- Instructor: admin
- Duration: 3 hours
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.
12 hours ,3 hours per week.
All Zoom lectures are monitored by trained staff.
Paul Starret
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).
Students will be awarded different certifications in each batch.
Adults
• 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
500$
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