Who should attend
Writers. Those who write content for websites, blogs, and documentation.
Digital Marketing Professionals. Those who write website content optimized for Search Engine Optimization (SEO) will find the course beneficial. The participants can analyze social media chatter to measure customers' sentiments regarding products/services and the corporation’s image.
Language Translators. Those involved in translation services from one language to another are used heavily in the legal profession and industry for corporate documentation.
Software and Hardware Professionals. Those currently working in the field of robotics and personal assistant appliances will find the course content to be relevant.
About the course
Natural Language Processing is one of the next frontiers in Machine Learning. Learn how to get started NLP in this online course, part of Caltech’s seminar-style learning labs for advanced analytics.
Natural Language Processing (NLP) has many applications in a broad range of industries. For example, it can create or improve written text used on websites and blogs. NLP analyzes social media posts to measure positive or negative sentiments, which helps understand customers or potential threats. Personal assistant software like Apple’s Siri, Google Assistant, Amazon’s Alexa, and robots use NLP text and audio elements to understand the user’s intent and respond appropriately. The main goal of NLP is to understand the text’s meaning.
There are currently two different approaches to NLP. The first one is the analysis of words, sentences, and the text's semantics using TextBlob, NLTK, and spaCy software packages. The other approach to NLP is to employ Machine Learning (ML) to analyze the text. Google Cloud Platform (GCP) and IBM Watson provide an ML API (Application Programming Interface) to analyze Natural Languages and provide translation service between languages.
Through hands-on activities, you will cover fundamental mathematical analysis of language. A significant focus will be to analyze the polarity, subjectivity, and sentiments of text using the concepts of n-grams found in software products currently in the public domain. Next, we will understand concepts of Stemming and Lemmatization. Finally, you will explore entity recognition and similarity detection concepts. Participants will investigate popular services such as Google Cloud Platform and IBM Watson, among others.
If you complete this course and either the Machine Learning for Advanced Analytics course or the Deep Learning with TensorFlow course, you are eligible to receive the Caltech CTME Data Analytics Certificate.
You will learn how to build competency in:
- Analysis of text to understand the meaning of the text
- Software: Python + TextBlob + Natural Language Tool Kit + spaCy + Pattern
- Analysis of Words + Sentences + Semantics + Polarity + Subjectivity
- Machine Learning models for text processing
- Naïve Bayes + Decision Trees models for text processing
- Deep Learning Neural Networks for text processing
- Translation Services using Deep Learning Neural Networks
- Speech-to-text Services using Deep Learning Neural Networks
- Cloud Services for Machine Learning
- Google Cloud Platform (GCP) for Natural Language API
- IBM Watson services for Natural Language
- GCP: Entity Identification + Syntax Analysis + Documentation Classification + Sentiment Analysis
- Social Media (Twitter) data analysis for customer sentiment analysis
- Text-based customer feedback data analysis
- Language Detection + Translation
- Inflection: Pluralization + Singularization
- Normalization: Stemming + Lemmatization
- Semantics using nGrams
- Entity Recognition: spaCy
- Similarity Detection: spaCy
Ash Pahwa, Ph.D., is an educator, author, entrepreneur, and technology visionary with three decades of industry and academic experience. He has founded several successful technology companies during his career, the latest of which is A+ Web Services. His expertise includes search engine optimizat...
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