- Understand Named Entity Recognition.
- Visualize POS and NER with Spacy.
- Use SciKit-Learn for Text Classification.
- Use Latent Dirichlet Allocation for Topic Modelling.
- Learn about Non-negative Matrix Factorization.
- Use the Word2Vec algorithm.
- Use NLTK for Sentiment Analysis.
- Use Deep Learning to build out your own chatbot.
First, we’ll learn how to work with text files and PDFs with Python. Next, we’ll learn how to search for patterns in text files using regular expressions.
After that, we will cover the basics of Natural Language Processing using the Natural Language Toolkit for Python and the state-of-the-art Spacy library for ultrafast tokenization, parsing, entity recognition, and lemmatization.
Following that, we will explore machine learning using Scikit-Learn to determine positive versus negative movie reviews and spam versus legitimate email messages, for example.
Featuring over 11.5 hours of engaging content and a course rating of 4.6 out of 5, this is an excellent course to learn Natural Language Processing with Python. It includes a Certificate of Completion.
2. Natural Language Processing in TensorFlow — [Coursera]
It is essential for software developers to understand how to use the tools needed to build scalable AI-powered algorithms. The course teaches best practices for using TensorFlow, an open-source machine learning framework.
In this NLP course, you will learn the following:
- Build natural language processing systems using TensorFlow.
- Process text, including tokenization and representing sentences as vectors.
- Apply RNNs, GRUs, and LSTMs in TensorFlow.
- Train LSTMs on existing text to create original poetry and more.
This course uses TensorFlow to build natural language processing systems. In this course, you will learn how to tokenize the text and represent sentences as vectors so neural networks can process them. Furthermore, you will learn how to implement RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll train an LSTM to create original poetry using existing text!
Featuring over 24 hours of engaging content and a course rating of 4.6 out of 5, this is an excellent course to learn Natural Language Processing in TensorFlow. It includes a Certificate of Completion.
3. Natural Language Processing with PyTorch — [Linkedin Learning]
Python is a popular deep-learning tool used by companies like OpenAI and Microsoft for learning natural language processing. This course will introduce you to PyTorch basics in natural language processing (NLP).
This NLP course will cover the following topics:
- NLP with Deep Learning Introduction
- PyTorch Basics
- Guided Project: CNN Text Classification with PyTorch
In this course, you will learn how to turn text into data sets that can be fed into deep-learning models. Furthermore, it discusses using RNNs and CNNs in a text classification project.
Additionally, the course explains how hyperparameters and model layers can be tuned for more robust and accurate results, along with the differences between the two algorithms.
Featuring over 41 minutes of engaging content and a course rating of 4.2 out of 5, this is an excellent course to learn NLP with PyTorch. It includes a Certificate of Completion.
4. Machine Learning: Natural Language Processing in Python (V2) — [Udemy]
This course is divided into 4 parts. The first part will discuss vector models and text preprocessing methods, and you will discover why vectors are so critical to data science and AI. It also introduces you to neural embedding principles, including word2vec, and GloVe, which converts text to vectors using CountVectorizer and TF-IDF.
In this NLP course, you will learn the following:
- How to convert text into vectors using CountVectorizer, TF-IDF, word2vec, and GloVe.
- How to implement a document retrieval system/search engine/similarity search/vector similarity.
- Probability models, language models, and Markov models (a prerequisite for Transformers, BERT, and GPT-3).
- How to implement a cipher decryption algorithm using genetic algorithms and language modeling.
- How to implement spam detection.
- How to implement sentiment analysis.
- How to implement an article spinner.
- How to implement text summarization.
- How to implement latent semantic indexing.
- How to implement topic modeling with LDA, NMF, and SVD.
- Machine learning (Naive Bayes, Logistic Regression, PCA, SVD, Latent Dirichlet Allocation).
- How to use Python, Scikit-Learn, TensorFlow, and more for NLP.
- Text preprocessing, tokenization, stopwords, lemmatization, and stemming.
- Parts-of-speech (POS) tagging and named entity recognition (NER).
In part 2, we will discuss probability models and Markov chains. This course covers one of the most essential models in data science and machine learning. The technique has been applied in various fields, including finance, bioinformatics, reinforcement learning, and natural language processing.
In part 3, we will discuss machine learning methods. This course will cover classic NLP tasks like spam detection, sentiment analysis, latent semantics analysis, and topic modeling.
Featuring over 22 hours of engaging content and a course rating of 4.7 out of 5, this is an excellent course to learn Natural Language Processing in Python. It includes a Certificate of Completion.
5. Natural Language Processing with Classification and Vector Spaces — [Coursera]
This course will teach you how to perform sentiment analysis of tweets using logistic regression and then naive Bayes. In this course, you will use vector space models to define relationships between words, then use PCA to reduce the dimensionality of the vector space and visualize those relationships.
In this NLP course, you will learn the following:
- Sentiment Analysis with Logistic Regression
- Sentiment Analysis with Naïve Bayes
- Vector Space Models
- Machine Translation and Document Search
Furthermore, you will develop an approximate k-nearest neighbor search algorithm for English-to-French translation using precomputed word embeddings and locality-sensitive hashes.
Featuring over 34 hours of engaging content and a course rating of 4.6 out of 5, this is an excellent course to learn NLP. It includes a Certificate of Completion.
6. Hands-On Natural Language Processing — [Linkedin Learning]
The course is designed to help developers better understand and use text data. Students will apply major natural language processing techniques in a hands-on environment with this course. You will learn how to replicate the knowledge you gain in your work.
This NLP course will cover the following topics:
- Named Entity Recognition (NER)
- Topic Modeling
- Text Summarization
- Sentiment Analysis
In this course, students will learn about the process flow of each task, use cases, and coding demonstrations. This course will cover topics such as named entity recognition, text summarization, topic modeling, and sentiment analysis.
Featuring over 1 hour of engaging content and a course rating of 4.4 out of 5, this is an excellent course to learn NLP. It includes a Certificate of Completion.
7. Natural Language Processing (NLP) in Python with 8 Projects — [Udemy]
This course on natural language processing is project-based and is one of the most comprehensive on the market. First, you will get an overview of the entire course and how natural language processing works. Then, we’ll set up our online environment in Google Colab.
In this NLP course, you will learn the following:
- Natural Language Processing.
- Implement NLP-related tasks with Scikit-learn, NLTK, and SpaCy.
- Apply Machine Learning Model to Classify Text Data.
- Text Classification (Spam Detection, Amazon product Review Classification).
- Text Summarization (Turn 5000-word article into 200 Words).
- Calculate Sentiment Score from Recently Posted Tweet (Tweeter API).
- Refresh your Deep Learning Concepts (ANN, CNN & RNN).
- Build your Word Embedding (Word2vec) Model with Keras.
- Word Embeddings application with Google Pretrained Model.
- Spam Message Detection with Neural Network Based CNN and RNN Model.
- Automatic Text Generation using TensorFlow, Keras, and LSTM.
- Working with Text Files & PDF in Python (PyPDF2 module).
- Tokenization, Stemming, and Lemmatization.
- Stop Words, Parts of Speech (POS) Tagging with NLTK.
- Vocabulary, Matching, Named Entity Recognition (NER).
- Data Analysis with Numpy and Pandas.
- Data Visualization with Matplotlib library.
Following that, we will examine basic NLP tasks such as tokenization, lemmatization, stop word removal, entity recognition, and part-of-speech tagging. Additionally, we will learn how to use the Spacy and NLTK libraries to accomplish them.
Lastly, you can refresh your knowledge of Numpy and Pandas libraries, Data Visualization with Matplotlib library, Text File Processing, and PDF File Processing.
Featuring over 10.5 hours of engaging content and a course rating of 4.3 out of 5, this is an excellent course to learn Natural Language Processing in Python with projects. It includes a Certificate of Completion.
8. Performing NLP Tasks Using the Cloudmersive API in Python — [Educative]
This course introduces you to Cloudmersive’s NLP API, including semantic analysis, language detection, and translation between languages using API calls. Additionally, you’ll learn how to request a segmentation through the API and rephrase a sentence.
In this NLP course, you will learn the following:
- Develop a deep understanding of Natural Language Processing and its applications.
- Get a working knowledge of the Cloudmersive NLP API and its endpoints.
- Learn to detect and translate between languages using the NLP API.
- Learn to analyze semantics and transform text using the NLP API.
- Get hands-on experience integrating the Cloudmersive NLP API with a Django application.
The course will end with a demonstration of Natural Language Processing using the Cloudmersive NLP API in a Django application.
Featuring over 1.5 hours of engaging content, this is an excellent course to perform NLP tasks using the Cloudmersive API in Python. It includes a Certificate of Completion.
9. Getting Started with Natural Language Processing — [Codecademy]
Natural Language processing refers to the way computers handle human language. It is one of the fastest growing fields in our lives, from your virtual assistant suggesting a restaurant to that terrible autocorrect you sent your cousin. The goal of this course isn’t just to use NLP tools — it’s to create them!
This NLP course will cover the following topics:
- Welcome to the Natural Language Processing Skill Path
- Getting Started with Natural Language Processing
- Text Preprocessing
- Language Parsing
- Language Quantification
- Text Generation
- NLP Portfolio Project
Throughout the course, students will use language parsing tactics to find meaning and insight into the text. You will learn about different methods for generating text. Additionally, you will learn about neural networks commonly used in NLP.
Featuring over 3 weeks of engaging content, this is an excellent skill path to apply Natural Language Processing with Python. It includes a Certificate of Completion.
10. Natural Language Processing on Google Cloud — [Pluralsight]
Learn how Google Cloud can assist you in solving NLP problems. The course also covers methods, techniques, and tools for developing neural networks and NLP projects using Vertex AI and TensorFlow.
This NLP course will cover the following topics:
- NLP on Google Cloud
- NLP with Vertex AI
- Text representatation
- NLP models
- Advanced NLP models
Featuring over 3.3 hours of engaging content and a course rating of 4.5 out of 5, this is an excellent course to learn NLP on Google Cloud. It includes a Certificate of Completion.
Thank you for reading this article about the best Natural Language Processing courses. I hope this course curation helped you to pick the right course to learn about NLP. In case you want to explore more, you can check these articles:
