Diploma in Artificial Intelligence and Natural Language Processing in collaboration with MAKAUT, WB

COURSE NAME

:

One Year Diploma in Artificial Intelligence and Natural Language Processing

COURSE CODE

:

CONTACT HOURS

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180 Hours (60 Hours Theory, 120 Hours Laboratory)

Prerequisite:

Bachelor Degree (B.Sc/B.E/B.Tech) or Diploma in Computer Science, Information Technology, Linguistics, or allied streams.

Course Objective:

1. Provide an overview of the basic concepts of Artificial Intelligence with an in-depth treatment of Natural Language Processing.

2. Analyse the applicability of AI and NLP based solutions is different domains and business scenarios.

3. Illustrate different frameworks and technologies that are used to develop conversational AI applications using NLP

Course Outcome : At the end of the course students will be able to

1: Understand in-depth what is Natural Language Processing and how it works.

2: Design conversational AI solutions for various domains.

3: Build conversational AI applications for enterprises and businesses.

#

Topic

Theory

Lab

1.

Overview of AI

2

2

2.

Approaches to AI – symbolic vs connectionist

2

2

3.

Aspects of symbolic AI

2

4

4.

Introduction to Machine Learning

4

4

5.

Common algorithms in ML

6

8

6.

Introduction to Natural Language Processing and its applications

4

4

7.

The NLP pipeline

6

8

8.

NLP Software Tools

4

8

9.

Dialogue Systems and Conversational AI

4

4

10.

Frameworks and Tools for building conversational AI systems

4

4

11.

Building a Chatbot System

4

8

12.

NLP Applications

4

4

13.

Deep Learning for NLP

4

6

14.

Industry Project

10

54

TOTAL

60

120

Course Content:

Overview of AI [2Theory, 2Laboratory]:

What is AI? – History of AI – Intelligent autonomous agents – Applications of AI – Artificial General Intelligence

Approaches to AI – symbolic vs connectionist [2Theory , 2Laboratory]:

Symbolic approach – Physical symbol system hypothesis – symbolic representation – symbolic learning – Connectionist approach – connectionist learning – connectionist representation – Integration of Symbolic and Connectionist approaches

Aspects of Symbolic AI [2Theory, 4Laboratory]:

Search – Knowledge Representation – Reasoning – Logic – Expert Systems – Planning Systems – LISP – Prolog

Introduction to Machine Learning [4Theory, 4Laboratory]:

What is machine learning? – Applications of machine learning – How does ML work? –Training data – model/algorithm – testing data – evaluation – prediction – tools required – WEKA

Common Algorithms in Machine Learning [6Theory, 8Laboratory]:

Labeled/unlabelled data – Supervised – classification – regression – Decision Tree – Conditional Random Field – Random Forest – Nearest Neighbour – Support Vector Machines – Unsupervised – clustering – association rules – Apriori algorithm – K-means

Python programming- Statements and comments – Data types and Variables – operators – Input/output – Data types – list – tuple – string – set – dictionary – arrays – matrix – File handling – file operation – directory – exceptions – Python libraries – Numpy – Pandas – Keras – scikit-learn

Introduction to NLP and its application [4Theory, 4Laboratory]:

What is NLP? – NLU and NLG – History – Computational Linguistics – Rule based approaches – Statistical approaches – Corpora – Thesauri – Gazetteers – Distributional Semantics – Word embeddings – Applications of NLP– Grammar and style checking – Document processing – Machine Translation – Dialog systems

The NLP Pipeline [6Theory, 8Laboratory]

Phonology – Morphology – Syntax – Tokenisation – Lemmatisation – Part of Speech tagging– Constituent membership – Syntactic Parsing – Named Entity Resolution– Coreference resolution – Dependency Parsing

NLP Software Tools [4Theory, 8Laboratory]:

Python tools – NLTK – spaCy – Java tools – Apache OpenNLP – Stanford NLP – Cloud based tools – IBM Watson NLU– Microsoft LUIS– AWS Comprehend

Dialogue Systems and Conversational AI [4Theory, 4Laboratory]:

History – Types of Dialogue Systems – Task oriented – Fulfillment – Chatbots – Corpus based – Retrieval based – Architecture of a Dialogue system – Dialog Manager – Intent – Entity – Slot – Turns – Initiatives – System – Mixed – Barge-in – Context – Open domain QA systems

Frameworks and Tools for building conversational AI systems [4Theory, 4Laboratory]:

IBM Watson Assistant – Google DialogFlow – RASA – LearnITyBot

Building a Chatbot System [4Theory, 8Laboratory]:

Design consideration for Chatbots – Implementing NLU – specifying intents – specifying entities – specifying slots – Selecting NLU tool – implementing the Dialog Manager – handling context – implementing the Fulfillment system

NLP Applications[4Theory, 4Laboratory]:

Sentiment analysis – Semantic Search – Document summarisation – Document auto-tagging – Latent semantic analysis (LSA)

Deep learning for NLP [4Theory, 6Laboratory]:

History – Perceptron – Activation function – Neural network – Deep neural network – Loss functions – Training – Gradient descent – DL for NLP – Distributional hypothesis – embeddings- Word2Vec – CBOW – Skip-grams

Industry Project [10Theory, 54Laboratory]:

Each student will be required to work on a Conversational AI project relevant to the industry. This will involve performing business requirements analysis, solutions design, and implementation.

Reference Books:

  1. Foundations of Statistical Natural Language Processing by Christopher Manning and Hinrich Schütze, The MIT Press
  2. The Oxford Handbook of Computational Linguistics, Oxford University Press
  3. Speech and Language Processing, Dan Jurafsky and James H. Martin, Prentice Hall

For more information and registration please contact

Aunwesha Academy

Learning and Development partner of Aunwesha Knowledge Technologies Pvt. Ltd

120A Linton Street Kolkata 700014 | email: enq@aunweshaacademy.com | Call/Whatsapp: 9088998585/9051952573/9830379592/6290622433