Best Natural Language Processing Courses

Find the best online Natural Language Processing Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Natural Language Processing Courses.

Modern Natural Language Processing in Python

Solve Seq2Seq and Classification NLP tasks with Transformer and CNN using Tensorflow 2 in Google Colab

Created by Martin Jocqueviel - Freelance data scientist


Students: 46328, Price: $119.99

Students: 46328, Price:  Paid

Modern Natural Language Processing course is designed for anyone who wants to grow or start a new career and gain a strong background in NLP.

Nowadays, the industry is becoming more and more in need of NLP solutions. Chatbots and online automation, language modeling, event extraction, fraud detection on huge contracts are only a few examples of what is demanded today. Learning NLP is key to bring real solutions to the present and future needs.

Throughout this course, we will leverage the huge amount of speech and text data available online, and we will explore the main 3 and most powerful NLP applications, that will give you the power to successfully approach any real-world challenge.

  1. First, we will dive into CNNs to create a sentimental analysis application.

  2. Then we will go for Transformers, replacing RNNs, to create a language translation system.

The course is user-friendly and efficient: Modern NL leverages the latest technologies—Tensorflow 2.0 and Google Colab—assuring you that you won’t have any local machine/software version/compatibility issues and that you are using the most up-to-date tools.

NLP – Natural Language Processing with Python

Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing

Created by Jose Portilla - Head of Data Science, Pierian Data Inc.


Students: 44759, Price: $89.99

Students: 44759, Price:  Paid

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 and more!

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!

Not only do you get fantastic technical content with this course, but you will also get access to both our course related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants.

All of this comes with a 30 day money back garuantee, so you can try the course risk free.

What are you waiting for? Become an expert in natural language processing today!

I will see you inside the course,


Natural Language Processing with Deep Learning in Python

Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets

Created by Lazy Programmer Team - Artificial Intelligence and Machine Learning Engineer


Students: 39935, Price: $39.99

Students: 39935, Price:  Paid

In this course we are going to look at NLP (natural language processing) with deep learning.

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.

In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.

First up is word2vec.

In this course, I’m going to 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

For those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers.

We are 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!

"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

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 or TensorFlow

  • Can write a recurrent neural network / LSTM / GRU in Theano or TensorFlow from basic primitives, especially the scan function

  • Helpful to have experience with tree algorithms


  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Data Science: Natural Language Processing (NLP) in Python

Applications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis.

Created by Lazy Programmer Inc. - Artificial intelligence and machine learning engineer


Students: 38489, Price: $109.99

Students: 38489, Price:  Paid

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 cipher decryption algorithm. These have applications in warfare and espionage. We will learn how to build and apply several useful NLP tools in this section, namely, character-level language models (using the Markov principle), and genetic algorithms.

The second project, where we begin to use more traditional "machine learning", is to build 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.

"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

  • Python coding: if/else, loops, lists, dicts, sets

  • Take my free Numpy prerequisites course (it's FREE, no excuses!) 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


  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Deep Learning and NLP A-Z™: How to create a ChatBot

Learn the Theory and How to implement state of the art Deep Natural Language Processing models in Tensorflow and Python

Created by Hadelin de Ponteves - AI Entrepreneur


Students: 26039, Price: $109.99

Students: 26039, Price:  Paid

We've talked about, speculated and often seen different applications for Artificial Intelligence - But what about one piece of technology that will not only gather relevant information, better customer service and could even differentiate your business from the crowd?

ChatBots are here, and they came change and shape-shift how we've been conducting online business. Fortunately technology has advanced enough to make this a valuable tool something accessible that almost anybody can learn how to implement.

If you want to learn one of the most attractive, customizable and cutting edge pieces of technology available, then this course is just for you!

Natural Language Processing (NLP) with BERT

Movies reviews Semantic analysis using BERT

Created by Hadelin de Ponteves - AI Entrepreneur


Students: 18173, Price: Free

Students: 18173, Price:  Free

Are you ready to dive right into one of the most exciting developments in data science right now: Google’s breakthrough NLP algorithm, BERT!

Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.

Our new case study course: Natural Language Processing (NLP) with BERT shows you how to perform semantic analysis on movie reviews using data from one of the most visited websites in the world: IMDB!

Perform semantic analysis on a large dataset of movie reviews using the low-code Python library, Ktrain.

But, why is BERT so revolutionary?

Not only is it a framework that has been pre-trained with the biggest data set ever used, it is also remarkably easy to adapt to different NLP applications, by adding additional output layers. This allows users to create sophisticated and precise models to carry out a wide variety of NLP tasks.

AI expert Hadelin de Ponteves guides you through some basic components of Natural Language Processing, how to implement the BERT model and sentiment analysis, and finally, Python coding in Google Colab.

Here’s how this 1-hour case study course will unfold:

Part 1: Data Preprocessing

  • Loading the IMDB dataset

  • Creating the training and test sets

Part 2: Building the BERT model

Part 3: Training and evaluating the BERT model

  • Getting the learner instance

  • Training and evaluating the BERT model

Plus, you’ll do it all using Google’s Colab free, browser-based notebook environment that runs completely in the cloud. It’s a game-changing interface that will save you time and supercharge your data science toolkit.

If you’ve been waiting for a chance to put your NLP skills to the test then this is the opportunity you've been waiting for. Click the ‘Enroll Now’ button and see you inside!

Build a Web Application with Python, Flask and NLP

Share the joy of famous quotes with a cloud-based web app using natural language processing to hit the right mood!

Created by Manuel Amunategui - Data Scientist & Quantitative Developer


Students: 16705, Price: Free

Students: 16705, Price:  Free

Let's share the wonderful joy of famous quotes to the world with a quoting machine web application that uses natural language sentiment to tailor the right quote for the user.

The class will teach you how to take your Python ideas and extend them to the web into real Web Applications so the world can enjoy your work.

In this class, we will:

  • develop our ideas in a local Jupyter notebook

  • gather data (famous quotes)

  • use the Vader NLP sentiment algorithm

  • tune our models and dispensing mechanisms locally

  • design the look and feel

  • get graphics

  • extend responsive HTML templates

  • port to the web using PythonAnywhere

  • enjoy great quotes in tune with our moods 24/7

Above all, you will understand how you can port your own Python ideas to the web into fully interactive web applications so the world can enjoy your work!

Introduction to Natural Language Processing

Learn basics of Natural Language Processing (NLP), Regular Expressions and Text Pre-processing using Python

Created by Analytics Vidhya - Data Science Community


Students: 14780, Price: Free

Students: 14780, Price:  Free

More than 80% of the data in this world is unstructured in nature, which includes text. You need text mining and Natural Language processing (NLP) to make sense out of this data. Natural Language Processing (NLP) helps you extract insights from emails of customers, their tweets, text messages. Natural Language Processing (NLP) can power many applications, such as language translation, question answering systems, chatbots and document summarisers.

What would you learn in Introduction to Natural Language Processing (NLP) with Python course?

  • Reading and working with text data using Python

  • Learn to use Regular Expressions to extract patterns from text

  • Text pre-processing

  • Text classification

Natural Language Processing (NLP) for Beginners Using NLTK

Your journey to NLP mastery starts here

Created by Harshal Samant - Engineer and a Machine Learning Enthusiast


Students: 13312, Price: Free

Students: 13312, Price:  Free

In this video series, we will start with in introduction to corpus we have at our disposal through NLTK. Once we download the corpus and learn different tricks to access it, we will move on to very useful feature in NLP called frequency distribution. In this section, we will see how calculate, tabulate and plot  frequency distribution of words. In the next section, we will start learning NLP specific techniques that include:

1. Stemming

2. Lemmatization

3. Tokenization

Natural Language Processing: NLP With Transformers in Python

Learn next-generation NLP with transformers for sentiment analysis, Q&A, similarity search, NER, and more

Created by James Briggs - Data scientist


Students: 11647, Price: $59.99

Students: 11647, Price:  Paid

Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.

In this course, we learn all you need to know to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR.

We cover several key NLP frameworks including:

  • HuggingFace's Transformers

  • TensorFlow 2

  • PyTorch

  • spaCy

  • NLTK

  • Flair

And learn how to apply transformers to some of the most popular NLP use-cases:

  • Language classification/sentiment analysis

  • Named entity recognition (NER)

  • Question and Answering

  • Similarity/comparative learning

Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.

All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:

  • History of NLP and where transformers come from

  • Common preprocessing techniques for NLP

  • The theory behind transformers

  • How to fine-tune transformers

We cover all this and more, I look forward to seeing you in the course!

Introduction to Natural Language Processing (NLP)

Learn how to analyze text data.

Created by Brian Sacash - Data Scientist


Students: 10073, Price: $89.99

Students: 10073, Price:  Paid

This course introduces Natural Language Processing through the use of python and the Natural Language Tool Kit. Through a practical approach, you'll get hands on experience working with and analyzing text.

As a student of this course, you'll get updates for free, which include lecture revisions, new code examples, and new data projects.

By the end of this course you will:

  • Have an understanding of how to use the Natural Language Tool Kit.
  • Be able to load and manipulate your own text data.
  • Know how to formulate solutions to text based problems.
  • Know when it is appropriate to apply solutions such as sentiment analysis and classification techniques.

Hands On Natural Language Processing (NLP) using Python

Learn Natural Language Processing ( NLP ) & Text Mining by creating text classifier, article summarizer, and many more.

Created by Next Edge Coding - Full Stack Developer & Data Enthusiast


Students: 7442, Price: $89.99

Students: 7442, Price:  Paid

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.

Natural Language Processing:Concept along with Case Study

Free Course: Natural Language Processing (NLP), Text Processing, Machine Learning, Spam Filter [Python]

Created by Rishi Bansal - Senior Developer


Students: 3916, Price: Free

Students: 3916, Price:  Free

This course provides a basic understanding of NLP. Anyone can opt for this course. No prior understanding of NLP is required.  Text Processing like Tokenization, Stop Words Removal, Stemming, different types of Vectorizers, WSD, etc are explained in detail with python code. Also difference between CountVectorizer and Hashing in Spam Filter.

Natural Language Processing

Learn the basics of NLP, Spark, Apache

Created by HotCubator Academy - A think tank in Data, Research and Entrepreneurial thinking


Students: 3743, Price: Free

Students: 3743, Price:  Free

An astounding amount of unstructured text data is generated every day by the internet, social media platforms, and a variety of other sources. To harness the real value of such large volume of textual data, it is absolutely necessary to have the skills to turn messy texts into powerful insights.

Welcome to this FREE course on Natural Language Processing which will give a very basic idea. If you have some experience with Python and interest in natural language processing (NLP), this course can provide you with the knowledge you need to tackle complex problems using machine learning.

The trainer in this course is - Dr. Mostafa Sheikh who is a Senior Data Scientist with over 20 years of hands on experience on AI, ML and NLP. In this very hands on course Dr. Mostafa  begins with a quick review of foundational NLP concepts, including how to clean text data and build a model on top of vectorized text. He then jumps into more complex topics such as Spark NLP, Webscraping, Parsing, Tensor flow. To wrap up the course, he lends these concepts a real-world context by showing how to develop and deploy machine learning problems.

The course is brought to you by HotCubator academy, a think tank of data and research based in Australia.

After completing this FREE course, you can retrieve a 80% discount coupon code to our master class on NLP.

Introduction to Terrorism Threat Analysis

Get a quick understanding of terrorism threat analysis. Learn how to locate data and tools available for analysis.

Created by J. Michael Stattelman - Chief Technology Officer


Students: 3357, Price: Free

Students: 3357, Price:  Free

This course is aimed at those tasked with the Safety and Security of crowds or event gatherings. The intent of this content is to get you aware of and familiar with the Terror Threat Analytics application by High Order Analytics for use in event-based terrorism threat analysis.

The aggregation and analysis of data for terror attacks, groups and individuals is a daunting task. It continues to grow each day with ever expanding International and Domestic terror threats coming online and congealing around a hate/isolationist/violent approach to global social issues.

As it stands currently, there is no other application that offers this functionality available for and open to the public. In our efforts to assist in the continued safety of the public we felt obligated to put this course together for the purposes of educating those tasked with security and risk.

This is a quick course designed to get the average user familiar with and proficient at the use of the T.T.A. application for use in your jurisdiction, municipality or region.

2021 Natural Language Processing in Python for Beginners

Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV Parsing

Created by Laxmi Kant - Principal Data Scientist at mBreath and KGPTalkie


Students: 2712, Price: $19.99

Students: 2712, Price:  Paid

Welcome to KGP Talkie's Natural Language Processing (NLP) course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python.

We will learn Spacy in detail and we will also explore the uses of NLP in real life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python. At the end part of this course, you will learn how to generate poetry by using LSTM. Multi-Label and Multi-class classification is explained. At least 12 NLP Projects are covered in this course. You will learn various ways of solving edge-cutting NLP problems.

You should have an introductory knowledge of Python and Machine Learning before enrolling in this course otherwise please do not enroll in this course.

In this course, we will start from level 0 to the advanced level.

We will start with basics like what is machine learning and how it works. Thereafter I will take you to Python, Numpy, and Pandas crash course. If you have prior experience you can skip these sections. The real game of NLP will start with Spacy Introduction where I will take you through various steps of NLP preprocessing. We will be using Spacy and NLTK mostly for the text data preprocessing.

In the next section, we will learn about working with Files for storing and loading the text data. This section is the foundation of another section on Complete Text Preprocessing. I will show you many ways of text preprocessing using Spacy and Regular Expressions. Finally, I will show you how you can create your own python package on preprocessing. It will help us to improve our code writing skills. We will be able to reuse our code systemwide without writing codes for preprocessing every time. This section is the most important section.

Then, we will start the Machine learning theory section and a walkthrough of the Scikit-Learn Python package where we will learn how to write clean ML code. Thereafter, we will develop our first text classifier for SPAM and HAM message classification. I will be also showing you various types of word embeddings used in NLP like Bag of Words, Term Frequency, IDF, and TF-IDF. I will show you how you can estimate these features from scratch as well as with the help of the Scikit-Learn package.

Thereafter we will learn about the machine learning model deployment. We will also learn various other important tools like word2vec, GloVe, Deep Learning, CNN, LSTM, RNN, etc.

At the end of this lesson, you will learn everything which you need to solve your own NLP problem.

Parsing in Natural Language Processing

Natural Language Processing

Created by Shilpa Mene - Assistant Professor


Students: 2217, Price: Free

Students: 2217, Price:  Free

This course is tailored for the beginner and intermediate learners. NLP stands for Natural Language Processing, which is a part of Computer Science, Human language, and Artificial Intelligence. It is the technology that is used by machines to understand, analyze, manipulate, and interpret human's languages. Natural Language Understanding (NLU) helps the machine to understand and analyze human language by extracting the metadata from content such as concepts, entities, keywords, emotion, relations, and semantic roles. Even though NLP is not a new domain, it catches attention of significant researches all over the world. Natural languages are usually used by human for verbal or written communication such as English, Marathi, Spanish etc. . NLP has its roots in 8th decade of previous century. It is branch of Artificial Intelligence which deals with processing of natural languages, specifically for some pragmatics. Its significance lies in applications developed by developers to make lives of common man easy and comfortable. Now a days this is one of the domain of research. There are typical phases of NLP such as morphological analysis, syntax analysis, semantic analysis, discourse analysis, pragmatic analysis and phonological analysis if required in an application. This course is tailored for the beginner and intermediate learners. Welcome to the course!!! I will count on your active participation and feedback. Journey of course includes Introduction to NLP, Applications of NLP , Phases of NL, Syntax Analysis, Simple top down parser, bottom up chart parser, top down chart parser.

Natural Language Processing (NLP) in Python with 8 Projects

Work on 8 Projects, Learn Natural Language Processing Python, Machine Learning, Deep Learning, SpaCy, NLTK, Sklearn, CNN

Created by Ankit Mistry - Software Developer | I want to Improve your life & Income.


Students: 1379, Price: $109.99

Students: 1379, Price:  Paid

Recent reviews:

"Thorough explanation, going great so far. A very simplistic and straightforward introduction to Natural Language Processing. I will recommend this class to any one looking towards Data Science"

"This course so far is breaking down the content into smart bite-size pieces and the professor explains everything patiently and gives just enough background so that I do not feel lost."

"This course is really good for me. it is easy to understand and it covers a wide range of NLP topics from the basics, machine learning to Deep Learning.

The codes used is practical and useful.

I definitely satisfy with the content and surely recommend to everyone who is interested in Natural Language Processing"


Update 1.0 :

Fasttext Library for Text classification section added.


Hi Data Lovers,

Do you have idea about Which Artificial Intelligence field is going to get big in upcoming year?

According to statista dot com which field of AI is predicted to reach $43 billion by 2025?

If  answer is 'Natural Language Processing', You are at right place.


Do you want to know

  • How Google News classify millions of news article into hundreds of different category.

  • How Android speech recognition recognize your voice with such high accuracy.

  • How Google Translate actually translate hundreds of pairs of different languages into one another.

If answer is "Yes", You are on right track.

and to help yourself, me and my friend Vijay have created comprehensive course  For Students and Professionals to learn Natural Language Processing from very Beginning


NLP - "Natural Language Processing" has found space in every aspect of our daily life.

Cell phone internet are the integral part of our life. Any most application you will find the use of NLP methods, from search engine of Google to recommendation system of Amazon & Netflix.

  • Chat-bot

  • Google Now, Apple Siri, Amazon Alexa

  • Machine Translation

  • Sentiment analysis

  • Speech Recognition and many more.

So, welcome to my course on NLP.

Natural Language Processing (NLP) in Python with 8 Projects


This course has 10+ Hours of HD Quality video, and following content.

Course Outline :

1 : Welcome In this section we will get complete idea about what we are going to learn in the whole course and understanding related to natural language processing.

2 :  Installation & Setup In this section we will get our online environment Google Colab setup.

3 : Basics of Natural Language Processing In this section we will dive into all basic NLP task like Tokenization, Lemmatization, stop word removal, name entity   recognition, part of speech tagging, and see how to apply with different functions available in a  Spacy and NLTK library.

4, 5, 6 : Spam Message Classification,  Restaurant Review Prediction (Good or bad),  IMDB, Amazon and Yelp review Classification

In the next 3 section we will get dive into a real world data set for text classification, spam detection, restaurant review classification, Amazon IMDb reviews. We will see how to do Pre-Processing and make your data suitable for machine learning algorithm and apply different Machine Learning estimator (Logistic Regression, SVM, Decision Tree) for classifying text.

7, 8 : Automated Text Summarization,  Twitter sentiment Analysis In this 2 section we will work upon real world application of NLP.

Automatic text summarisation, Which compress your text to find the summary of big articles

Another one we will work is finding the sentiment from the recently posted tweet about some specific keyword with the help of Twitter API - tweepy library

9 : Deep Learning Basics In This Section we will get a basic idea about Deep learning concept, like artificial neural network activation function and how ANN works.

10 : Word Embedding In This Section, we will see How to implement word2vec on our custom datasets, as well as using Pretrained Google Model.

11, 12 : Text Classification with CNN & RNN In this section we will see how to apply advanced deep learning model like convolution neural networks and recurrent neural networks for text classification.

13 : Automatic Text Generation using TensorFlow, Keras and LSTM In this section we will apply neural network based LSTM model to automatically generate text.

14, 15, 16, 17 : Numpy, Pandas, Matplotlib + File Processing In this section, for all of you who want refresh concept related to data analysis with Numpy and Pandas library, Data Visualization with Matplotlib library, and Text File processing and PDF File processing.


So, This is the one of the most comprehensive course on natural language processing,

And I am expecting you to know basic knowledge of python and your curiosity to learn Different techniques in NLP world.


  • Lifetime access to Natural Language Processing (NLP) with Python Course

  • Udemy Certificate of Completion available for download

  • Friendly support in the Q&A section

So What Are You Waiting For ? Enroll today! and Empower Your Career !

I can't wait for you to get started on mastering NLP with Python.

Start analyzing your text data & I will see you inside a class.


Ankit & Vijay

Natural Language Processing for Text Summarization

Understand the basic theory and implement three algorithms step by step in Python! Implementations from scratch!

Created by Jones Granatyr - Professor


Students: 1239, Price: $19.99

Students: 1239, Price:  Paid

The area of ​​Natural Language Processing - PLN (Natural Language Processing - NLP) is a subarea of ​​Artificial Intelligence that aims to make computers capable of understanding human language, both written and spoken. Some examples of practical applications are: translators between languages, translation from text to speech or speech to text, chatbots, automatic question and answer systems (Q&A), automatic generation of descriptions for images, generation of subtitles in videos, classification of sentiments in sentences, among many others! Another important application is the automatic document summarization, which consists of generating text summaries. Suppose you need to read an article with 50 pages, however, you do not have enough time to read the full text. In that case, you can use a summary algorithm to generate a summary of this article. The size of this summary can be adjusted: you can transform 50 pages into only 20 pages that contain only the most important parts of the text!

Based on this, this course presents the theory and mainly the practical implementation of three text summarization algorithms: (i) frequency-based, (ii) distance-based (cosine similarity with Pagerank) and (iii) the famous and classic Luhn algorithm, which was one of the first efforts in this area. During the lectures, we will implement each of these algorithms step by step using modern technologies, such as the Python programming language, the NLTK (Natural Language Toolkit) and spaCy libraries and Google Colab, which will ensure that you will have no problems with installations or configurations of software on your local machine.

In addition to implementing the algorithms, you will also learn how to extract news from blogs and the feeds, as well as generate interesting views of the summaries using HTML! After implementing the algorithms from scratch, you have an additional module in which you can use specific libraries to summarize documents, such as: sumy, pysummarization and BERT summarizer. At the end of the course, you will know everything you need to create your own summary algorithms! If you have never heard about text summarization, this course is for you! On the other hand, if you are already experienced, you can use this course to review the concepts.

Introduction to NLP

Fundamentals of Natural Language Processing

Created by Sunil Kumar Mishra - AI Enthusiast | Startup Mentor I Author


Students: 766, Price: Free

Students: 766, Price:  Free

Natural Language processing is today a fast-emerging technology area. It has also been one of the most difficult topics to handle for the computers. Thanks to the advancement in artificial intelligence, we can process natural language more easily today. Many business applications today leverage the power of NLP. With the perfection on speech to text and text to speech conversions, NLP tools are used today as personal assistants and robo advisors. The chat bots are primary interface for many business applications. The NLP engine can process vast amounts of texts and classify them as well as translate them to another language. NLP programs are working in conjunction with the image recognition techniques to automatically generate captions from the images and the vice-versa.

This course is tries to demystify some aspects of NLP and address some of the challenges and approaches to handle the same.

Expectations from the course

1. Why NLP is important

2. Complexity in handling NLP

3. Business use cases of NLP

4. Different types of NLP problems

5. Approach for solving NLP problems

6. Applying machine learning concepts

7. Word embedding

This course uses python programming for basic hands-on. Some of the practice sessions in this course include: -

1. Standard text handling using nltk

2. Pre-processing text (normalization)

3. Sentiment analysis of the review comments

4. Spam detection using machine learning algorithms

Natural Language Processing: Machine Learning NLP In Python

A Complete Beginner NLP Syllabus. Practicals: Linguistics, Sentiment, Scrape Tweets, RNNs, Chatbot, Hugging Face & more!

Created by Nidia Sahjara - NLP Engineer & Researcher


Students: 596, Price: $89.99

Students: 596, Price:  Paid

This course takes you from a beginner level to being able to understand NLP concepts, linguistic theory, and then practice these basic theories using Python - with very simple examples as you code along with me.

Get experience doing a full real-world workflow from Collecting your own Data to NLP Sentiment Analysis using Big Datasets of over 50,000 Tweets.

  • Data collection: Scrape Twitter using: OSINT - Open Source Intelligence Tools: Gather text data using real-world techniques. In the real world, in many instances you would have to create your own data set; i.e source your data instead of downloading a clean, ready-made file online

  • Use Python to search relevant tweets for your study and NLP to analyze sentiment.

Language Syntax: Most NLP courses ignore the core domain of Linguistics. This course explains the fundamentals of Language Syntax & Parse trees - the foundation of how a machine can interpret the structure of s sentence.

New to Python: If you are new to Python or any computer programming, the course instructions make it easy for you to code together with me. I explain code line by line.

No Installs, we go straight to coding - Code using Google Colab - to be up-to-date with what's being used in the Data Science world 2021!

The gentle pace takes you gradually from these basics of NLP foundation to being able to understand Mathematical & Linguistic (English-Language-based, Non-Mathematical) theories of Deep Learning.

Natural Language Processing Foundation

  • Linguistics & Semantics - study the background theory on natural language to better understand the Computer Science applications

  • Pre-processing Data (cleaning)

  • Regex, Tokenization, Stemming, Lemmatization

  • Name Entity Recognition (NER)

  • Part-of-Speech Tagging


SQuAD - Stanford Question Answer Dataset. Train your Q&A Model on this awesome SQuAD dataset.


  • NLTK

  • Sci-kit Learn

  • Hugging Face

  • Tensorflow

  • Pytorch

  • SpaCy

  • Twint

The topics outlined below are taught using practical Python projects

  • Parse Tree

  • Markov Chain

  • Text Classification & Sentiment Analysis

  • Company Name Generator

  • Unsupervised Sentiment Analysis

  • Topic Modelling

  • Word Embedding with Deep Learning Models

  • Closed Domain Question Answering (Like asking questions on many different topics, from Beyonce to Iranian Cuisine)

  • LSTM using TensorFlow, Keras Sequence Model

  • Speech Recognition

  • Convert Speech to Text

Neural Networks

  • This is taught from first principles - comparing Biological Neurons in the Human Brain to Artificial Neurons.

  • Practical project: Sentiment Analysis of Steam Reviews

Word Embedding: This topic is covered in detail, similar to an undergraduate course structure that includes the theory & practical examples of:

  • TF-IDF

  • Word2Vec

  • One Hot Encoding

  • gloVe

Deep Learning

  • Recurrent Neural Networks

  • LSTMs

    • Get introduced to Long short-term memory and the recurrent neural network architecture used in the field of deep learning.

    • Build models using LSTMs

Practical Text and Sentimental Analysis

Natural Language Understanding and Natural Language Processing

Created by Kelvin Fosu - Founder of AnyBodyCanDevelop-ABCD


Students: 456, Price: Free

Students: 456, Price:  Free

Learn to use some python libraries for text analysis(spelling correction, Parts of Speech (POS) Tagging pluralization, tokenization) and sentimental analysis like Polarity( positive or negative sentiments) and subjectivity( the beliefs and feelings of a person expressed in a  text or tweet post.

In this course, we would learn lots of different methods used in text analysis using TextBlob's methods and the Tweepy library to analyze sentiments in tweets.

The course assumes you already have python3, Anaconda already installed and you're comfortable using Jupyter notebook. Also, some background understanding of python basics is very helpful.

You'll have free -downloadable access to the course activities/ exercise from the first section of the course module. The jupyter notebook exercise file has been well commented on so you understand what we are trying to achieve with each line of code.

This should help you practice on your own while watching the video. Also, more sessions will be added as they are being edited.

*Python 3* is the version of python used in the lectures and Jupyter notebook is the IDE used in programming for the course.

It should be noted that python and anaconda installations and downloads and setting up anaconda and python is not taught in this course.

TextBlob package and Tweepy Package