Natural Language Processing :Basics to Advanced Course

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 48.0 KHz
Language: English | Size: 3.31 GB | Duration: 4h 29m

Natural Language Processing : Basics to Advanced with pratical implementation using Python, TensorFlow, spaCy, gensim
What you'll learn
Concepts of Natural Language Processing and its Applications
Pratical implementation of Natural Language Processing Technqiues using Python, TensorFlow, spaCy and gensim libraries

Description
In this course you will be learning about Natural Language Processing (NLP) from an experienced professional. Giving machines the capacity to find meaning in unstructured data pulled from natural language holds notable promise. By 2025, the global NLP market is expected to reach over $34 billion, growing at a CAGR of 21.5% and there would be high deamand for NLP skills.

In this course I cover length and breadth of topics in NLP. I explain NLP concepts in a simple way along with practical implementation in Python using libraries like NLTK, spaCy, TensorFlow. I also discuss various topics like text pre-processing, text classification, text summarization, topic modelling and word embeddings. I also cover NLP applications in various domains like healthcare, finance. I am sure this course should help you in getting started and also become proficient in NLP

In this video course you will learn the following about Natural Language Processing:

Introduction to NLP

Its Applications in domains like finance and healthcare

Stemming and Lemmatimzation with NLTK and spaCy

TF-IDF, Bag of Words Represntation

Named Entity Recognition with spaCy in python

Word2Vec model and custom word2vec model in python

Exploratory data analysis on text dataset using python

LDA topic modelling

Text Classification with Neural network using Tensorflow in Python

Text Classification with Convolutional Neural Network( CNN) using Tensorflow in Python

Text Classification with Long Short Term memory( LSTM) networks using Tensorflow in Python

Extractive Text Summarization using gensim and python

Abstractive Text Summarization using Google PEGASUS