Name: Introduction To Natural Language Processing Course Certification- Henry Harvin® in Bareilly | Henry Harvin
Uploaded: 2021-03-16 11:58:44
Description: Important Branch of Artificial Intelligence | Deals with Interaction between computers and humans using the Natural language | NLP techniques depend on Machine Learning to procure meaning from human languages | NLP is used to decipher, read, and understand human language

Module 1: Introduction to NLP
In this module the candidate will learn about Natural Language Processing, which includes its Components, Application, challenges, Scope, Data Formats and Text Processing. It will also focus on Preprocessing in NLP and Tweets Cleanup Using Regular Expressions.
- Introduction to Natural Language Processing
- Components of NLP
- Applications of NLP
- Challenges and scope
- Data formats
- Text Processing
- Assisted Practice: Implement Text Processing Using Stemming and Regular Expression after Noise Removal and Convert It into List of Phrases
- Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop
- Tweets Cleanup and Analysis Using Regular Expressions
- Test of Module 1
Module 2: Feature Engineering on text data
In this module, the candidate will learn about Featuring Engineering on Text Data, which includes understanding and working of N-Gram, Bag of Words, Document Term Matrix, Word Embedding, Word Analogies, Dense Encoding, Topic Modelling and how to build your own News Search Engine.
- N-Gram
- Bag of Words
- Document Term Matrix
- TF-IDF
- Levenshtein Distance
- Word Embedding(Word2Vec)
- Doc2vec
- PCA
- Word Analogies
- Dense Encoding
- Topic Modelling
- Assisted Practice: Word2vec Model Creation
- Assisted Practice: Word Analogies Demo
- Assisted Practice: Identify Topics from News Items
- Build Your Own New Search Engine
- Quiz Module 2
Module 3: Natural Languages Understanding Techniques
In this module, the candidate will learn about NLP understanding Techniques, which include understanding Parts of Speech Tagging, Dependency, Constituency, and Morphological Parsing, Fuzzy search, Sentiment analysis, Document Indexing Etc.
- Parts of Speech Tagging
- Dependency Parsing
- Constituency Parsing
- Morphological Parsing
- Named Entity Recognition
- Coreference Resolution
- Word Sense Disambiguation
- Fuzzy Search
- Document and Sentence Similarity
- Document Indexing
- Sentiment Analysis
- Assisted Practice: Analyzing the Disease and Instrument Name with the Action Performed
- Assisted Practice: Analyzing the Sentiments
- Assisted Practice: Extract City and Person Name from Text
- Identifying Top Product Feature from User Reviews
- Quiz Module 3
Module 4: Natural Language Generation
In this module, the candidate will learn about NLP Generation, which includes understanding Retrieval Based Model and Generative Based Model, Language Modelling, Sentence Correction and Creation of our own Spell Checker.
- Retrieval based model
- Generative based model
- AIML
- Language Modelling
- Sentence Correction
- Assisted Practice: Create AIML Patterns for QnA on Mental Wellness
- Assisted Practice: To Predict the Next Word in a Sentence
- Create your Own Spell Checker
- Quiz Module 4
Module 5: NLP Libraries
In this module, the candidate will learn about NLP Libraries, which includes a proper understanding of Spacey, Gensim, TextBlob, Stanford NLP, and the creation of your own NLP Module.
- Spacy
- NLTK
- Gensim
- TextBlob
- Stanford NLP
- LUIS
- Assisted Practice: Simplilearn Review Analysis
- Create your Own NLP Module
- Quiz Module 5
Module 6: NLP with machine learning and deep Learning
In this module, the candidate will learn about Machine Learning and Deep Learning with NLP, which includes Neural Machine Translation, Recurrent Neural networks, Long Short Term Memory, Text Classification and summarization, Document Clustering Etc.
- Neural Machine Translation
- Introduction to RNN, LSTM
- LSTM Forward Pass
- LSTM Backprop through time
- Applications of LSTM
- Advanced LSTM Structures
- Encoder Decoder Attention
- Text Classification and Summarization
- Document Clustering
- Attention Mechanism
- Question Answering Engine
- Assisted Practice: Target Spam Words and Patterns
- Assisted Practice: Summarization of News
- Document Clustering for BBC News
- Quiz Module 6
Module 7: Speech Recognition Techniques
In this module, the candidate will learn about Speech Recognition Techniques, which includes, understanding Basic Concepts for Voice, Sequential Models, Saving Model, creating Speech Model, and Speech Libraries.
- Basic concepts for voice/sound
- Sequential models
- Creating speech model
- Saving model
- Implementation/use cases
- Speech libraries
- Assisted Practice: Translation from Speech to Text
- Speech to Text: Extract Keywords from Audio Reviews
- Quiz Module 7
Module 8: Complimentary Module 1: Soft Skills Development
- Business Communication
- Preparation for the Interview
- Presentation Skills
Module 9: Complimentary Module 2: Resume Writing
- Resume Writing

Natural Language Processing Course in Bareilly

11 Nov 2024

16 Hours

11 Nov 2024

16 Hours

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Key Highlights

16 Hours of Instructor-Led Sessions

5 Hours of Live Interactive Doubt Solving Sessions

3 Hours of Live Master Sessions by Industry Experts

192 Hours of Self-Paced Learning

Job-Ready Portfolio of 2 Capstone Projects

80 Auto-Graded Assessments

4+ Industry Case Studies

24 Guided Hands-On Exercises

2+ Assignments and Mini Projects

1 Mock Interview and 1 Hackathon

Get 1-Year Gold Membership of Henry Harvin® School of AI & Machine Learning

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About The Natural Language Processing Course

Curriculum For Natural Language Processing NLP Course in Bareilly

Module 1: Introduction to NLP

Module 2: Feature Engineering on text data

Module 3: Natural Languages Understanding Techniques

Module 4: Natural Language Generation

Module 5: NLP Libraries

Module 6: NLP with machine learning and deep Learning

Module 7: Speech Recognition Techniques

Module 8: Complimentary Module 1: Soft Skills Development

Module 9: Complimentary Module 2: Resume Writing

Admission Closes On 11 Nov 2024

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What you'll Learn in this course

Models and Techniques used in NLP

Tokenization and NLP Pipeline

Creation of Customised NLP Modules

Speech Recognition Techniques

Sentence and Word Embedding Methods

Sequence Tagging & Tasks

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