The best Natural Language Processing online courses & Tutorials to Learn Natural Language Processing for beginners to advanced level.
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.
Disclosure: Coursesity is supported by the learners community. We may earn an affiliate commission when you make a purchase via links on Coursesity.
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
- Natural Language Processing and Text Mining Without Coding
- Natural Language Processing
- Natural Language Processing in TensorFlow
- Natural Language Processing (NLP)
- Natural Language Processing
- Natural Language Processing Fundamentals in Python
- Natural Language Processing
Practical Applications of NLP: spam detection, sentiment analysis, article spinners, and latent semantic analysis.
⭐ : 4.5 (6,110 ratings)
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.
Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets.
⭐ : 4.6 (3,734 ratings)
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 the 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: king - man = queen - woman France - Paris = England - London December - Novemeber = 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.
Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing.
⭐ : 4.5 (1,618 ratings)
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 chat bot
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 of text files. Afterwards 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, tokenization. Next we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in 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 the text information. Through state of the art visualization libraries we will be able 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 modelling, 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 chat bots.
You can take NLP - Natural Language Processing with Python Certificate Course on Udemy.
Learn Natural Language Processing ( NLP ) & Text Mining by creating text classifier, article summarizer, and many more.
⭐ : 4.4 (621 ratings)
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 personal spam filter or sentiment predictor
- Create personal text summarizer
In this course you will learn the various concepts of natural language processing by implementing them hands on in 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 which 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 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.
Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples.
⭐ : 4.4 (188 ratings)
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
- 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 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 on real data into 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, 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, barplots, 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 modelling. We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret 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 has been made available free of charge and you can use it 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 Twiter 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.
Analyze Text using Natural Language Processing ( NLP ) techniques & Text Mining without writing a single line of code.
⭐ : 4.6 (165 ratings)
Students will learn various Natural Language Processing techniques and build various real world examples all without writing a line of code. Most of the boom is using data that is organized and structured from your databases and spreadsheets but a huge opportunity awaits from the untapped unstructured text data (aka tweets, Facebook posts, blog posts, comments, SMS, chats, voice transcripts, etc.). Within the data science field, natural language processing is an extremely hot area in academia, startups and is just being started to be used widely within the mainstream of corporate America. Data Scientist job posting with natural language processing skills roughly doubled in 2016. Why is this great news for you? When you learn natural language processing and text mining, you will be among the elite few who can choose from a huge amount of career opportunities and a high 6-figure average salary. The sudden increase in demand for Data Scientists with natural language processing and text mining skills will create a huge gap in the coming few years. A Rare Opportunity to Quickly Learn Natural Language Processing and Text Mining at an Affordable Cost. No Previous Knowledge of Programming Required. Traditional Natural Language Processing and Text Mining requires students to know software programming, which enables them to write NLP algorithms. As a new learner the complexity of learning programming languages like Python or R can be demotivating and make you lose interest fast. But in this groundbreaking Udemy course, you’ll learn Natural Language Processing and Text Mining fundamentals and techniques without any coding whatsoever.
You will learn these basic concepts using a visual tool where you can just drag drop Natural Language Processing functions, hiding the ugliness of code, as a result, it’s much easier and faster to learn. There’s literally no other course on the market that teaches Machine Learning without the need for programming knowledge or coding using Rapidminer. Happily, now you can shorten your learning curve and be on your way toward earning a 6-figure income with this groundbreaking Udemy training. We’ll Build Several Natural Language Processing Algorithms and Advanced Reports Using Text I’ll “hand-hold” you as we build from scratch several hands-on examples using Natural Language Processing algorithms used in the real world. I will explain where and how these algorithms are used. Learn Both the Theory and Application of Natural Language Processing The course will teach you those fundamental concepts of natural language processing by implementing practical exercises which are based on real world examples. You will learn the theory, but get hands on practice building these natural language processing algorithms. You’ll also get access to: The datasets used in all the exercises. The solution files of the completed exercises. Cheat sheets to help you remember the fundamental concepts. Join the class now! Who this course is for: Software Programmers or Data Analysts Trying To Learn Natural Language Processing and Text Mining Business Analysts With No Programming Background Yet Want To Learn Natural Language Processing and Text Mining Anyone Who Wants To Understand the Fundamentals of Natural Language Processing and Text Mining
You can take Natural Language Processing and Text Mining Without Coding Certificate Course on Udemy.
Learn Natural Language Processing from 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, dialogue state.
⭐ : 4.6 (440 ratings)
This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, 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 most hot topics in today’s NLP. You will build your own conversational chat-bot that will assist with search on 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 duplicates 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 attention mechanism in encoder-decoder neural networks. Core techniques are not treated as black boxes. On the contrary, you will get 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 the very state-of-the-art in NLP research. In this module we will have two parts: first, a broad overview of 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 flows 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 next words given some previous words. This task is called language modeling and it is used for suggests 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 "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 documents with topic models and how these models can be used for search and data exploration. Nearly any task in NLP can be formulates 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 details and you will see how attention technique resembles word alignment task in traditional pipeline. This week we will overview so-called task-oriented dialog systems like Apple Siri or Amazon Alexa. We will look in details at 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.
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.
⭐ : 4.6 (553 ratings)
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 Embeddings and labelled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space. This will begin the process of training a neural network to udnerstand sentiment in text -- and you'll begin by looking at movie reviews, training a neural network on texts that are labelled '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 labelled. 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 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.
This course is part of the Microsoft Professional Program in Artificial Intelligence. Natural language processing (NLP) is one of the most important technologies of the information age.
With this course, you will:
- Apply deep learning models to solve machine translation and conversation problems.
- Apply deep structured semantic models on information retrieval and natural language applications.
- Apply deep reinforcement learning models on natural language applications.
- Apply deep learning models on image captioning and visual question answering.
Understanding complex language utterances is also a crucial part of artificial intelligence. In this course, you will be given a thorough overview of Natural Language Processing and how to use classic machine learning methods. You will learn about Statistical Machine Translation as well as Deep Semantic Similarity Models (DSSM) and their applications. We will also discuss deep reinforcement learning techniques applied in NLP and Vision-Language Multimodal Intelligence.
You can take Natural Language Processing (NLP) Certificate Course on EDX.
From your virtual assistant recommending a restaurant to that terrible autocorrect you sent your parents, natural language processing (NLP) is a rapidly growing presence in our lives. NLP is all about how computers work with human language. Don’t just use NLP tools — make them!
With this course, you will:
- Learn natural language toolkit
- Learn praising with regular expression
- Learn bag-of-words language model
- Learn language prediction and text generation
- Learn advance NPL topics
This course provides an overview of main NLP concepts, and you will build a Python chatbot! But check back later, we will be adding more advanced content soon that will get you to the outcomes that you want.
You can take Natural Language Processing (NLP) Certificate Course on Codeacademy.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
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 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. This course includes regular expressions & word tokenization, Simple topic identification, Named-entity recognition, Building a "fake news" classifier.
You can take Natural Language Processing Fundamentals Certificate Course on Datacamp.
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, machine translation.
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