Natural Language Processing with Python Training

Self-Paced, Live Online & Classroom Enterprise Certification Training

Natural Language Processing with Python course will take you through the essentials of text processing all the way up to classifying texts using Machine Learning algorithms. You will learn various concepts such as Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing and so on using Python’s most famous NLTK package. Once you delve into NLP, you will learn to build your own text classifier using the Naïve Bayes algorithm.

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Program Architect

Prof. Dr. James G. Shanahan

Dr. James has spent the past 25 years developing and researching cutting-edge artificial intelligent systems. He has (co)founded several companies, advises high-tech startups and held appointments at AT&T (Executive Director of Research), Turn Inc., Xerox Research, Mitsubishi Research, and at Clairvoyance Corp. He teaches at UC Berkeley and has published six books, more than 50 research publications, and has over 20 patents in the areas of machine learning and information processing.

What is Natural Language Processing with Python Course about?

Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed.

This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience.

What are the objectives of Natural Language Processing with Python Course ?

The training enables you to:

Explain the basics of Natural Language Processing in the most popular Python Library: NLTK
Adapt techniques to access or modify some of the most common file types
Using I python notebooks, master the art of step by step text processing
Gain insight into the 'Roles' played by an NLP Engineer
Interpret Bag of Words Modelling and Tokenization of Text.
Utilize n-Gram Models to model and analyze the Bag of words from Corpus
Interpret Latent Semantic Analysis and its usage in the processing of context-aware Semantic Content.
Work with real-time data
Interpret Sentiment Analysis one of the most interesting applications of Natural Language Processing
Gain expertise to handle business in the future, living the present

Who is Natural Language Processing with Python Course for?

Developers
Data Analytics Professionals
Teams who want to design automatic predictive models on text data

What are the prerequisites for Natural Language Processing with Python Course?

Working knowledge in Python
Good Understanding of Machine Learning Concept

Available Training Modes

Live Online Training

18 Hours

Classroom Training

3 Days

Course Outline Expand All

Expand All

Artifical Intelligence - Fundamentals

Introduction

What is AI?

Philosophy of AI

Goals

What contributes to AI?

Programming without and with AI

Applications of AI

Types of Intelligence

Agents and Environments

Python - A Refresher

Why Python for ML?

Anaconda – Overview and Installation

Using Jupyter Notebook

Variables

Comprehension

Functions and Modules

Concept of Classes and Objects

Python Modules - A Refresher

NumPy – Array manipulation

Pandas – Data Analytics

Matplotlib and Seaborn – Data Visualization

Sklearn – Machine Learning (Regression and Classification)

Natural Language Processing (NLP)

Introduction

History of NLP

Study of Human Languages

Ambiguity and Uncertainty in Language

Phases

Text Mining

Overview of Text Mining

Need of Text Mining

Using NLP

Applications of Text Mining

OS Module

Reading and Writing the files

Setting the NLTK environment

Accessing the NLTK corpora

Extracting, Cleaning and Pre-Processing Text

Tokenization

Frequency Distribution

Different types of Tokenizers

Stemming

Lemmatization

Bigrams, Trigrams and Ngrams

Stopwords

POS Tagging

Named Entity Recognition

Analysing Sentence Structure

Regular Expressions

Syntax Trees

Chunking and Chinking

Context Free Grammars (CFG)

Automatic Text Paraphrasing

Text Classification

What is Text Classification?

How does Text Classification works?

Applications

Usecases

Text Summarization

What is Text Summarization?

Steps involved in Summarization

Applications

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