NLP for Non-Coders Mini Bootcamp

In this bootcamp, we will cover how natural language processing (NLP) provides a foundation from which clustering, machine learning, classification, search engineering and graph databases make efficient sense of large volumes of data. We will bring together these various areas into a real world example to demonstrate the resulting synergy. We will define unstructured and structured data and how they form a holistic foundation from which to understand large amounts of complex data.

We will then learn how unstructured text data is structured so that it can be analyzed and assimilated with structured data. We will then learn how collections of documents can be summarized, clustered and classified for a deeper, high-level understanding of the documents and concepts embedded in them. Next, we will explore search engineering which allows efficient exploration of collections of documents. Finally, we will see how graph databases can help bring diverse text data into a coherent linking of the data collection where visualization is leveraged to help glean insights.

Who is this course for:

• ● Anyone who is interested in entering the world of NLP and do not know where to start
• ● Anyone who wants an introduction to ML/Deep Learning and how to apply it to NLP
• ● Anyone who is interested in building NLP applications in Python
• ● Anyone who wants to understand how commonly used NLP applications are built

Course Requirements:

• ● Having an interest in learning Natural Language Processing and data science is enough to take this course.
• ● This course has no prior coding experience requirement.

• ● Working with Test Data in Python
• ● Regular Expressions
• ● Text processing with NLTK and Spacy
• ● POS Tagging
• ● NER Tagging
• ● Text Normalization
• ● Stemming
• ● Lemmatization
• ● Topic Modeling
• ● Interpreting Patterns
• ● LDA
• ● LSA (Latent Semantic Analysis)
• ● Feature Engineering for Text
• ● Bag of Words
• ● TF-IDF
• ● SVD
• ● Word Embedding
• ● Identify Topics in Text
• ● Text Classification
• ● Deep Learning for NLP
• ● Project
• ● Auto. Tagging
• ● Article Categorize
• ● Social Media Info. Extract
• ● Spam Classification

• 
• Start learning the NLP for Non-Coders with outside of business hours schedule!
• The 12 hours of schedule is as follows:
• March 17 – 24 – 31 and April 7
• Tuesdays, from 6:30 pm to 9:30 pm

The venue for the bootcamp is Magnimind Academy Sunnyvale Campus: 830 Stewart Dr #182, Sunnyvale, CA 94085. The capacity is limited to 20 people.

NLP for Non-Coders Mini Bootcamp is now also available online. Anyone who wants to attend this mini bootcamp can join online live webinars where the same course content will be taught. Online sessions will be distributed through zoom conferences. Students will have access to the screen of the instructor, external camera showing class atmosphere, whiteboard, and be able to ask questions through chat. You may attend this mini bootcamp no matter where you are.

Tuition fee

NLP for Non-Coders Mini Bootcamp has a $300 tuition fee.

For the “Early Bird” applicants (January 15 – March 10), the tuition fee is $250.

Payment process

After you finish filling your application form, the website will direct you to the payment page. There, you can select available payment options.

Cancellation

If you’re not satisfied with the course you may cancel your application.

The application process starts at magnimindacademy.com. You can view the course pages and learn more about your intended course. You can apply by clicking the “Buy now” button and then fill out the application form.

Paul Starrett is a licensed private investigator (CA, IL) and attorney (CA) specializing in high-profile investigations and legal consulting especially where electronic data is central. He is founder and CEO of Starrett Consulting, Inc., a full-service investigations firm that leverages API’s from open-source and commercial data-science applications to analyze structured and unstructured data. He is former General Counsel and Chief Global Risk Officer of an international, publicly-held data management corporation heading their global legal, operations and risk-management groups. His 25 year career began in law enforcement and corporate security and later progressed into information-security engineering, electronic discovery and information management. Paul’s education includes a Master of Science in Predictive Analytics from Northwestern University and a Master of Laws (LL.M.) in Taxation from Golden Gate University. He is also a Certified Fraud Examiner (CFE) and EnCase Certified Computer Forensics Examiner (EnCE).