5 “Best” NLP Courses & Certifications (September 2023)

As we move further into a data-driven world dependent on AI technologies, Natural Language Processing, or NLP, is becoming one the most demanded skills. It is present nearly everywhere, but most notably in web searches, advertisement, customer service, language translation services, sentiment analysis, and more. 

NLP certifications are crucial for an individual looking to be a leader in this field. 

Here are the top 5 NLP Certifications currently available:

1. Natural Language Processing Specialization (Coursera)

This specialization course is aimed at preparing you to design NLP applications for question-answering and sentiment analysis. You will also learn how to develop language translation tools, summarize text, and build chatbots. 

The course was designed and is taught by experts in NLP, machine learning, and deep learning. Two of those experts are Younes Bensouda Mourri, an instructor of AI at Stanford University, and Lukasz Kaiser, a Staff Research Scientist at Google Brain who co-authored Tensorflow. 

Here are some of the main aspects of this course: 

• Logistic regression, Naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, and translate words
  
• Dynamic programming, hidden Markov models, and word embeddings for auto correction
  
• Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in Tensorflow and Trax
  
• Encoder-decoder, causal, and self-attention, along with T5, Bert, transformer, and reformer
  
• Intermediate Level
  
• Duration: 4 months, 6 hours/week
  

2. Natural Language Processing in TensorFlow (Coursera)

This course is aimed at software developers looking to build AI-powered algorithms. It teaches you the best TensorFlow practices, and you will build NLP systems using it. You will also learn to process text, including tokenizing, as well as resprest sentences as vectors. Other parts of this course involve applying RNNs, GRUs, and LSTMs in Tensorflow. 

It is recommended that you take the first 2 courses of the TensorFlow Specialization and have a solid understanding of coding in Python before taking this course.

Here are some of the main aspects of this course: 

• Train an LSTM on existing text
  
• Build NLP systems using TensorFlow
  
• Applying RNNs, GRUs, and LSTMs in TensorFlow
  
• Intermediate Level
  
• Duration: 14 hours

3. Natural Language Processing in Python (Datacamp)

This course provides you with the core NLP skills needed to convert data into valuable insights. You will learn how to automatically transcribe TED talks, and the course will introduce popular NLP Python libraries such as NLTK, scikit-learn, spaCy, and SpeechRecognition. 

Here are some of the main aspects of this course: 

• Build your own chatbot
  
• Transcribe audio files
  
• Extract insights from real-world sources
  
• Transcribe Ted Talks
  
• 6 courses total
  
• Duration: 25 hours

4. Feature Engineering for NLP in Python (Datacamp)

This course teaches you techniques that will allow you to extract useful information from text and process them into a format suitable for applying ML models. More specifically, you will learn about POS tagging, named entity recognition, readability scores, the n-gram and tf-idf models, and how to implement them using scikit-learn and spaCy. You will also learn to compute how similar two documents are to each other. In the process, you will predict the sentiment of movie reviews and build movie and Ted Talk recommenders. Following the course, you will be able to engineer critical features out of any text and solve some of the most challenging problems in data science!

Here are some of the main aspects of this course: 

• NLP basics like identifying and separating words
  
• Compute how similar 2 documents are to each other
• Basic and advanced libraries
  
• 4 courses total
  
• Over 50 exercises and 15 videos
  
• Duration: 4 hours

5. Advanced NLP with SpaCy (Datacamp)

In this course, you'll learn how to use spaCy, a fast-growing industry standard library for NLP in Python, to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Here are some of the main aspects of this course: 

• Finding words, phrases, names and concepts
• Large scale data analysis
• Processing pipelines
• Training a neural network model