Natural Language Processing Specialization– Coursera ranks first in the list of best online natural language processing courses. Natural Language Processing (NLP) is a linguistic, computer science, and artificial intelligence subfield that employs algorithms to interpret and manipulate human language. This technology is one of the most widely used areas of machine learning and is essential for effectively analyzing massive amounts of unstructured, text-heavy data. As AI grows in popularity, so will the demand for professionals who can create models that analyze speech and language, uncover contextual patterns, and generate insights from text and audio.

By the end of this Specialization, you will be able to create NLP applications for question-answering and sentiment analysis, tools for translating languages and summarizing text, and even chatbots. These and other NLP applications will be at the forefront of the upcoming shift to an AI-powered future.

This Specialization was created and taught by two NLP, machine learning, and deep learning experts. Younes Bensouda Mourri is an AI Instructor at Stanford University who also contributed to the development of the Deep Learning Specialization. ukasz kaiser is a Google Brain Staff Research Scientist and co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Courses Details:

• Natural Language Processing with Classification and Vector Spaces
• Natural Language Processing with Probabilistic Models
• Natural Language Processing with Sequence Models
• Natural Language Processing with Consideration Models

Extra Benefits:

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with this, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback,
• Graded Quizzes with Feedback, Graded Programming Assignments.

Who Should Enroll?

• Those who have a working knowledge of machine learning, intermediate Python including experience with a deep learning framework such as TensorFlow and Keras.
• Along with this, you should have knowledge of calculus, linear algebra, and statistics.

Interested to Enroll?

• If yes, then get more details here- Natural Language Processing Specialization

Natural Language Processing in TensorFlow– Coursera ranks 2nd in the list of best online natural language processing courses. If you are a software developer looking to create scalable AI-powered algorithms, you must first understand how to use the tools available to you. This Specialization will teach you the best practices for using TensorFlow, a popular open-source machine learning framework.

In Course 3 of the deeplearning.ai TensorFlow Specialization, you will use TensorFlow to build natural language processing systems. You will learn how to process text, including tokenizing and vectorizing sentences so that they can be fed into a neural network. You'll also discover how to use RNNs, GRUs, and LSTMs in TensorFlow. Finally, you'll have the opportunity to train an LSTM on existing text to generate original poetry!

This is yet another popular Natural language processing course. TensorFlow in Practice Specialization includes this course. In this course, you will use TensorFlow to create natural language processing systems. TensorFlow is a popular open-source machine learning framework. You will also learn how to process text, including tokenizing and representing sentences as vectors for input into a neural network. You will also learn RNN, GRU, LSTM, and other techniques. In this course, you will work on training LSTMs on existing text in order to generate original poetry, among other things. This course appeals to me because of its practical presentation. You will not be bored while taking this course.

Topics Covered

• Sentiment in text
• Word Embeddings
• Sequence models
• Sequence models and literature

Extra Benefits

• You will get a Shareable Certificate.
• You will work on hands-on assignments and projects that will enhance your portfolio.
• Along with that, you will get to solve Practice Quizzes and Graded Assignments.

Who Should Enroll?

• Those who have intermediate-level knowledge in Python, high school level mathematics knowledge, and those who are familiar with TensorFlow.

Provider: deeplearning.ai
Rating: 4.6/5.0
Time to Complete- 14 Hours
Enroll here: coursera.org/learn/natural-language-processing-tensorflow

NLP -Natural Language Processing with Python- Udemy ranks 3rd in the list of best online natural language processing courses. Welcome to the most comprehensive Natural Language Processing course available on the internet! This course is intended to be your complete online resource for learning how to use Natural Language Processing with Python. The course will teach you everything you need to know to become a world-class NLP practitioner using Python.

This course is a Best Seller on Udemy. That is correct, because this course is a comprehensive online resource for learning how to use Natural Language Processing with Python. This course will teach you everything you need to know to become a world-class NLP practitioner using Python. This course will teach you how to use Python to work with Text Files. In this course, you will utilize Regular Expressions for pattern searching in text.

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 their 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 their 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.

Topics Covered-

• Python Text Basics.
• Natural Language Processing Basics.
• Part of Speech Tagging and Named Entity Recognition.
• Text Classification.
• Semantics and Sentiment Analysis.
• Topic Modeling.
• Deep Learning for NLP.

Extra Benefits-

• You will get a Certificate of Completion.
• You will also get 2 articles and 2 downloadable resources.
• Along with that, you will get full-time access to course material.

Who Should Enroll?

• There is not too many requirements for enrolling in this course. Only those who know Python and want to learn NLP can enroll.

Interested to Enroll?

• If yes, then get more details here- NLP -Natural Language Processing with Python

Rating: 4.5/5.0
Provider: Jose Portilla
Time to Complete- 11.5 hours

Enroll here: udemy.com/course/nlp-natural-language-processing-with-python

Data Science: Natural Language Processing (NLP) in Python –Udemy ranks 4th in the list of best online natural language processing courses. This course will teach you how to build MULTIPLE practical systems using natural language processing (NLP), a branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so there is no hard math - just straight up Python coding. This course's materials are completely free.

Following a brief discussion of what NLP is and what it can do, you will start building very useful things. They'll start by developing a cipher decryption algorithm. These can be used in warfare and espionage. In this section, you will learn how to create and apply several useful NLP tools, including character-level language models (using the Markov principle) and genetic algorithms.

The second project, where they 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 they'll build a model for sentiment analysis in Python. This is something that allows you to assign a score to a block of text that tells you how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market.

Topics Covered-

• Natural Language Processing- What is it used for?
• Decrypting Ciphers.
• Build your own Spam Detector.
• Build your own Sentiment analyzer.
• NLTK Exploration.
• Latent Semantic Analysis.
• Write your own article spinner.
• How to learn more about NLP.
• Machine Learning Basic Review.

Extra Benefits-

• You will get a Certificate of Completion.
• Along with that, you will get full-time access to course material.

Who Should Enroll?

• This course is suitable for-
• Students who are comfortable writing Python code and who want to learn more about machine learning but don’t want to do a lot of math.
• Professionals who are interested in applying machine learning and NLP to practical problems like spam detection,
• Internet marketing, and sentiment analysis

Interested to Enroll?

• If yes, then get more details here- Data Science: Natural Language Processing (NLP) in Python

Rating: 4.5/5.0
Provider: Lazy Programmer
Time to Complete- 10 hours

Enroll here: udemy.com/course/data-science-natural-language-processing-in-python

Previously, you learned about some of the fundamentals, such as how many NLP problems are simply disguised machine learning and data science problems, and simple, practical methods such as bag-of-words and term-document matrices. These enabled them to do some pretty cool things, such as detect spam emails, write poetry, spin articles, and group similar words together.

This course will teach you NLP (natural language processing) using deep learning. This course will teach you about word2vec and how to use it. You will also learn how to use gradient descent and alternating least squares to implement GloVe. For named entity recognition, this course employs recurrent neural networks. You will also learn how to build recursive neural tensor networks for sentiment analysis. Let's take a look at the topics covered in this course.

Topics Covered

• Outline, Review, and Logistical Things.
• Beginner’s corner- Working with word vectors.
• Review of Language Modeling and Neural Networks.
• Word Embedding and Word2Vec.
• Word Embedding using GloVe.
• Unifying Word2Vec and GloVe.
• Using a Neural Network to solve NLP problems.
• Recursive Neural Network
• Theano and Tensorflow Basics Review.

Extra Benefits

• You will get a Certificate of Completion.
• Along with that, you will get full-time access to course material.

Who Should Enroll?

• Those who have knowledge of calculus, matrix addition, multiplication, and, probability and comfortable with Python coding, and Numpy coding.
• If someone has knowledge of neural networks and backpropagation.
• Those who can write feedforward neural network in Theano or TensorFlow and recurrent neural network / LSTM /
• GRU in Theano or TensorFlow.

Interested to Enroll?

• If yes, then get more details here- Natural Language Processing with Deep Learning in Python

Rating: 4.5/5.0
Provider: Lazy Programmer
Time to Complete- 12 hours
Enroll here: udemy.com/course/natural-language-processing-with-deep-learning-in-python

This is one of the best NLP Online Courses available. This course covers a wide range of Natural Language Processing topics. Sentiment analysis, summarization, dialogue state tracking, and many other features are available. By the end of this Specialization, you will have created NLP applications for question-answering and sentiment analysis, tools for translating languages and summarizing text, and even a chatbot!

This Specialization was created and taught by two NLP, machine learning, and deep learning experts. Younes Bensouda Mourri is an AI Instructor at Stanford University who also contributed to the development of the Deep Learning Specialization. ukasz kaiser is a Google Brain Staff Research Scientist and co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Topics Covered

• Intro and text classification
• Language modeling and sequence tagging
• Vector Space Models of Semantics
• Sequence to sequence tasks
• Dialog systems

Extra Benefits

• You will get a Shareable Certificate.
• Along with that, you will build a conversational chat-bot.

Who Should Enroll?

• This course requires basic knowledge of linear algebra and probability theory, machine learning setup, and deep neural networks.

Interested to Enroll?

• If yes, then get more details here- Natural Language Processing

Provider: National Research University Higher School of Economics
Rating: 4.5/5.0
Time to Complete: 32 Hours

Enroll here: coursera.org/learn/classification-vector-spaces-in-nlp

You will learn cutting-edge natural language processing techniques to process speech and analyze text in this Nano-degree program. You will also build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, in this program to teach the computer to perform tasks such as speech recognition, machine translation, and others. This Nanodegree program will teach you how to tag parts of speech in sentences and compare their performance using various techniques such as table lookups, n-grams, and hidden Markov models.

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, and more! This Nano Degree Program consists of three courses. Let's look at the specifics of the courses.

Courses List

• Introduction to Natural Language Processing
• Computing with Natural Language
• Communicating with Natural Language

Extra Benefits

• You will get a chance to work on Real-world projects with Industry Experts.
• You will get Technical mentor support, Github review, etc.

Who Should Enroll?

• Those who have Intermediate Knowledge of Python, Statistics, Machine Learning, & Deep Learning.

Interested to Enroll?

• If yes, then get more details here- Become a Natural Language Processing Expert

Rating: 4.5/5.0
Time to complete: 3 months (If you spend 10-15 hours per week)

Enroll here: udacity.com/course/natural-language-processing-nanodegree--nd892

Edureka offers Live Classes at your convenience. This course will teach you the fundamentals of text processing, as well as how to classify texts using Machine Learning algorithms. Using Python's most well-known NLTK package, you will learn various concepts such as Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing, and so on.

After you've mastered these concepts, you'll be able to create your own text classifier using the Nave Bayes algorithm. This course is jam-packed with real-world examples where you can put what you've learned to use. Semantic Analysis, Text Processing, Sentiment Analytics, and Machine Learning are a few examples. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language.

Topics Covered-

• Introduction to Text Mining and NLP
• Hands-On/Demo:Install NLTK Packages using NLTK Downloader
  • Accessing your operating system using the OS Module in Python
  • Reading & Writing .txt Files from/to your Local
  • Reading & Writing .docx Files from/to your Local
  • Working with the NLTK Corpora
  • Extracting, Cleaning, and Pre-processing Text
• Hands-On/Demo:Tokenization: Regex, Word, Blank line, Sentence
  • Tokenizers
  • Bigrams, Trigrams & Ngrams
  • Stopword Removal
  • POS Tagging
  • Named Entity Recognition (NER)
  • Analyzing Sentence Structure
• Hands-On/Demo:Parsing Syntax Trees
  • Chunking
  • Chinking
  • Automate Text Paraphrasing using CFG’s
  • Text Classification – I
• Hands-On/Demo:Demonstrate Bag of Words Approach
  • Working with CountVectorizer()
  • Using TF & IDF
  • Text Classification – II
• Hands-On/Demo:Converting text to features and labels
  • Demonstrate text classification using Multinomial NB Classifier
  • Leveraging Confusion Matrix
  • In-Class ProjectHands-On:Sentiment Analysis

Extra Benefits

• You will get Edureka’s Natural Language Processing Engineer Certificate.
• You get lifetime access to the course materials, presentations, quizzes, and installation guide.
• You will also get 60 days of Cloud Lab access
• Along with that, you will get 24 x 7 Expert Support.
• You will get a chance to work on practical assignments after each class.
• You will also get a chance to work on Live projects.

Who Should Enroll?

• Those who have knowledge of Python programming and a good understanding of Machine Learning concepts.

Interested to Enroll?

• If yes, then get more details here-Natural Language Processing with Python Certification Course

Rating: 4.3/5.0
Mode of Learning- Live Classes

Enroll here: edureka.co/python-natural-language-processing-course

This course begins with an understanding of how Python handles text, the structure of text as it appears to machines and humans, and an overview of the nltk framework for manipulating text. You will learn how to use basic natural language processing methods in that course. You will also learn how to write code that categorizes documents. This course is a component of the Applied Data Science with Python Specialization program.

This course will teach the fundamentals of text mining and text manipulation. The course begins with an understanding of how Python handles text, the structure of text to both machines and humans, and an overview of the nltk text manipulation framework. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).

Topics Covered-

• Working with Text in Python
• Basic Natural Language Processing
• Classification of Text
• Topic Modeling

Extra Benefits-

• You will get a Shareable Certificate.
• Along with that, you will get to solve Practice Quizzes and Graded Assignments.

Who Should Enroll?

• Those who have intermediate-level knowledge in Python, Machine Learning, Plotting, Charting & Data
• Representation in Python.
• So, if you have the following knowledge, then only you should enroll yourself in this course. But if you don’t have, then you can enroll yourself in this specialization program- Applied Data Science with Python Specialization. This specialization program will also cover this course- Applied Text Mining in Python.

Interested to Enroll?

• If yes, then get more details here- Applied Text Mining in Python

Provider: University of Michigan
Rating: 4.2/5.0
Time to Complete- 29 hours

Enroll here: coursera.org/learn/python-text-mining

DataCamp provides this Natural Language Processing Course. This course will teach you the fundamentals of natural language processing (NLP), such as how to identify and separate words, extract topics in a text, and create your own fake news classifier.

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.

Topics Covered

• Regular expressions & word tokenization
• Simple topic identification
• Named-entity recognition
• Building a “fake news” classifier

Who Should Enroll?

• Those who have working knowledge in Python.

Interested to Enroll?

• If yes, then get more details here- Introduction to Natural Language Processing in Python

Rating: 4.1/5.0
Time to Complete: 4 hours
Enroll here: datacamp.com/courses/introduction-to-natural-language-processing-in-python