Natural Language Processing (NLP) with BERT

Natural Language Processing (NLP) with BERT, available at Free, has an average rating of 4.25, with 11 lectures, based on 1510 reviews, and has 25505 subscribers.

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You will learn about Natural Language Processing How to implement the BERT model Sentiment Analysis How to code in Python with Google Colab This course is ideal for individuals who are Anyone interested in Natural Language Processing or Anyone interested in learning about BERT It is particularly useful for Anyone interested in Natural Language Processing or Anyone interested in learning about BERT.

Enroll now: Natural Language Processing (NLP) with BERT

Summary

Title: Natural Language Processing (NLP) with BERT

Price: Free

Average Rating: 4.25

Number of Lectures: 11

Number of Published Lectures: 10

Number of Curriculum Items: 11

Number of Published Curriculum Objects: 10

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Natural Language Processing

How to implement the BERT model

Sentiment Analysis

How to code in Python with Google Colab

Who Should Attend

Anyone interested in Natural Language Processing

Anyone interested in learning about BERT

Target Audiences

Anyone interested in Natural Language Processing

Anyone interested in learning about BERT

Are you ready to dive right into one of the most exciting developments in data science right now: Google鈥檚 breakthrough NLP algorithm, BERT!

Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science鈥nd now we鈥檙e giving you that chance for FREE.

Our new case study course: Natural Language Processing (NLP) with BERT shows you how to perform semantic analysis on movie reviews using data from one of the most visited websites in the world: IMDB!

Perform semantic analysis on a large dataset of movie reviews using the low-code Python library, Ktrain.

But, why is BERT so revolutionary?

Not only is it a framework that has been pre-trained with the biggest data set ever used, it is also remarkably easy to adapt to different NLP applications, by adding additional output layers. This allows users to create sophisticated and precise models to carry out a wide variety of NLP tasks.

AI expert Hadelin de Ponteves guides you through some basic components of Natural Language Processing, how to implement the BERT model and sentiment analysis, and finally, Python coding in Google Colab.

Here鈥檚 how this 1-hour case study course will unfold:

Part 1: Data Preprocessing

Loading the IMDB dataset

Creating the training and test sets

Part 2: Building the BERT model

Part 3: Training and evaluating the BERT model

Getting the learner instance

Training and evaluating the BERT model

Plus, you鈥檒l do it all using Google鈥檚 Colab free, browser-based notebook environment that runs completely in the cloud. It鈥檚 a game-changing interface that will save you time and supercharge your data science toolkit.

If you鈥檝e been waiting for a chance to put your NLP skills to the test then this is the opportunity you’ve been waiting for. Click the 鈥楨nroll Now鈥?button and see you inside!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Getting started

Lecture 2: Dataset + Code + Colab Link

Lecture 3: Learning Paths

Lecture 4: Importing the libraries

Chapter 2: Part 1: Data Preprocessing

Lecture 1: Loading the IMDB dataset

Lecture 2: Creating the training and test sets

Chapter 3: Part 2: Building the BERT model

Lecture 1: Building the BERT model

Chapter 4: Part 3: Training the BERT model

Lecture 1: Getting the learner instance

Lecture 2: Training and evaluating the BERT model

Chapter 5: Congratulations!! Dont forget your Prize 馃檪

Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)

Instructors

Hadelin de Ponteves
Passionate AI Instructor

SuperDataScience Team
Helping Data Scientists Succeed

Ligency Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 12 votes

2 stars: 26 votes

3 stars: 141 votes

4 stars: 546 votes

5 stars: 785 votes

Frequently Asked Questions

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