Natural Language Processing (NLP)

First, we will cover the fundamental mathematical analysis of NLP. You will write Python code to access NLTK, TextBlob, and spaCy software packages. Next, You will learn how text can be tokenized using regular expressions, NLTK, and TextBlob. You will learn how text tokens are converted into vectors and how the vectorization process, including count vectorizer, cosine similarity computation, and TF-IDF (Term Frequency Inverse Document Frequency), is used. Finally, we will cover how text semantics can be analyzed using Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).

In the second approach, you will explore the Machine/Deep Learning models for NLP. First, we will create Naïve Bayes machine learning models for document classification. Learn how Deep learning tools can generate Word Embeddings like Word2Vec. Next, you will learn how to employ transformers, including GPT and BERT, for semantic analysis of text.

Through hands-on activities, you will cover fundamental mathematical analysis of language. Build your critical understanding of popular services such as Google Cloud Platform and IBM Watson, among others, for Natural Language Processing.

By completing this short course and either the Machine Learning for Advanced Analytics course or the Deep Learning with TensorFlow course, you will be eligible to receive the Caltech CTME Machine Learning for Advanced Analytics Certificate.

*This written course description was enhanced by NLP functionality.

**The Benefits and bullets remain as is below

Benefits

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

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.

Schedule

Classes are held in a Hybrid environment. This course now runs every Saturday from 8:00am to 12:00pm PT at the Caltech campus in Pasadena, CA. Since this class is now hybrid, participants also have the option of attending this class remotely via Zoom. There are 4 hours of instruction every Saturday for 6 Days, which makes a total of 24 instruction hours. During the week before each session you will receive emails containing location-specific instructions.

Fee: $2130 for the program. Payment is due (or credit card charged) on the Friday prior to first day of the course. You may pay the entire program fee of $2130 in one payment, or you may pay the program fee in 3 increments of $710.

Hours and Credits: Participants will earn 2.4 Continuing Education Units (CEUs).

Grading: These programs are based on a Pass/Fail grading system.

Instructor

Ash Pahwa, PhD