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

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

Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re 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’s 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’ll do it all using Google’s Colab free, browser-based notebook environment that runs completely in the cloud. It’s a game-changing interface that will save you time and supercharge your data science toolkit.

If you’ve 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 ‘Enroll Now’ button and see you inside.

Enroll now

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides a comprehensive study of one aspect of science, math, and technology
Taught by Hadelin de Ponteves, who is an AI expert
Takes a creative approach to an otherwise established topic, field, or subject
Offers hands-on labs and interactive materials
Belongs to series of other courses, as this may indicate comprehensiveness and detail
This course explicitly requires learners to come in with extensive background knowledge first

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Natural Language Processing (NLP) with BERT with these activities:
Review Introduction to Machine Learning
Recall the foundational concepts of machine learning, including supervised learning, to strengthen your understanding of BERT and its role in NLP.
Show steps
  • Revise the basic principles of ML, such as supervised learning algorithms
  • Review examples of ML applications in NLP
Review Python Programming Concepts
Go over the fundamental principles of Python programming to strengthen your understanding of syntax, data structures, and object-oriented programming concepts, which are essential for BERT implementation.
Browse courses on Python Basics
Show steps
  • Revise the fundamentals of Python Syntax (variables, data types, control flow)
  • Review the concepts of Python data structures (lists, tuples, dictionaries)
  • Explore Object-oriented programming principles (classes, objects, inheritance)
Read 'Natural Language Processing with PyTorch'
Gain a deeper understanding of NLP concepts and techniques by reading this book, which provides a comprehensive overview of NLP with PyTorch.
Show steps
  • Read chapters on NLP fundamentals and PyTorch basics
  • Study chapters on advanced NLP techniques, such as BERT
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice BERT coding exercises
Reinforce your understanding of BERT and Python coding through hands-on exercises
Browse courses on BERT
Show steps
  • Find and download a set of coding exercises on BERT
  • Work through the exercises, following the instructions carefully
  • Test your solutions against the provided test cases
  • Debug any errors or issues you encounter
Create a BERT research compilation
Develop a collection of resources, tools, and materials for the course
Browse courses on BERT
Show steps
  • Start with a literature review to identify key references on BERT
  • Compile a list of online resources, such as tutorials, articles, and videos
  • Gather examples of code and scripts that demonstrate the use of BERT
  • Organize and document your compilation in a structured format

Career center

Learners who complete Natural Language Processing (NLP) with BERT will develop knowledge and skills that may be useful to these careers:
Natural Language Processing Engineer
An NLP Engineer focuses on building and deploying NLP models. This course will help you build a solid foundation in BERT, one of the latest and most powerful NLP models. Building a solid foundation in BERT will allow you to build more sophisticated and precise models to carry out a wide variety of NLP tasks.
Data Scientist
Data Scientists leverage knowledge of computer science, math, and statistics to extract insights from data and solve problems. As such, a foundation in Natural Language Processing (NLP) is essential to allow for the extraction of meaning from text data to prepare it for modeling. This course will provide a solid foundation in BERT, one of the latest and most powerful NLP models, for use by Data Scientists.
Machine Learning Engineer
A Machine Learning Engineer designs, builds, deploys, and maintains machine learning models. As NLP is a subfield of machine learning engineers, this course will enhance your skillset by providing a solid foundation in BERT, one of the latest and most powerful NLP models. Having a solid foundation in BERT will help you build more sophisticated and precise models to carry out a wide variety of NLP tasks.
Data Analyst
Data Analysts use data to find trends and patterns to solve business problems. NLP is essential for data analysts who work with text data. This course will teach you how to perform semantic analysis on text data, which is a critical skill for data analysts.
Business Analyst
Business Analysts help businesses understand their needs and develop solutions to improve their operations. NLP can help Business Analysts understand customer feedback, analyze market trends, and identify new business opportunities. This course will provide you with a solid foundation in NLP, which can help you be more successful in your role.
Customer Success Manager
Customer Success Managers help customers achieve their goals with a product or service. NLP can help Customer Success Managers understand customer feedback, identify customer needs, and develop solutions to improve customer satisfaction. This course will provide you with a solid foundation in NLP, which can help you be more successful in your role.
Product Manager
Product Managers oversee the development and launch of new products. They need to have a deep understanding of the customer and the market. NLP can help Product Managers understand customer feedback and identify new product opportunities. This course will provide you with a solid foundation in NLP, which can help you be more successful in your role.
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. NLP can help Marketing Managers understand customer feedback, analyze market trends, and identify new marketing opportunities. This course will provide you with a solid foundation in NLP, which can help you be more successful in your role.
Sales Manager
Sales Managers lead and motivate sales teams to achieve sales targets. NLP can help Sales Managers understand customer feedback, analyze market trends, and identify new sales opportunities. This course will provide you with a solid foundation in NLP, which can help you be more successful in your role.
Content Writer
Content Writers create and edit written content for websites, blogs, and other marketing materials. NLP can help Content Writers understand their audience, identify keywords, and write engaging content. This course will provide you with a solid foundation in NLP, which can help you be more successful in your role.
Project Manager
Project Managers plan, execute, and close projects. NLP can help Project Managers understand customer requirements, identify project risks, and develop project plans. This course will provide you with a solid foundation in NLP, which can help you be more successful in your role.
UX Designer
UX Designers design and evaluate user interfaces. NLP can help UX Designers understand user needs, identify usability issues, and develop user-centered designs. This course will provide you with a solid foundation in NLP, which can help you be more successful in your role.
Recruiter
Recruiters find and hire candidates for open positions. NLP can help Recruiters screen resumes, identify qualified candidates, and improve the hiring process. This course will provide you with a solid foundation in NLP, which can help you be more successful in your role.
Operations Manager
Operations Managers oversee the day-to-day operations of a business. NLP can help Operations Managers improve efficiency, reduce costs, and improve customer satisfaction. This course will provide you with a solid foundation in NLP, which can help you be more successful in your role.
Software Engineer
Software Engineers apply the principles of computer science to design, develop, deploy, and maintain software systems. As such, because this course will teach you how to perform semantic analysis and build and train NLP models, it may be useful in giving you an edge over other candidates during the hiring process.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Natural Language Processing (NLP) with BERT.
Provides a comprehensive overview of deep learning for natural language processing. It covers a wide range of topics, including word embeddings, recurrent neural networks, and transformers. It valuable resource for anyone who wants to learn more about deep learning for NLP.
Provides a comprehensive overview of statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about statistical learning.
Provides a practical introduction to natural language processing. It covers a wide range of topics, including tokenization, stemming, parsing, and machine learning. It valuable resource for anyone who wants to learn more about NLP and how to use it for real-world tasks.
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including deep learning models, deep learning algorithms, and deep learning applications. It valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of speech and language processing. It covers a wide range of topics, including phonetics, phonology, morphology, syntax, and semantics. It valuable resource for anyone who wants to learn more about speech and language processing.
Provides a comprehensive overview of reinforcement learning. It covers a wide range of topics, including reinforcement learning models, reinforcement learning algorithms, and reinforcement learning applications. It valuable resource for anyone who wants to learn more about reinforcement learning.
Provides a comprehensive overview of computer vision. It covers a wide range of topics, including computer vision models, computer vision algorithms, and computer vision applications. It valuable resource for anyone who wants to learn more about computer vision.
Provides a comprehensive overview of natural language processing with Python. It covers a wide range of topics, including natural language processing models, natural language processing algorithms, and natural language processing applications. It valuable resource for anyone who wants to learn more about natural language processing with Python.
Provides a comprehensive overview of natural language generation. It covers a wide range of topics, including natural language generation models, natural language generation algorithms, and natural language generation applications. It valuable resource for anyone who wants to learn more about natural language generation.

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