Natural Language Processing - Build LLM Web App - RNN LSTM

Natural Language Processing | Build LLM Web App | RNN & LSTM, available at $49.99, has an average rating of 4.5, with 61 lectures, based on 68 reviews, and has 16191 subscribers.

You will learn about You will gain insights on what Natural Language Processing(NLP) is, its Applications & Challenges You will learn Sentence Segmentation, Word Tokenization, Stemming, Lemmatization, Parsing, POS & Ambiguities in NLP You will learn to execute using Machine Learning, NLTK & Spacey You will learn to work with Text Files with Python You will utilize Regular Expressions for pattern searching in text You will use Part of Speech Tagging to automatically process raw text files You will visualize POS and NER with Spacy You will understand Vocabulary Matching with Spacy You will use NLTK for Sentiment Analysis This course is ideal for individuals who are Data Scientists, Python Programmers, ML Practitioners, IT Managers managing data science projects or Python developers interested in learning how to use Natural Language Processing It is particularly useful for Data Scientists, Python Programmers, ML Practitioners, IT Managers managing data science projects or Python developers interested in learning how to use Natural Language Processing.

Enroll now: Natural Language Processing | Build LLM Web App | RNN & LSTM

Summary

Title: Natural Language Processing | Build LLM Web App | RNN & LSTM

Price: $49.99

Average Rating: 4.5

Number of Lectures: 61

Number of Published Lectures: 36

Number of Curriculum Items: 61

Number of Published Curriculum Objects: 36

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

You will gain insights on what Natural Language Processing(NLP) is, its Applications & Challenges

You will learn Sentence Segmentation, Word Tokenization, Stemming, Lemmatization, Parsing, POS & Ambiguities in NLP

You will learn to execute using Machine Learning, NLTK & Spacey

You will learn to work with Text Files with Python

You will utilize Regular Expressions for pattern searching in text

You will use Part of Speech Tagging to automatically process raw text files

You will visualize POS and NER with Spacy

You will understand Vocabulary Matching with Spacy

You will use NLTK for Sentiment Analysis

Who Should Attend

Data Scientists, Python Programmers, ML Practitioners, IT Managers managing data science projects

Python developers interested in learning how to use Natural Language Processing

Target Audiences

Data Scientists, Python Programmers, ML Practitioners, IT Managers managing data science projects

Python developers interested in learning how to use Natural Language Processing

Recent Updates:

Nov 2022: Updated videos for RNN and LSTM

Apr 2023: Added a video lecture on transformers

Sep 2023: Added a video lecture on how to build an LLM web application

Natural Language Processing (NLP) is a very interesting field associated with AI and is at the forefront of many useful applications like a chatbot. Knowledge of NLP is considered a necessity for those pursuing a career in AI. This course covers both the theory as well as the applications of NLP. Case studies are explained along with a walkthrough of the codes for a better understanding of the subject.

A detailed explanation of how to build a web app for NLP using Streamlit is also explained.

NLP is a subfield of computer science and artificial intelligence concerned with interactions between computers and human (natural) languages. It is used to apply machine learning algorithms to text and speech.

For example, we can use NLP to create systems like speech recognition, document summarization, machine translation, spam detection, named entity recognition, question answering, autocomplete, predictive typingand so on.

Nowadays, most of us have smartphones that have speech recognition. These smartphones use NLP to understand what is said. Also, many people use laptops whose operating system has built-in speech recognition.

Some Examples:

1.Cortana

The Microsoft OS has a virtual assistant called Cortana that can recognize a natural voice. You can use it to set up reminders, open apps, send emails, play games, track flights and packages, check the weather and so on.

2.Siri

Siri is a virtual assistant of the Apple Inc.’s iOS, watchOS, macOS, HomePod, and tvOS operating systems. Again, you can do a lot of things with voice commands: start a call, text someone, send an email, set a timer, take a picture, open an app, set an alarm, use navigation and so on.

In this course we will deal with:

a)NLP Introduction:

· What is NLP

· Applications of NLP

· Challenges in NLP

b)Key concepts in NLP:

· Sentence Segmentation

· Word Tokenization

· Stemming

· Lemmatization

· Parsing

· POS

· Ambiguities in NLP

c)NLP in Action

· NLTK

· Sentence Tokenization

· Word Tokenization

· Stemming

· Lemmatization

· Noise Removal

· Spacy

· Parts of Speech Tagging

· Dependency Parsing

· Spell Correction

· Point of View

· Regular Expressions

· Flash Text

· Named Entity Recognition – NER

d)Case studies:

· Speech recognition

· Sentiment analysis

· Word Cloud

· Spam detection

You will not only get fantastic technical content with this course, but you will also get access to both our course-related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants.

All of this comes with a 30-day money back guarantee, so you can try the course risk-free.

What are you waiting for? Become an expert in natural language processing today!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Key concepts in NLP

Lecture 1: Sentence Segmentation

Lecture 2: Tokenization

Lecture 3: Regular Expressions Refresher

Lecture 4: Stemming

Lecture 5: Lemmatization

Lecture 6: Stop Words

Lecture 7: Parts of Speech

Lecture 8: Dependency Parsing

Lecture 9: Encoding | BPE

Lecture 10: Ambiguities in NLP

Chapter 3: Ambiguities in NLP

Lecture 1: Lexical Ambiguity

Lecture 2: Syntactic Ambiguity

Lecture 3: Pragmatic Ambiguity

Lecture 4: Ellipsis Handling

Lecture 5: Structural Ambiguity and Metaphorical Language

Lecture 6: Sarcasm Detection

Chapter 4: Case Studies (with walk through of the codes)

Lecture 1: Case Study 1: Sentiment Analysis & Word Cloud

Lecture 2: Case Study 2: Speech to Text deployment in a call center

Lecture 3: Case Study 3: Text Summarization

Lecture 4: Case Study 4: Spam Classification Using Machine Learning

Chapter 5: Deep Learning in NLP

Lecture 1: Why do you need RNN

Lecture 2: Math Behind RNN

Lecture 3: LSTM

Lecture 4: Build a Spam Detection Model Using RNN and LSTM

Lecture 5: Transformers and Building Q&A system for a pdf file

Chapter 6: Creating an NLP Web App Using Streamlit

Lecture 1: Infrastructure for Streamlit

Lecture 2: Creating a very simple web app and Getting started with streamlit

Lecture 3: Header and Sub Header

Lecture 4: Reading and displaying contents of a file

Lecture 5: Uploading a file

Lecture 6: NLP Wordcloud App

Lecture 7: Deploying the app in Heroku

Lecture 8: Deploying the app in streamlit

Chapter 7: Create a ChatGPT powered app using Streamlit

Lecture 1: LLM Web App

Chapter 8: Bonus Lecture

Lecture 1: Bonus Lecture

Instructors

SeaportAi .
Artificial Intelligence and Business Transformation Experts

Rating Distribution

1 stars: 1 votes

2 stars: 3 votes

3 stars: 10 votes

4 stars: 18 votes

5 stars: 36 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!