Track
Natural Language Processing in Python
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Natural Language Processing in Python
Prerequisites
There are no prerequisites for this trackCourse
Master text analysis with essential NLP techniques from preprocessing to advanced transformer models.
Course
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Course
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Course
Learn how to load, transform, and transcribe speech from raw audio files in Python.
Project
Transcribe customer support audio calls, evaluate sentiment, search in text and identify common entities to improve customer service!
Course
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
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Enroll NowFAQs
Is this Track suitable for beginners?
Yes, this Natural Language Processing track is suitable for beginners. It covers foundational concepts related to NLP like identifying words and extracting topics, building chatbots, feature engineering, sentiment analysis and spoken language processing. All these concepts are covered in easy-to-understand courses that use simple Python examples.
What is the programming language of this Track?
This Track uses Python as the programming language.
Which jobs will benefit from this Track?
This Track will benefit professionals looking to develop core Natural Language Processing (NLP) skills. This track will be useful to professionals pursuing jobs in linguistics, natural language processing, deep learning, artificial intelligence, big data and software engineering.
How will this Track prepare me for my career?
This Track will help you learn and practically implement natural language processing techniques for analyzing and manipulating data using Python programming language. You will be able to apply this knowledge to job opportunities related to linguistics, natural language processing, deep learning, artificial intelligence, big data and software engineering.
How long does it take to complete this Track?
The track is self-paced, so users can spend as long or as little time as they like working through exercises and courses. However, Tracks usually take 25 hours to complete.
What's the difference between a skill track and a career track?
Skill tracks are designed to give users a broad understanding of certain topics and encourage self-paced learning, whereas career tracks focus on practical problem solving and help users acquire job-specific skills.
What datasets will I be exposed to?
The datasets used in this track are real-world sources including Wikipedia articles, online review sites, and data from a flight booking system.
What skills will I have mastered by the end of the track?
By the end of this track, you will have learned how to identify words and extract topics in text, build your own chatbot, work with popular Python NLP libraries, use natural language processing techniques to transcribe audio files, and extract insights from real-world data sources.
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