Introduction
Natural language processing (NLP) is a sub-branch of artificial intelligence. The field of NLP is concerned with enabling computers to have the ability to understand text and speech similar to humans.
With NLP, the intelligent systems are integrated with computational linguistic functionalities using machine learning and deep learning techniques. In the new era of machine-based translations, NLP systems can perform language translation, GPS systems, intelligent driving assistance, digital assistants, speech to text, chatbots, automated grammar correction tools, and more.
Researchers worldwide are continuously developing advanced NLP mechanisms for speed recognition, speech tagging, sentiment analysis that are gaining acceptance in various industries. As the field of AI continues to evolve, its subfields, such as NLP, continue to flourish with new advances each year.
Besides, the use of NLP is rapidly increasing across industries such as the automotive industry, electronics, e-commerce, and various other intelligent solutions. Therefore, aspirants should equip themselves with the theoretical and practical experiences of NLP to gain a strong foothold in the industry.
Related reading: Top 15 Online Courses to Learn Python
1. Natural Language Processing Specialization – Coursera
Offered by one of the pioneers in the industry, Andrew Ng’s DeepLearning AI offers this program on Coursera. Perhaps, this course is one of the best natural language processing courses on the platform.
The training focuses on enabling learners to build NLP applications that can perform multiple functionalities such as question answering, sentiment analysis, and creating different tools to translate various languages.
In addition, the learners will understand the process of summarizing texts and learn to build chatbots for assisting users. By the end of the course, the learners will have sufficient exposure to different concepts and gain practical experience building NLP applications.
The learners will become familiar with concepts like logistic regression, Naïve Bayes, and word vectors to successfully implement sentiment analysis and translate words, understand neural networks, LSTMs, and work on building applications capable of text generation and named entity recognition.
Finally, the learners will have a comprehensive understanding of dynamic programming, hidden Markov models, and learning to implement autocorrect and autocomplete features, use encoder-decoder, summarize texts, and perform machine translations for complete sentences.
The course curriculum includes:
- Natural Language Processing with Classification and Vector Spaces
- Natural Language Processing with Probabilistic Models
- Natural Language Processing with Sequence Models
- Natural Language Processing with Attention Models
COURSE DETAILS:
Instructor: Younes Bensouda Mourri, Eddy Shyu
Level: Intermediate
Duration:4 months
User Review: 4.6/5
No. of Reviews: 4055
Price: Free Enrollment (Charges applicable for certification)
2. Professional Certificate in Text Analytics with Python – edX
The University of Canterbury offers this specialization on edX. The course participants can learn natural language processing and the core techniques required for text analytics.
On the practical side, the learners will perform analysis using Python programming and learn to create pipelines for text classification purposes using machine learning techniques.
These hands-on sessions offered by the program are critical in learning about automated workflows, from data collection to visualization. For the scientific aspect of the course, the participants will learn about concepts like understanding languages computationally and the differences in how a human and AI view text documents.
Besides, the learners will understand the limitations of specific computational approaches for language translation and the ethical requirements. Various real-world case studies are provided for the participants to engage practically and perform data science-related tasks to gain insights from unstructured data by performing text analytics.
By the end of the course, the learners will gain proficiency in coding to build applications using unstructured data such as news articles and tweets and applying machine learning classifiers for document categorization.
In addition, the learners will be able to perform NLP tasks for identifying document similarity, visualizing and interpreting text analytics using statistical significance tests, and assessing different scientific and ethical foundations of applications used for text analytics.
The course curriculum includes:
Introduction to Natural Language Processing
Module 1. Why Use Text Analytics? Learning how artificial intelligence can help work with language data
Module 2. Working with Text Data: Learning what language looks like from human and machine perspectives
Module 3. Text Classification: Learning how to use machine learning to categorize documents based on content, authorship, and sentiment.
Visualizing Natural Language Processing
Module 1. Text Similarity:
Learning to use machine learning to find out which words and documents have similar meanings.
Module 2. Visualizing Text Analytics:
Learning to explain a model using visualization and significance testing.
Module 3. Applying Text Analytics to New Fields:
Learning to apply computational linguistics to new problems and new data sets.
COURSE DETAILS:
Instructor: Jonathan Dunn, Tom Coupe, Jeanette King, and Girish Prayag
Level: Intermediate
Duration: 3 months
User Review: NA
No. of Reviews: NA
Price: $498
3. Natural Language Processing with Deep Learning in Python – Udemy
This training program is offered on Udemy. The course is one of the top trending NLP courses on the platform with increasing enrollments. The course participants require a basic understanding of programming and machine learning as various advanced concepts are introduced in the latter half of the program.
The participants will become familiar with word2vec concepts from theory to its implementation. Further, the concepts of using Gensim library to obtain pre-trained word vectors are covered in depth. Besides, the concepts of the GloVe method and matrix factorization techniques are demonstrated with various practical examples to help learners understand the algorithm and its implementation for building recommender systems.
Different other concepts are also covered in detail, such as speech tagging, named entity recognition, and using recurrent neural networks. Finally, the learners will also learn to implement RNN for performing sentiment analysis for a given problem.
The course curriculum includes:
- Introduction, Outline, Review, and Logistical Things
- Working with Word Vectors
- Review of Language Modeling and Neural Networks
- Word Embedding and Word2Vec
- Word Embedding using GloVe
- Unifying Word2Vec and GloVe
- Using Neural Networks to Solve NLP Problems
- Recursive Neural Networks (Tree Neural Networks)
- Theano and TensorFlow Basics Review
- Setting Up the Environment (FAQ)
- Bonus: Python Coding for Beginners and Effective Learning Strategies for Machine Learning
COURSE DETAILS:
Instructor: Lazy Programmer Team
Level: Intermediate
Duration: 12 hours
User Review: 4.6/5
No. of Reviews: 6770
Price: $22.3
4. NLP- Natural Language Processing with Python – Udemy
This natural language processing certification course is excellent for learners willing to learn from the basics to the advanced concepts. The training program is offered on Udemy.
The participants will begin with the basics to learn how to work with text and pdf files using Python and understand the basic expressions to identify patterns inside the text files. Next, the essentials of NLP and the NLP Toolkit library for Python are covered in depth.
Further, other fundamental topics are covered, such as stemming, lemmatization, stop words phrase matching, and tokenization. As for the speech tagging concepts, the participants will learn by using Python scripts to automate assigning words in text for appropriate parts of speech like nouns, verbs, and objectives.
Besides, the learners will delve into the concepts of named entity recognition and visualization libraries to understand relationships in real-time.
Additionally, other critical concepts are covered, such as machine learning with Scikit-learn for text classification purposes and learning, building a machine learning system that can determine the positive and negative movie reviews.
There are additional projects on complex unsupervised learning methods for NLP tasks and understanding the word2Vec algorithm to provide comprehensive practical knowledge.
The course curriculum includes:
- Introduction
- 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
- Bonus Section
COURSE DETAILS:
Instructor: Jose Portilla
Level: Beginner/ Intermediate
Duration: 11 hours 24 minutes
User Review: 4.6/5
No. of Reviews: 10462
Price: $46
5. Become a Natural Language Processing Expert Nanodegree Program – Udacity
Udacity offers this excellent NLP specialization course. The participants can master their skills to understand how to process and manipulate human language and build models on real-world data. Besides, the learners will gain hands-on experience in sentiment analysis, machine translation, and other relevant NLP tasks.
Additionally, the learners will learn to process speech and analyze text and build probabilistic and deep learning models. Further, the concept of Markov models and RNN are covered in-depth with demonstrations to learn to perform speech recognition tasks effectively. However, one should be aware that prerequisites include intermediate or advanced Python experience and an understanding of object-oriented programming.
There is also a requirement for intermediate statistical background and machine learning techniques. In addition, one should have a working knowledge of the deep learning frameworks such as TensorFlow, Keras, and PyTorch.
Introduction to Natural Language Processing
- Learning the primary techniques used in NLP
- Getting familiarized with various terminologies and topics
- Building the first application using IBM Watson
- Understanding how text is processed to be used in models
- Learning techniques like tokenization, stemming, and lemmatization
- Understanding the process of speech tagging and named entity recognition
- Hidden Markov Models
- Training HMM and Viterbi and Baum-Welch algorithm
- Using HMM for speech tagging model building
Computing with Natural Language
- Extracting features from text
- Embedding algorithms like Word2Vec and Glove
- Understanding the primary uses of deep learning models in NLP
- Machine translation, topic models, and sentiment analysis
- Understanding the concepts of attention, advanced deep learning methods that are powering Google Translate
- Understanding additive and multiplicative attention for machine translation, text summarization, and image captioning
- Using transformer and extending the use of attention to eliminate the need for RNN
- Information extraction and information retrieval systems
- Learning about question answering and its applications
Communicating with Natural Language
- Basics of how computer science is used for understanding human language and spoken words
- Familiarity with VUI applications
- Setting up AWS and building Alexa skills with an existing template
- Basics of Amazon AWS
- Creating fully functional Alexa skills using Amazon’s API
- Deploying the skills
- Pipeline for speech recognition
- Processing and extracting features from sound signals
- Building probabilistic and machine learning language models to extract words and grammar from sound signals
COURSE DETAILS:
Instructor: Luis Serrano, Jay Alammar, Arpan Chakraborty, Dana Sheahen
Level: Intermediate/Advanced
Duration: 3 months
User Review: 4.5/5
No. of Reviews: 508
Price: $299/monthly, $763 for 3-month access
6. Applied Artificial Intelligence: Natural Language Processing – Future – FutureLearn
This certification is part of the Advanced and Applied AI on Microsoft Azure Expert Track. However, the module on NLP can be enrolled separately. The program is available on FutureLearn. You will learn about NLP and understand machine translation, semantics, syntactic passing, and dialectal systems throughout the course. Besides, the learners will be introduced to vision and language joint learning and inference problems, and other challenges in NLP.
The learners will also explore the concepts of multimodal intelligence tasks and understand the uses of deep learning models for image captioning and visual question answering.
By the end of the course, the learners will have a comprehensive understanding of NLP and become practically equipped to implement deep learning models for NLP tasks such as machine translation and conversation and build deep reinforcement learning models for different NLP solutions.
Some of the critical concepts of this curriculum include:
- Introduction to NLP and Deep Learning
- Overview of Classic Machine Learning Methods and Cutting-Edge Deep Learning Methods
- Neural Models for Machine Translation
- Introduction to Statistical Machine Translation
- Deep Semantic Similarity Model and its Applications
- Natural Language Understanding: Continuous Word Representations and Neural Knowledge Base Embedding
- Deep Reinforcement Learning in NLP
- Vision-Language Multimodal Intelligence
COURSE DETAILS:
Instructor: Industry Professionals
Level: Intermediate
Duration: 3 weeks
User Review: NA
No. of Reviews: NA
Price: $39/monthly
7. Natural Language Processing in TensorFlow – Coursera
This is another excellent course offered by DeepLearning AI on Coursera. Although the program is part of the TensorFlow Developer Professional Certificate, this specific module is available for separate enrollment. The course’s prerequisites require participants to have experience in Python programming and a background in basic mathematics.
The training offers concepts on NLP using TensorFlow to build models that can perform NLP tasks such as processing text, tokenization, and representing sentences as vectors for using as an input for the neural network. Further, the learners will deep dive into the concepts of RNNs, GRUs, and LSTM models in TensorFlow.
Finally, the learners will have solid hands-on experience by understanding how to train LSTM on existing text to create original poetry as part of the projects.
The course contents are:
Sentiment in Text
- Introduction
- Word Based Encodings
- Using APIs
- Text to Sequence
- Tokenizer
- Padding
- Working with Tokenizer and Padding (Practical Sessions)
Word Embeddings
The second module lets learners learn how tokens are mapped as vectors in a high-dimensional space. Besides, learners learn to use embedding and labeled examples to tune the vectors to identify words with similar meanings in a similar direction in the vector space. Again, learners focus on training the neural network for sentiment analysis.
Sequence Models
- Introduction
- LSTMs
- Implementing LSTM in code
- Accuracy and Loss
- Looking into the Code
- Using a Convolutional Network
Sequence Models and Literature
- Introduction
- Preparing the Training Data
- More on Training
- Finding What the Next Word Should Be
- Predicting a Word
- Poetry using NLP
COURSE DETAILS:
Instructor: Laurence Moroney
Level: Intermediate
Duration: 25 hours
User Review: 4.6/5
No. of Reviews: 5815
Price: Free Enrollment (Charges applicable for certification)
8. Deep Learning Foundations: Natural Language Processing with TensorFlow – LinkedIn Learning
This training program is available on LinkedIn Learning. In this course, the learners will learn to harness the power of NLP and deep learning models and implement them for textual data for better decisions. In addition, the learners will deep dive into the concepts of RNNs and understand word encoding and the use of TensorFlow for tokenization.
Further, the learners delve into the concepts of word embedding and learn to implement TensorFlow to classify movie reviews and project vectors as part of the practical sessions. Besides, the concepts on LSTM are discussed in detail, and critical concepts are offered to enable learners to understand how to improve movie review classifiers.
Finally, the learners will learn to train RNNs to predict the next word in a sentence and generate original text.
The course curriculum contents are:
- Leveraging deep learning for natural language processing
- Introduction to NLP
- Word Encodings
- Tokenization using TensorFlow
- Padding the Sequence
- Recognizing Sarcasm in Texts
- Introduction to Word Embeddings
- Classifying Movie Review using TensorFlow
- Projecting Vectors using TensorFlow
- Building a Text Classifier
- Introduction to RNNs
- Implementing LSTMs with TensorFlow
- Improving the Movie Review Classifier
- Text Generation and Predicting the Next Word
- Generating Poetry
- Conclusion
COURSE DETAILS:
Instructor: Harshit Tyagi
Level: Intermediate
Duration: 1 hour 47 minutes
User Review: NA
No. of Reviews: NA
Price: 1-month Free Trial (Charges applicable after Trial Period)
9. Natural Language Processing in Python Skill Track – DataCamp
DataCamp is yet another popular platform that offers this NLP course. In this expert track program, the participants will gain core NLP skills and learn to convert data into insights.
Multiple projects focus on allowing learners to build models for learning to automatically transcribe TED talks or identify if a given movie review is positive or negative. The learners will also explore various popular NLP Python libraries such as NLTK and Scikit-learn, spaCy, and speech recognition.
In addition, the learners will become familiar with concepts like identifying words and extracting topics in text using NLP applications.
Finally, the learners will learn to build chatbots to transform human language into actionable instructions. For the final projects, the learners will create a model that helps transcribe audio files and extract insights from real-world sources such as Wikipedia or online review sites, including data from a flight booking system.
The course contents are:
- Introduction to Natural Language Processing in Python
- Sentiment Analysis in Python
- Building Chatbots in Python
- Advanced NLP with spaCy
- Spoken Language Processing in Python
- Feature Engineering for NLP in Python
COURSE DETAILS:
Instructor: Katherine Jarmul, Violeta Misheva, Alan Nichol, Ines Montani
Level: Intermediate
Duration: 25 hours
User Review: NA
No. of Reviews: NA
Price: Pricing information available on Sign Up
10. NLP Certification Training with Python – Edureka
Edureka’s NLP certification training covers all the essentials of text processing and enables learners to learn about classifying text using machine learning algorithms. In addition, there are other critical concepts covered, such as tokenization, stemming, lemmatization, POS tagging named entity recognition, syntax tree parsing, and more.
The learners will also explore the NLTK library in Python to perform NLP tasks. In addition, the learners will learn to build text classifiers using the Naïve Bayes algorithm. Besides, the course’s prerequisites include working knowledge of Python programming and a good understanding of machine learning concepts. The course contents are:
Introduction to Text Mining and NLP
- Overview of Text Mining
- Need for Text Mining
- NLP in Text Mining
- Applications of Text Mining
- OS Module
- Reading, Writing to Text, and Word Files
- Setting the NLTK Environment
- Accessing the NLTK Corpora
Extracting, Cleaning, and Pre-Processing Text
- Tokenization
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigram, and Ngrams
- Stemming
- Lemmatization
- Stopwords
- POS Tagging
- Named Entity Recognition
Analyzing Sentence Structure
- Syntax Trees
- Chunking
- Chinking
- Context-Free Grammars
- Automating Text Paraphrasing
Text Classification I
- Machine Learning
- Bag of Words
- Countvectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
Text Classification II
- Converting Text to Features and Labels
- Multinomial Naïve Bayes Classifier
- Leveraging Confusion Matrix
In-Class Project
- Implementing Text Processing Techniques Starting with Tokenization
- Expressing End-to-End Work on Text Mining
- Implementing Machine Learning with Text Processing
COURSE DETAILS:
Instructor: Industry Professionals
Level: Intermediate
Duration: 3 weeks
User Review: 5/5
No. of Reviews: 4000
Price: $233.6
Conclusion
In recent years, NLP is becoming part of our daily lives, with NLP integrated into websites in chatbots to answer queries and assess e-commerce feedback to understand the customer segments. From asking Siri or Alexa to play a song or providing an address or information to intelligent lighting solutions to intelligent driving assistance mechanisms, NLP is practically everywhere.
With several advances in the field and wide acceptance of NLP-based products in the market, it is definite that this technology will continue to flourish and provide various novel technologies in the future. Market leaders such as Google, Amazon, and other companies continue to hire NLP engineers each year with high salary packages and offered exciting and challenging job prospects.
Besides, Talent.com presents mind-blogging statistics of NLP engineer salaries in the USA with a median salary of $155,000, while the most experienced professionals are offered between $187,500 to $200,00 according to different regions. Therefore, it is evident that NLP engineers are one of the most sought-after professionals in the industry.
Any aspirant or professional willing to deep dive into the NLP industry should upskill themselves with NLP skills to gain some of the best job positions and lucrative opportunities in the marketplace.
