Mastering Natural Language Processing (NLP) with Deep Learning from Pluralsight

Dive into text data analysis, preprocessing, word embeddings, sequence models and transformers. This course will teach you how to use the power of NLP with deep learning techniques.

Understanding the complex world of natural language processing can be a challenging but essential task in this rapidly growing industry.

In this course, Mastering Natural Language Processing (NLP) with Deep Learning, you’ll gain the ability to analyze text data using advanced deep learning techniques.

First, you’ll explore the essentials of NLP, including the unique challenges of text data and how to preprocess it effectively for various NLP tasks.

Next, you’ll discover the concept of neural networks and their applications in NLP, learning about sequence models and recurrent neural networks (RNNs).

Finally, you’ll learn how to implement and fine-tune transformers such as BERT and GPT for real-world NLP applications.

When you’re finished with this course, you’ll have the skills and knowledge of NLP with deep learning needed to solve complex language processing tasks and build innovative AI solutions.

What's inside

Syllabus

Course Overview

Natural Language Processing (NLP) Essentials: Exploring Text Data

NLP Architectures, Sequence Models, and Transformers

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers fundamentals of text data analysis, which is the cornerstone of NLP tasks

Helps students master preprocessing techniques, the essential step before training NLP models

Demonstrates the use of word embeddings, an advanced NLP technique that captures the semantic meaning of words

Teaches students how to apply sequence models and transformers, core architectures in NLP

Provides hands-on experience with implementing and fine-tuning transformers, empowering students with practical NLP skills

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 Mastering Natural Language Processing (NLP) with Deep Learning with these activities:

Refresh NLP Fundamentals

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Review foundational NLP concepts to ensure you have a solid understanding of the subject matter before starting the course.

Browse courses on Text Data Analysis

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Review key NLP concepts such as tokenization, stemming, and lemmatization.
Gain familiarity with common NLP toolkits and libraries.

Review 'Natural Language Processing with Python'

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Enhance your understanding of NLP concepts and techniques by reading a foundational book on the subject.

View Natural Language Processing with Python:... on Amazon

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Go through relevant chapters to reinforce your learning.

Practice Text Preprocessing Techniques

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Engage in hands-on exercises to master text preprocessing techniques and data cleaning strategies.

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Apply tokenization, stemming, and lemmatization to real-world text data.
Practice removing stop words and rare words for effective text representation.

Four other activities

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Explore Sequence Models for NLP

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Delve into the concepts and applications of sequence models, particularly RNNs, in NLP.

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Understand the architecture and training principles of RNNs.
Learn about different types of RNNs and their use cases in NLP.

Fine-Tune Transformer Models

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Gain hands-on experience in fine-tuning pre-trained transformer models for NLP tasks.

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Select an appropriate transformer model for your task.
Prepare your dataset and fine-tune the model using specific parameters.
Evaluate the performance of the fine-tuned model and make adjustments as needed.

Build a Basic NLP Chatbot

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Gain practical experience by building a simple chatbot using NLP techniques.

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Design the chatbot's conversational flow.
Train the chatbot on a relevant dataset.
Evaluate the chatbot's performance and make improvements.

Develop an NLP-Based Text Summarization System

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Apply your NLP knowledge to build a practical system that can summarize text effectively.

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Design the architecture of the summarization system.
Train the system using a suitable dataset.
Evaluate the performance of the system and refine it for optimal results.

Career center

Learners who complete Mastering Natural Language Processing (NLP) with Deep Learning will develop knowledge and skills that may be useful to these careers:

NLP Engineer

With a focus on building and maintaining NLP systems for various applications, NLP Engineers are responsible for the entire NLP pipeline, from data collection and preprocessing to model development and deployment. A course like Mastering Natural Language Processing with Deep Learning can provide NLP Engineers with in-depth knowledge and practical experience in the field.

See salaries and explore the career path for NLP Engineer

NLP Researcher

NLP Researchers conduct research in the field of natural language processing, developing new algorithms and techniques to improve the accuracy and efficiency of NLP systems. A course on Mastering Natural Language Processing with Deep Learning provides NLP Researchers with a solid foundation in the latest advancements in the field, allowing them to contribute to the development of cutting-edge NLP technologies.

See salaries and explore the career path for NLP Researcher

Natural Language Processing Scientist

NLP Scientists conduct research and develop new methods and techniques for natural language processing. A course like Mastering Natural Language Processing with Deep Learning can provide NLP Scientists with the knowledge and skills to conduct cutting-edge research in the field.

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Text Mining Engineer

Text Mining Engineers use natural language processing techniques to extract useful information from unstructured text data. A course on Mastering Natural Language Processing with Deep Learning can provide Text Mining Engineers with the skills to develop and apply NLP algorithms for various text mining tasks, such as text classification, sentiment analysis, and topic modeling.

See salaries and explore the career path for Text Mining Engineer

Computational Linguist

Computational Linguists study the computational aspects of human language, developing mathematical models and algorithms to understand and process natural language. A course on Mastering Natural Language Processing with Deep Learning can provide Computational Linguists with a solid foundation in the latest NLP techniques, enabling them to contribute to the development of new language technologies.

See salaries and explore the career path for Computational Linguist

Information Retrieval Engineer

Information Retrieval Engineers design and implement systems for searching and retrieving information from large collections of data. As NLP techniques are increasingly used in search engines and other information retrieval systems, a course like Mastering Natural Language Processing with Deep Learning can provide Information Retrieval Engineers with the necessary skills to develop and improve these systems.

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Technical Writer

Technical Writers create and maintain technical documentation for software, hardware, and other products. As NLP techniques are increasingly used to improve the quality and accessibility of technical documentation, a course on Mastering Natural Language Processing with Deep Learning can be beneficial for Technical Writers who want to specialize in NLP-based documentation.

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Knowledge Engineer

Knowledge Engineers design and build knowledge-based systems that can reason and solve problems. A course on Mastering Natural Language Processing with Deep Learning can provide Knowledge Engineers with the skills to develop NLP-based knowledge-based systems that can understand and process natural language.

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Linguistic Data Analyst

Linguistic Data Analysts collect, analyze, and interpret linguistic data to gain insights into language patterns and usage. A course on Mastering Natural Language Processing with Deep Learning can provide Linguistic Data Analysts with the computational skills to analyze large volumes of text data and extract meaningful insights.

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Information Architect

Information Architects design and organize information systems to ensure that they are easy to find and use. A course on Mastering Natural Language Processing with Deep Learning can provide Information Architects with the skills to design and implement NLP-based information retrieval systems.

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Machine Learning Engineer

Machine Learning Engineers research, design, build, test, deploy, monitor, and maintain machine learning models. As NLP becomes ubiquitous in a wide array of software and services, Machine Learning Engineers can build a foundation for a specialization in NLP with a course like Mastering Natural Language Processing with Deep Learning.

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Product Manager

Product Managers are responsible for planning, developing, and launching new products or features. With the growing adoption of NLP in product development, a course on Mastering Natural Language Processing with Deep Learning can be beneficial for Product Managers who want to incorporate NLP-based features into their products.

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Software Developer

Software Developers design, develop, test, and maintain software applications. Given the increasing use of NLP in software development, a course on Mastering Natural Language Processing with Deep Learning can be beneficial for Software Developers who want to specialize in building NLP-based applications.

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Business Analyst

Business Analysts use data and analysis to identify and solve business problems. A course on Mastering Natural Language Processing with Deep Learning can provide Business Analysts with the skills to analyze text data and extract insights that can help businesses make better decisions.

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Data Scientist

For individuals interested in using code to find solutions to complex business problems, Data Scientists build and implement predictive models for tasks such as customer segmentation, fraud detection, and forecasting. Given the prominence of natural language processing in various domains, a course on Mastering Natural Language Processing with Deep Learning may be useful for a Data Scientist who wants to specialize in analyzing textual data or developing NLP-based solutions.

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