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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
• One hot encoding
• 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.
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