12 Best Natural Language Processing Courses & Tutorials to Learn Online
Highly curated best Natural Language Processing tutorials for beginners. start with the best Natural Language Processing tutorials and learn Natural Language Processing as beginners.
The best Natural Language Processing online courses & Tutorials to Learn Natural Language Processing for beginners to advanced levels.
Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software.
The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers.
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Top Natural Language Processing Courses, Tutorials, Certifications List
- Data Science: Natural Language Processing (NLP) in Python
- Natural Language Processing with Deep Learning in Python
- NLP - Natural Language Processing with Python
- Hands On Natural Language Processing (NLP) using Python
- Data Science:Data Mining & Natural Language Processing in R
- NLP with Python for Machine Learning Essential Training Online Class
- Natural Language Processing
- Natural Language Processing in TensorFlow
- Text Mining and Natural Language Processing in R
- Deep Learning Foundations: Natural Language Processing with TensorFlow Online Class
- Natural Language Processing Fundamentals in Python
- Natural Language Processing
1. Data Science: Natural Language Processing (NLP) in Python
Practical Applications of NLP: spam detection, sentiment analysis, article spinners, and latent semantic analysis.
- Course rating: 4.6 out of 5.0 (11,273 Ratings total)
- Duration: 10 Hours
- Certificate: Certificate of completion
With this course, you will:
- Write your own spam detection code in Python
- Write your own sentiment analysis code in Python
- Perform latent semantic analysis or latent semantic indexing in Python
- Have an idea of how to write your own article spinner in Python
In this course, you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight-up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a spam detector. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these. Next we'll build a model for sentiment analysis in Python. This is something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market. We'll go over some practical tools and techniques like the NLTK (natural language toolkit) library and latent semantic analysis or LSA. Finally, we end the course by building an article spinner.
This is a very hard problem and even the most popular products out there these days don't get it right. These lectures are designed to just get you started and to give you ideas for how you might improve on them yourself. Once mastered, you can use it as an SEO, or search engine optimization tool. Internet marketers everywhere will love you if you can do this for them! This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. Suggested Prerequisites: Python coding: if/else, loops, lists, dicts, sets Take the free Numpy prerequisites course to learn about Numpy, Matplotlib, Pandas, and Scikit-Learn, as well as Machine Learning basics Optional: If you want to understand the math parts, linear algebra and probability are helpful TIPS (for getting through the course): Watch it at 2x. Take handwritten notes.
You can take Data Science: Natural Language Processing (NLP) in Python Certificate Course on Udemy.
2. Natural Language Processing
go with Deep Learning in Python
Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets.
- Course rating: 4.5 out of 5.0 (6,165 Ratings total)
- Duration: 12 Hours
- Certificate: Certificate of completion
With this course, you will:
- Understand and implement word2vec
- Understand the CBOW method in word2vec
- Understand the skip-gram method in word2vec
- Understand the negative sampling optimization in word2vec
- Understand and implement GloVe using gradient descent and alternating least squares
- Use recurrent neural networks for parts-of-speech tagging
- Use recurrent neural networks for named entity recognition
- Understand and implement recursive neural networks for sentiment analysis
- Understand and implement recursive neural tensor networks for sentiment analysis
In this course, you will look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. We’ll learn not just 1, but 4 new architectures in this course. First up is word2vec.
This course will show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know. Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like a king - man = queen - woman France - Paris = England - London December - November = July - June.
The course is also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems. Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train. We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.
Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. See you in class! TIPS (for getting through the course): Watch it at 2x. Take handwritten notes. This will drastically increase your ability to retain the information. Write down the equations. If you don't, I guarantee it will just look like gibberish. Ask lots of questions on the discussion board. The more the better! Realize that most exercises will take you days or weeks to complete. Write code yourself, don't just sit there and look at my code. Suggested Prerequisites: calculus (taking derivatives) matrix addition, multiplication probability (conditional and joint distributions) Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own Can write a feedforward neural network in Theano and TensorFlow Can write a recurrent neural network / LSTM / GRU in Theano and TensorFlow from basic primitives, especially the scan function Helpful to have experience with tree algorithms.
You can take Natural Language Processing with Deep Learning in Python Certificate Course on Udemy.
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3. NLP - Natural Language Processing with Python
Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing.
- Course rating: 4.6 out of 5.0 (6,337 Ratings total)
- Duration: 11.5 Hours
- Certificate: Certificate of completion
With this course, you will:
- 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 automatically process raw text files
- 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
Welcome to the best Natural Language Processing course on the internet. This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course, we will cover everything you need to learn in order to become a world-class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside text files. Afterward, we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state-of-the-art Spacy library for ultra-fast tokenization, parsing, entity recognition, and lemmatization of text. We'll understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrase matching, and tokenization. Next, we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in a text to their appropriate part of speech, such as nouns, verbs, and adjectives, an essential part of building intelligent language systems. We'll also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying text information. Through state-of-the-art visualization libraries, we will be able to view these relationships in real time. Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews or spam versus legitimate email messages. We will expand this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modeling, where our machine learning models will detect topics and major concepts from raw text files. This course even covers advanced topics, such as sentiment analysis of text with the NLTK library, and creating semantic word vectors with the Word2Vec algorithm. Included in this course is an entire section devoted to state-of-the-art advanced topics, such as using deep learning to build out our own chatbots.
You can take NLP - Natural Language Processing with Python Certificate Course on Udemy.
4. Hands-On Natural Language Processing (NLP) using Python
Learn Natural Language Processing ( NLP ) & Text Mining by creating text classifiers, article summarizers, and many more.
- Course rating: 4.0 out of 5.0 (1,127 Ratings total)
- Duration: 10.5 Hours
- Certificate: Certificate of completion
With this course, you will:
- Understand the various concepts of natural language processing along with their implementation
- Build natural language processing-based applications
- Learn about the different modules available in Python for NLP
- Create a personal spam filter or sentiment predictor
- Create a personal text summarizer
In this course, you will learn the various concepts of natural language processing by implementing their hands-on in a python programming language. This course is completely project-based and from the start of the course, the main objective would be to learn all the concepts required to finish the different projects. You will be building a text classifier which you will use to predict sentiments of tweets in real-time and you will also be building an article summarizer that will fetch articles from websites and find the summary. Apart from these, you will also be doing a lot of mini-projects through out the course. So, at the end of the course, you will have a deep understanding of NLP and how it is applied in the real world. Who this course is for: Anyone willing to start a career in data science and natural language processing Anyone willing to learn the concepts of natural language processing by implementing them Anyone willing to learn Sentiment Analysis
You can take Hands-On Natural Language Processing (NLP) using Python Certificate Course on Udemy.
5.Data Science: Data Mining & Natural Language Processing in R
Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples.
- Course rating: 4.4 out of 5.0 (275 Ratings total)
- Duration: 13 Hours
- Certificate: Certificate of completion
With this course, you will:
- Perform the most important pre-processing tasks needed prior to machine learning in R
- Carry out data visualization in R
- Use machine learning for unsupervised classification in R
- Carry out supervised learning by building classification and regression models
in R - Evaluate the accuracy of supervised machine learning algorithms and compare their performance in R
- Carry out sentiment analysis using text data in R
MASTER DATA SCIENCE, TEXT MINING, AND NATURAL LANGUAGE PROCESSING IN R: Learn to carry out pre-processing, visualization, and machine learning tasks such as clustering, classification, and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. Many courses use made-up data that does not empower students to implement R-based data science in real life. After taking this course, you’ll easily use packages like the caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks. The underlying motivation for the course is to ensure you can apply R-based data science to real data in practice today. Start analyzing data for your own projects, whatever your skill level, and Impress your potential employers with actual examples of your data science projects. More specifically, here's what's covered in the course: Getting started with R, R Studio, and Rattle for implementing different data science techniques Data Structures, and Reading in Pandas, including CSV, Excel, JSON, and HTML data. How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes, etc. Creating data visualizations like histograms, boxplots, scatterplots, bar plots, pie/line charts, and MORE Statistical analysis, statistical inference, and the relationships between variables. Machine Learning, Supervised Learning, & Unsupervised Learning in R Neural Networks for Classification and Regression Web-Scraping using R Extracting text data from Twitter and Facebook using APIs Text mining Common Natural Language Processing techniques such as sentiment analysis and topic modeling. We will spend some time dealing with some of the theoretical concepts related to data science. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each video, you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course have been made available free of charge and you can use them as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW! Who this course is for: Students wishing to learn practical data science and machine learning in R Students wishing to learn the underlying theory and application of data mining in R Students interested in obtaining/mining data from sources such as Twitter Students interested in pre-processing and visualizing real-life data Students wishing to analyze and derive insights from text data Students interested in learning basic text mining and Natural Language Processing (NLP) in R.
You can take Data Science: Data Mining & Natural Language Processing in R Certificate Course on Udemy.
6. NLP with Python for Machine Learning Essential Training
A quick summary of basic natural language processing (NLP) concepts
- Course rating: 7,790 total enrollments
- Duration: 7 h
- Certificate: Certificate on completion
The course includes:
- NLP Basics
- Supplemental Data Cleaning
- Vectorizing Raw Data
- Feature Engineering
- Building Machine Learning Classifiers
This course can provide you with the knowledge you need to tackle complex problems using machine learning. It provides a quick summary of basic natural language processing (NLP) concepts, covers advanced data cleaning and vectorization techniques, and then takes a deep dive into building machine learning classifiers.
During this last step, you will learn how to build two different types of machine learning models, as well as how to evaluate and test variations of those models.
You can take NLP with Python for the Machine Learning Essential Training certification course on Linkedin Learning.
7. Natural Language Processing
Learn Natural Language Processing from the National Research University Higher School of Economics. This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, and dialogue state.
- Course rating: 4.5 out of 5.0 (732 Ratings total)
- Duration: 32 Hours
- Certificate: Certificate of completion
This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, and dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the hottest topics in today’s NLP. You will build your own conversational chatbot that will assist with searching on the StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicate detection. Throughout the lectures, we will aim at finding a balance between traditional and deep learning techniques in NLP and cover them in parallel. For example, we will discuss word alignment models in machine translation and see how similar it is to the attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get an in-depth understanding of what’s happening inside. To succeed in that, we expect your familiarity with the basics of linear algebra and probability theory, machine learning setup, and deep neural networks. Some materials are based on one-month-old papers and introduce you to very state-of-the-art NLP research. In this module we will have two parts: first, a broad overview of the NLP area and our course goals, and second, a text classification task. It is probably the most popular task that you would deal with in real life. It could be news flow classification, sentiment analysis, spam filtering, etc. You will learn how to go from raw texts to predicted classes both with traditional methods (e.g. linear classifiers) and deep learning techniques (e.g. Convolutional Neural Nets). In this module, we will treat texts as sequences of words. You will learn how to predict the next words given some previous words. This task is called language modeling and it is used for suggestions in search, machine translation, chat-bots, etc. Also, you will learn how to predict a sequence of tags for a sequence of words. It could be used to determine part-of-speech tags, named entities, or any other tags, e.g. ORIG and DEST in the "flights from Moscow to Zurich" query. We will cover methods based on probabilistic graphical models and deep learning. This module is devoted to a higher abstraction for texts: we will learn vectors that represent meanings. First, we will discuss traditional models of distributional semantics. They are based on a very intuitive idea: "you shall know the word by the company it keeps". Second, we will cover modern tools for word and sentence embeddings, such as word2vec, FastText, StarSpace, etc. Finally, we will discuss how to embed the whole document with topic models and how these models can be used for search and data exploration. Nearly any task in NLP can be formulated as a sequence-to-sequence task: machine translation, summarization, question answering, and many more. In this module, we will learn a general encoder-decoder-attention architecture that can be used to solve them. We will cover machine translation in more detail and you will see how the attention technique resembles word alignment tasks in a traditional pipeline. This week we will overview so-called task-oriented dialog systems like Apple Siri or Amazon Alexa. We will look in detail at the main building blocks of such systems namely Natural Language Understanding (NLU) and Dialog Manager (DM). We hope this week will encourage you to build your own dialog system as a final project!
You can take Natural Language Processing Certificate Course on Coursera.
8. Natural Language Processing in TensorFlow
Learn Natural Language Processing in TensorFlow from deeplearning.ai. If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you.
- Course rating: 4.6 out of 5.0 (4,703 Ratings total)
- Duration: 14 Hours
- Certificate: Certificate of completion
With this course, you will:
- 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
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an LSTM on existing text to create original poetry! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. The first step in understanding sentiment in text, and in particular when training a neural network to do so is the tokenization of that text. This is the process of converting the text into numeric values, with a number representing a word or a character. This week you'll learn about the Tokenizer and pad_sequences APIs in TensorFlow and how they can be used to prepare and encode text and sentences to get them ready for training neural networks! Last week you saw how to use the Tokenizer to prepare your text to be used by a neural network by converting words into numeric tokens and sequencing sentences from these tokens. This week you'll learn about Embeddings, where these tokens are mapped as vectors in a high dimension space. With Embedding and labeled examples, these vectors can then be tuned so that words with similar meanings will have a similar direction in the vector space. This will begin the process of training a neural network to understand the sentiment in the text -- and you'll begin by looking at movie reviews, training a neural network on texts that are labeled 'positive' or 'negative', and determining which words in a sentence drive those meanings. In the last couple of weeks, you looked first at Tokenizing words to get numeric values from them and then using Embeddings to group words of similar meaning depending on how they were labeled. This gave you a good, but rough, sentiment analysis -- words such as 'fun' and 'entertaining' might show up in a positive movie review, and 'boring' and 'dull' might show up in a negative one. But sentiment can also be determined by the sequence in which words appear. For example, you could have 'not fun', which of course is the opposite of 'fun'. This week you'll start digging into a variety of model formats that are used in training models to understand the context in sequence! Taking everything that you've learned in training a neural network based on NLP, we thought it might be a bit of fun to turn the tables away from classification and use your knowledge for prediction.
You can take Natural Language Processing in TensorFlow Certificate Course on Coursera.
9. Text Mining and Natural Language Processing in R
Hands-On Text Mining and Natural Language Processing (NLP) Training for Data Science Applications in R.
- Course ratings: 4.6 out of 5.0 (640 Ratings total)
- Duration: 8.5 Hours
- Certificate: Certificate of completion
In this course, you will learn how to:
- extract text data from websites.
- extract data from social media sites and carry out analysis of these using visualization, stats, machine learning, and deep learning.
The course includes:
- Data Structures and Reading in R, including CSV, Excel, JSON, and HTML data.
- Web-Scraping using R
- Extracting text data from Twitter and Facebook using APIs
- Extract and clean data from the FourSquare app
- Exploratory data analysis of textual data
- Common Natural Language Processing techniques such as sentiment analysis and topic modeling
- Implement machine learning techniques such as clustering, regression, and classification on textual data
- Network analysis
This course will help you implement the methods using real data obtained from different sources. After taking this course, you’ll easily use packages like the caret, dplyr to work with real data in R.
You will also learn to use the common social media mining and natural language processing packages to extract insights from text data. This course will even introduce you to some very important practical case studies - such as identifying important words in a text and predicting movie sentiments based on textual reviews.
You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation.
You can take Text Mining and Natural Language Processing in R Certificate Course on Eduonix.
10. Deep Learning Foundations: Natural Language Processing with TensorFlow Online Class
Learn foundational deep-learning techniques to classify, predict, and generate text using different neural networks.
- Course rating: 638 total enrollments
- Duration: 2 h
- Certificate: Certificate on completion
The course includes:
- Getting Started with NLP
- Text Classification
- Classification with RNNs and LSTMs
- Text Generation with RNNs
This NLP tutorial provides a complete guide to understanding NLP using recurrent neural networks (RNNs). It begins by introducing you to word encodings and using TensorFlow for tokenization.
Next, the course describes the important concept of word embeddings and shows you how to use TensorFlow to classify movie reviews and project vectors. It discusses RNNs and long short-term memory (LSTM), then shows you how to improve the movie review classifier from earlier in the course.
You can take Deep Learning Foundations: Natural Language Processing with TensorFlow Online Class certification course on Linkedin Learning.
11. Natural Language Processing Fundamentals in Python
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
- Course rating: 61728 total enrollments
- Duration: 4 Hours
In this course, you'll learn natural language processing (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 that utilize deep learning to solve common NLP problems. This course will give you the foundation to process and parse the text as you move forward in your Python learning. This course includes regular expressions & word tokenization, Simple topic identification, Named-entity recognition, and Building a "fake news" classifier.
You can take Natural Language Processing Fundamentals Certificate Course on Datacamp.
12. Natural Language Processing
Learn cutting-edge natural language processing techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as speech recognition, and machine translation.
- Course rating: 4.5 out of 5.0 (350 Ratings total)
- Duration: 150 Hours
- Certificate: Certificate of completion
This program requires experience with Python, statistics, machine learning, and deep learning. Learn text processing fundamentals, including stemming and lemmatization. Explore machine learning methods in sentiment analysis. Build a speech tagging model. Learn advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures. Learn voice user interface techniques that turn speech into text and vice versa. Build a speech recognition model using deep neural networks.
You can take Natural Language Processing Certificate Course on Udacity.
Image Source: Aliz AI
Thank you for reading this. We hope our course curation would help you to pick the right course to learn Natural Language Processing. In case you want to explore more, you can take the free Natural Language Processing courses.