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NLP Certification- BERT, GPTs, Software 3.0 & Multimodal NLP

Multimodal NLP, GPT-4 Expectations, Multilingual NLP for Text Classification, Language Translations & Sentiment Analysis
3.8
3.8/5
(62 reviews)
10,229 students
Created by

8.8

CourseMarks Score®

10.0

Freshness

6.6

Feedback

9.2

Content

Platform: Udemy
Video: 2h 23m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

8.8 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 6/2022.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

6.6 / 10
We analyzed factors such as the rating (3.8/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.2 / 10
Video Score: 7.9 / 10
The course includes 2h 23m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 9.7 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.9 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

16 articles.
67 resources.
0 exercise.
0 test.

Table of contents

Description

This course introduces you to the fundamentals of Transformers in NLP. The topics include are;
1. Recurrent Neural Networks & LSTM
2. Bi-Directional Encoder Representation from Transformers.
3. Masked Language Modelling.
4. Next Sentence Prediction using Transformers.
5. Generative Pre-trained Transformers and their implementation in RASA and SpiCy.
6. Complete Code for Online Fraud Detection System.
7. Complete Code for Text Classification.
8. Complete Code for Language Translation System.
9. Complete Code for Movie Recommender System.
10. Complete Code for Speech to Text Conversion using GPT-2.
11. Complete Code for Chatbot using GPT3.
12. Complete Code for Text Summary System using GPT3.
13. Automated Essay Scoring using Transformer Models.
14. Sentiment Analysis using Pre-trained Transformers.
15. Training and Testing a GPT- 2 for Novel Writing.
16. Game Design using AlphaGo and Transformers.
17. 50+ NLP coding exercises along with complete solutions to complete this certification.

Transformers (formerly known as PyTorch-transformers and pytorch-pretrained-bert) provide thousands of pre-trained models to perform tasks on different modalities such as text, vision, and audio.
These models can be applied on:
•Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages.
•Images, for tasks like image classification, object detection, and segmentation.
•Audio, for tasks like speech recognition and audio classification.
Transformer Models are great with Sequential Data and are Pre-trained which makes them versatile and capable. It allows further to Gain Out-of-the-Box Functionality. Transformer models enable you to take a large-scale LM (language model) trained on a massive amount of text (the complete works of Shakespeare), then update the model for a specific conceptual task, far beyond mere “reading,” such as sentiment analysis and even predictive analysis.

You will learn

✓ Understanding of Transformers from scratch to BERT to GPT3
✓ Language Translations using Transformers in NLP
✓ Text Classification and Implementation of Chatbot in RASA and Spicy
✓ GPTs as Few Shot Learners & Multilingual NLP
✓ GPT 4- What to expect?
✓ 50+ NLP Coding Exercises with Coding Solutions
✓ Attention and Multi- Head Attention in NLP Transformers
✓ Implement a Transformer for an NLP based task/ activity
✓ Google Mum as multilingual unified platfrom

Requirements

• Basic Familiarity with the Natural Language Processing is recommended but not essential

This course is for

• Beginner students interested in learning NLP via Transformers

How much does the NLP Certification- BERT, GPTs, Software 3.0 & Multimodal NLP course cost? Is it worth it?

The course costs $14.99. And currently there is a 82% discount on the original price of the course, which was $84.99. So you save $70 if you enroll the course now.

Does the NLP Certification- BERT, GPTs, Software 3.0 & Multimodal NLP course have a money back guarantee or refund policy?

YES, NLP Certification- BERT, GPTs, Software 3.0 & Multimodal NLP has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the NLP Certification- BERT, GPTs, Software 3.0 & Multimodal NLP course, but there is a $70 discount from the original price ($84.99). So the current price is just $14.99.

Who is the instructor? Is Junaid Zafar a SCAM or a TRUSTED instructor?

Junaid Zafar has created 9 courses that got 405 reviews which are generally positive. Junaid Zafar has taught 42,686 students and received a 4.0 average review out of 405 reviews. Depending on the information available, Junaid Zafar is a TRUSTED instructor.
Dr. Engr. Junaid Zafar- Academician with 20 years experience
Prof. Dr. Engr. Junaid Zafar is currently working as Chairperson in Department of Electrical and Computer Engineering, Government College University, Lahore. He is also Director, Office of Research, innovation and Commercialization. He has completed his PhD in Electrical and Electronics Engineering, The University of Manchester University, UK, and BSc in Electrical Engineering from U.E.T Lahore. He is Academic visitor to the University of Cambridge, UK, MMU, UK and National University of Ireland. He remained Dual Degree programme coordinator at the Lancaster University, UK. Dr. Engr. Junaid Zafar received Roll of Honors for National Education Commission and Outstanding Teacher/ Researcher Awards from the Higher Education Commission, Pakistan. He is leading the macine learning and Artificial Intelligence centre with GC University, Lahore. He is member of Universal Association of Electronics & Computer Engineers, International Association of Computer Science & Information, and member of International Association of Engineers, IAENG Society of Artificial Intelligence, IAENG Society of Electrical Engineering, Science & Engineering Institute, IAENG Society of Imaging Engineering, Institute of Research Engineers & Doctors, and IAENG Society of Wireless Networks. He is member of editorial board in Journal of Future Technologies & Communications, Technical Programme committee, Frontiers of Information & Technologies, and Technical Programme Committee, Multi- Conference on Sciences & Technology. He is also serving as reviewer for IEEE Transactions on Microwave Theory & Techniques, IEEE Transactions on Antennas, IEEE Antenna & Wireless Propagation Letters, IEEE Transactions on Plasma Science, IEEE Transactions on Magnetics, International Journal of Electronics, and IET Antennas & Radio- wave Propagation. He has so far taught over twenty diffrent online courses based on outcome based student oriented models. He has also supervised more than 100 Masters/ MPhil thesis. He has published over 50 high impact factor publications and presented his work at several national and international renowned platforms.

8.8

CourseMarks Score®

10.0

Freshness

6.6

Feedback

9.2

Content

Platform: Udemy
Video: 2h 23m
Language: English
Next start: On Demand

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