Toxic Question Classification using BERT and Tensorflow 2.4

[mitsumi]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 18 lectures (2h 43m) | Size: 813 MB

Toxic Question Classification using BERT and Tensorflow 2.4

What you'll learn:
At the end of my course students will be able to build text classification engine

Requirements
Beginner Data Science and Software engineers

Description
Course Description

Learn to build Toxic Question Classifier engine with BERT and TensorFlow 2.4

Build a strong foundation in Deep learning text classifiers with this tutorial for beginners.

Understanding of text classification

Learn word embeddings from scratch

Learn BERT and its advantages over other technologies

Leverage pre-trained model and fine-tune it for the questions classification task

Learn how to evaluate the model

User Jupyter Notebook for programming

Test model on real-world data

A Powerful Skill at Your Fingertips Learning the fundamentals of text classification h puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, have excellent documentation. Text classification is a fundamental task in natural language processing (NLP) world.

No prior knowledge of word embedding or BERT is assumed. I'll be covering topics like Word Embeddings, BERT, and Glove from scratch.

Jobs in the NLP area are plentiful, and being able to learn text classification with BERT will give you a strong edge. BERT is state of art language model and surpasses all prior techniques in natural language processing.

Google uses BER for text classification systems. Text classifications are vital in social media. Learning text classification with BERT and Tensorflow 2.4 will help you become a natural language processing (NLP) developer which is in high demand.

Content and Overview

This course teaches you how to build a text classification engine using open source Python, Tensorflow 2.4 and Jupyter framework. You will work along with me step by step to build text classification engine

Word Embeddings

* Word2Vec

* One hot encoding

* Glove

* BERT

Build Application

* Download dataset

* Download pre-trained model

* Fine Tune Model on Quora dataset

* Model Evaluation

* Testing Model on real-world data

What am I going to get from this course?

Learn text classification with BERT and Tensorflow 2.4 from a professional trainer from your own desk.

Over 10 lectures teaching you how to build a text classification engine

Suitable for beginner programmers and ideal for users who learn faster when shown.

Visual training method, offering users increased retention and accelerated learning.

Breaks even the most complex applications down into simplistic steps.

Offers challenges to students to enable the reinforcement of concepts. Also, solutions are described to validate the challenges.

Who this course is for
Beginner Python Developers who are curious about text classification

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