Natural Language Processing In Python A Complete Guide




Python Online Training


Freshers and Career Changers


Both Classroom and Online Classes


Week Days and Week Ends

Duration :

60 Days

Python Objectives

•Best practices for Python
•You learn how to use Python code.
•How to create an Python project from scratch.
•How to write Python from scratch (no experience required!)
•Learn Python in the most efficient and easy way
•Learn fundamentals of Python for Beginners: Practical and hands-on learning
•An easy way to learn one of the widely used Python
•you will be confident in your skills as a Developer / designer
•Learn and understand the fundamentals of Python and how to apply it to web development.

natural language processing in python a complete guide Training Highlights

•Post training offline support available
•Free technical support for students
•Doubt clarification in class and after class
•The courses range from basic to advanced level
•We provide Classroom and Online training in Metro Cities
•Hands On Experience – will be provided during the course to practice
•Live project based on any of the selected use cases, involving implementation of the concepts
•We help the students in building the resume boost their knowledge by providing useful Interview tips

Who are eligible for Python

•Architect, Program Manager, Delivery Head, Technical Specialist, developer, Sr. Developer, Transition Manager, Quality Manager, Consultant
•DBA, Developers, Programmers, Software Engineers, QA Managers, Product Managers, Development Managers, Mobile Developers, IOS Developers, Android
•Java, Core Java, J2ee, Ui, Java Fullstack, Front End, Angularjs, Angular, React.js, Java Senior Developers, Java Developers, Java Lead, Ui Lead, Ui Developers
•Protocol Testing, Php Developer, Oracle, Senior Managers, Oracle DBA, Dotnet, Java, oracle, DBA, Database Administration, 12c, RAC, Goldengate
•Solution Architect, Technical Lead, Software Developer, Testing Engineer, Project Manager, sap, sas, sql, magento, wordpress, laravel, mysql, Payment Gateways


•Natural Language Processing with Python
•The Course Overview
•Installing and Setting Up NLTK
•Implementing Simple NLP Tasks and Exploring NLTK Libraries
•PartOfSpeech Tagging
•Stemming and Lemmatization
•Named Entity Recognition
•Frequency Distribution with NLTK
•Frequency Distribution on Your Text with NLTK
•Concordance Function in NLTK
•Similar Function in NLTK
•Dispersion Plot Function in NLTK
•Count Function in NLTK
•Introduction to Recurrent Neural Network and Long Short Term Memory
•Programming Your Own Sentiment Classifier Using NLTK
•Perform Sentiment Classification on a Movie Rating Dataset
•Starting with Latent Semantic Analysis
•Programming Example of Principal Component Analysis
•Programming Example of Singular Value Decomposition
•Test Your Knowledge
•Mastering Natural Language Processing with Python
•Substituting and Correcting Tokens
•Similarity Measures
•Understanding Word Frequency
•Introducing PartsofSpeech Tagging
•Default Tagging
•Statistical Modeling Involving the ngram Approach
•Developing a Chunker Using POStagged Corpora
•Treebank Construction
•Extracting Context Free Grammar CFG Rules from Treebank
•Creating a Probabilistic Context Free Grammar from CFG
•Introducing Semantic Analysis
•Introducing NER
•An NER System Using Hidden Markov Model
•Generation of the synset id from Wordnet
•Introducing Discourse Analysis
•Anaphora Resolution
•Evaluation of NLP Tools
•Next Generation Natural Language Processing with Python
•NLP and Its Uses
•Statistical Analysis of Language Counting Versus Understanding
•Exploring Different Types of Text Data
•NLP Libraries in Python and Installation
•Finding and Loading Spam SMS Data
•Preparing SMS Data for Analysis and Training a Classifier
•Classifying Messages Evaluating and Testing
•Understanding Text as Noisy Data
•Splitting Documents into Parts
•Turning Words into Numbers
•Supervised Learning Refresher
•Supervised Learning Refresher Continued
•Building a Pipeline in scikitlearn to Categorize News Articles
•Optimizing a Classifier Using GridSearchCV
•Deploying a Trained Model in Production
•Finding Structure in a Text Corpus
•Understanding Gensim for Efficient Topic Modelling
•Creating a Corpus and Extracting Topics
•Evaluation of Topic Models
•Working with Vector Space Models
•Implementing Semantic Parsing
•Part-Of-Speech Tagging