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NLP and Deep Learning

NLP and Deep Learning

In this course you will learn about deep neural networks, and how to use them in processing text with Python (Natural Language Processing or NLP).


In this course you will learn about deep neural networks (deep learning), and how to leverage them in processing, understanding and mining for insights from text. We start with an introduction to neural networks and deep learning. We then dive into essentials of representation learning like word and document embeddings and then move onto more complex methodologies including convolutional neural networks and sequence models and deep transfer learning approaches including universal embeddings and transformers. Popular applications are also covered with hands-on tutorials and exercises including text classification, information extraction, recommenders, search, summarization, translation and more.

  • Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

In this course you will learn:

  • How to specify and run artificial neural networks and deep networks
  • How deep networks represent words as binary vectors
  • How to use recurrent neural networks for sequential learning (sequence-to-sequence modeling)
  • How to use attention models to improve predictive performance

Who Should Take This Course

Data scientists and aspiring data scientists.

Our Instructors

Mr. Dipanjan Sarkar

Mr. Dipanjan Sarkar

Dipanjan (DJ) Sarkar is a Data Science Lead, published author and has been recognized as a Google Developer Expert in Machine Learning by Google in 2019. He has also been recognized as one of the Top Ten Data Scientists in India, 2020 by a few leading technology magazines and publishing houses. Dipanjan has led advanced analytics initiatives working with several Fortune 500 companies like Applied Materials, Intel and Open Source organizations like Red Hat (now IBM). He primarily works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems.

He holds a master of technology degree from IIIT Bangalore, with specializations in data science and software engineering and completed his post graduate diploma in machine learning and artificial intelligence from Columbia University in the City of New York.

Dipanjan has been an analytics practitioner and consultant for several years now, specializing in machine learning, natural language processing, computer vision and deep learning. Having a passion for data science and education, he also acts as an AI Advisor, Subject Matter Expert and Instructor at various organizations like Springboard, Propulsion Academy and (The Institute for Statistics Education) where he helps people build their skills on areas in data science and artificial intelligence. Dipanjan also beta-tests new courses on data science for popular MOOC platform, Coursera, before they are released. He is a published author, having authored several books on R, Python, Machine Learning, Natural Language Processing, and Deep Learning which includes Text Analytics with Python 2nd ed.

Course Syllabus

Week 1

Introduction to Deep Learning and Representation Learning

  • Introduction to deep neural networks and deep learning
  • Applications of Deep Learning for NLP
  • Essential components of deep learning
    1. Types of models (especially for NLP)
    2. Layers, activation and loss functions 
    3. Gradient descent and Backpropagation
  • Representation Learning for Text Data
    1. Understanding Embeddings 
    2. Types of Embeddings 
    3. CBOW (Continuous Bag of Words) and SkipGram
    4. Word Embedding Models – Word2Vec, GloVe, FastText
    5. Obtaining document embeddings


Week 2

 Context Sensitive Learning: Convnets and Sequential Models

  • Convolutional Neural NetworksConvnets for NLP
    1. Convolutional Neural Networks
    2. 1D-CNNs for Text Classification
  • Sequential Models for NLP
    1. Recurrent Neural Networks
    2. Long Short Term Memory Networks (LSTMs)
    3. Gated Recurrent Units (GRUs)
    4. Bi-directional LSTMs (Bi-LSTMs)
    5. Contextual Embeddings using BiLSTMs – ELMO
  • Sequence-to-Sequence Models for NLP
    1. Encoder-Decoder Models
    2. Applications of Encoder-Decoder Models


Week 3

Deep Transfer Learning for NLP

  • Transfer Learning
    1. Need and importance
    2. Methodologies
  • Pre-trained word embeddings
    1. Word2vec, GloVe, FastText
  • Universal Embeddings
    1. Neural net language model (NNLM)
    2. Universal Sentence Encoder
  • Transformers and Multi-task Learning
    1. Brief and types of transformers
    2. Advantages of pre-trained transformers
    3. Applications


Week 4

Attention, Transformers and Applications

  • Limitations of Sequential and Sequence-to-sequence models
  • Attention in sequential Models
  • Understanding Transformer Models
    1. Encoders and Decoders
    2. Self Attention and Multi-headed Attention
    3. Contextual and Pooled Embeddings
    4. BERT
  • Transformers Variants 
    1. Bidirectional Encoder Representations from Transformers (BERT) variants – Roberta, Albert, XLNet etc.
    2. Efficient Transformers with knowledge distillation – DistilBERT
    3. Generative Transformers – generative pre-training (GPT) family



Class Dates


03/08/2024 to 04/05/2024
Instructors: Mr. Dipanjan Sarkar
07/12/2024 to 08/09/2024
Instructors: Mr. Dipanjan Sarkar
11/08/2024 to 12/06/2024
Instructors: Mr. Dipanjan Sarkar


03/14/2025 to 04/11/2025
Instructors: Mr. Dipanjan Sarkar
07/11/2025 to 08/08/2025
Instructors: Mr. Dipanjan Sarkar
11/14/2025 to 12/12/2025
Instructors: Mr. Dipanjan Sarkar


You should be sufficiently familiar with Python to follow and use code examples (see suggested course below).  You should also be familiar with the neural net material covered in the predictive analytics course, below.


Introduction to NLP and Text Mining

In this course you will be introduced to the essential techniques of natural language processing (NLP) and text mining with Python.
  • Skill: Intermediate
  • Credit Options: ACE, CAP, CEU

Private: Predictive Analytics 2 with Python – Neural Nets and Regression

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on Python, to visualize and explore predictive modeling.
  • Skill: Intermediate
  • Credit Options: ACE, CAP, CEU
Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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NLP and Deep Learning

Additional Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.


Homework in this course consists of coding exercises using Python.

Course Text

The text used for the practical work in this course is Text Analytics with Python (Apress, 2019) by Dipanjan Sarkar, chosen for its wealth of hands on Python illustrations and code.  The code for these illustrations is organized here:

Note: this text is also used in the introductory course, Introduction to NLP and Text Mining


The course uses Python.

Course Fee & Information

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to  Click here for a special introductory discount code.

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate degree, 2 semester hours in computer science, computer information systems, or cyber security. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)


There is no additional information for this course.

Register For This Course

NLP and Deep Learning