Coursera MOOC / Non-credit 0

Advanced Tokenization and Sentiment Analysis

Name: Advanced Tokenization and Sentiment Analysis
Price: 49 USD
Availability: InStock

About this course

This course offers a clear pathway to undertsand advanced tokenization and sentiment analysis—two core pillars of modern NLP. You'll learn how to convert raw text into structured input using subword, character-level, and adaptive tokenization techniques, and how to extract sentiment using rule-based, statistical, and deep learning models. Through hands-on exercises, you’ll gain the skills to handle complex language input, model sentiment at fine granularity, and deploy systems that generalize across domains and languages. By the end of this course, you will be able to: - Explain and apply advanced tokenization techniques, including BPE, character-level, and streaming methods - Handle out-of-vocabulary terms and domain-specific language using adaptive and hybrid encoding strategies - Build sentiment analysis models using VADER, Naïve Bayes, BERT, and RoBERTa - Address challenges such as class imbalance, multilingual variation, and aspect-level sentiment - Evaluate sentiment systems using semantic similarity, temporal trends, and domain-specific metrics This course is ideal for NLP practitioners, data scientists, developers, and applied researchers aiming to build robust, ethical, and production-ready sentiment analysis systems. A basic understanding of Python, NLP fundamentals, and machine learning is recommended. Join us to learn how tokenization and sentiment analysis power the next generation of intelligent language technologies.

68/100

CourseAsk score

What the provider tells you: 32/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

Explain and apply advanced tokenization techniques
Handle out-of-vocabulary terms using adaptive encoding strategies
Build sentiment analysis models using VADER, Naïve Bayes, BERT, and RoBERTa
Address challenges like class imbalance and multilingual variation
Evaluate sentiment systems using domain-specific metrics

Machine Learning Artificial Intelligence #deep learning #machine learning #nlp #sentiment analysis #tokenization #bert #naive bayes #language processing #semantic similarity #vader #roberta #out-of-vocabulary #adaptive encoding #class imbalance #multilingual

$49.00

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