Natural Language Processing: NLP With Transformers in Python from Udemy

Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.

In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR.

We cover several key NLP frameworks including:

HuggingFace's Transformers
TensorFlow 2
PyTorch
spaCy
NLTK
Flair

And learn how to apply transformers to some of the most popular NLP use-cases:

Language classification/sentiment analysis
Named entity recognition (NER)
Question and Answering
Similarity/comparative learning

Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.

All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:

History of NLP and where transformers come from
Common preprocessing techniques for NLP
The theory behind transformers
How to fine-tune transformers

We cover all this and more, I look forward to seeing you in the course.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Examines transformer models, which are the backbone of NLP and machine learning

Introduces students to key NLP frameworks including HuggingFace's Transformers, TensorFlow, PyTorch, spaCy, NLTK, and Flair

Covers important NLP use cases such as language classification, sentiment analysis, named entity recognition, question answering, and similarity learning

Provides a comprehensive overview of the history of NLP and the evolution of transformer models

Includes practical projects and examples that demonstrate the application of transformer models in real-world NLP tasks

Taught by experienced instructors with a strong reputation in the field of NLP and machine learning

Reviews summary

Practical nlp with modern transformers

According to learners, this course is a comprehensive and highly practical guide to modern NLP using transformers. Many highlight the course's up-to-date content and its focus on hands-on projects, particularly the real-world application of transformer models like BERT and DPR. Students frequently praise the clarity of the instructor's explanations for complex topics and the effective coverage of key frameworks such as HuggingFace, TensorFlow, and PyTorch. While generally well-received, some mention the pace can be fast for beginners and that prior Python and ML knowledge is essential. Recent reviews indicate the instructor actively updates the content based on feedback, addressing earlier concerns like outdated libraries.

Generally clean code, but minor bugs occasionally noted.

"Code examples were clean and easy to follow."

"Good course overall, but a few minor bugs in the code that I had to debug myself."

"Nothing major, but can be frustrating for those less experienced with debugging."

"I appreciated the well-structured codebases provided for the projects."

Course content stays current with the latest NLP advancements.

"Instructor updated the course recently, fixing some library version issues mentioned in older reviews. It's now very current and relevant."

"This course is a goldmine for anyone serious about NLP, especially with its coverage of modern transformer models."

"The HuggingFace section was a lifesaver and shows how up-to-date the material is."

"Truly a comprehensive guide to transformers, keeping pace with the rapidly evolving field."

Complex concepts are explained clearly and effectively.

"The instructor explains complex concepts like attention mechanisms and fine-tuning very clearly."

"The theory behind transformers was explained well, making it accessible even for challenging topics."

"Instructor's explanations are generally clear and make it easy to follow along."

"I appreciated how well key frameworks were introduced and integrated into practical examples."

Strong emphasis on hands-on application and real-world projects.

"The hands-on projects, especially the open domain Q&A, were incredibly practical."

"The two full-size NLP projects really set this course apart. It’s not just theory; you actually build useful applications."

"I particularly enjoyed the real-world applications and how I can apply this knowledge immediately in my work."

"The emphasis on practical deployment is great, providing valuable skills for my career."

Some found the pace fast, desiring more depth in theory.

"Sometimes the pace was a bit fast, and I had to rewatch lectures."

"A bit rushed in some parts, especially the mathematical foundations."

"Could use more depth in certain advanced theoretical concepts, but for practical application, it's superb."

"I wish there were more exercises to practice concepts after the lectures."

Requires prior Python and machine learning background.

"Too advanced for me. I struggled with the pace and assumed knowledge. Not really for beginners."

"Prerequisites are definitely not for absolute beginners in Python or ML; come prepared."

"This course is solid for intermediate to advanced learners, but it might be challenging without a foundational understanding."

"I found myself needing to supplement with external resources when the mathematical foundations were rushed."

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Career center

Learners who complete Natural Language Processing: NLP With Transformers in Python will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

An NLP Engineer builds machine learning models that understand human language. With the increasing popularity of transformers in NLP, learning about them is essential for the role, as transformers are state of the art. This course covers the basics of transformers while also diving deep into their implementation and use, which can help you develop the skills and knowledge necessary for a successful career as an NLP Engineer. The course also covers preprocessing techniques and fine-tuning transformers, which are essential to your day-to-day work in NLP.

See salaries and explore the career path for Natural Language Processing Engineer

Machine Learning Engineer

An MLE develops and deploys machine learning models. Transformers are a key part of many of these models, making learning about their capabilities, implementation, and fine-tuning a key part of maximizing your productivity.

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Data Scientist

Similar to an MLE, a Data Scientist will develop and make use of machine learning models. Therefore, a deep understanding of transformers will help you achieve success as a Data Scientist.

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Speech Scientist

Speech Scientists work to improve the technology and accuracy of ASR and NLP. They work across a number of domains. Therefore, this course can be a major benefit in building your skillset as a Speech Scientist.

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Computational Linguist

A Computational Linguist works to build better computer interfaces for handling language. The course may also help you develop foundational knowledge in NLP, which will help you develop stronger overall models.

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Information Scientist

Information Scientists build and manage information in complex systems. This often includes the use of Natural Language Processing and various NLP models. Due to the increasing use of transformers in NLP pipelines, it may be helpful to learn more about them, which you can do via this course.

See salaries and explore the career path for Information Scientist

NLP Researcher

An NLP Researcher will work on the cutting edge of NLP to improve current methods. Learning about transformers will help you stay at the forefront of that research.

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Software Developer

A Software Developer who wishes to specialize in NLP will benefit greatly from learning about transformers. This course will provide a solid foundation in the subject, while also providing examples of using transformers for common NLP tasks.

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Product Manager

A Product Manager in NLP will benefit from learning about transformers. This course will help you better understand the capabilities and limitations of transformers, which will help you build better products.

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Technical Writer

A Technical Writer who specializes in NLP will need to understand transformers. This course will help you to clearly explain the concepts of transformers and their use in NLP systems to a non-technical audience.

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Content Strategist

A Content Strategist who focuses on NLP will benefit from knowledge of transformers. This course will help you understand how transformers can be used to create more effective and engaging content.

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Information Architect

An Information Architect who specializes in NLP will need to understand transformers. This course will help you to design and implement NLP systems that are effective and efficient.

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UX Designer

A UX Designer who specializes in NLP will benefit from learning about transformers. This course will help you to design NLP systems that are easy to use and understand.

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Business Analyst

A Business Analyst who specializes in NLP will need to understand transformers. This course will help you to understand the business value of NLP and how transformers can be used to achieve business goals.

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Technical Support Specialist

A Technical Support Specialist who supports NLP systems will benefit from learning about transformers. This course will help you to troubleshoot and resolve problems with NLP systems.

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