Course Content
Module 1: Current Challenges in eDiscovery
Traditional eDiscovery Pain Points The Data Deluge: Volume, Velocity, Variety Cost and Time Implications Human Error and Inconsistency
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Module 2: How AI Supports Document Review
Introduction to AI in Legal Machine Learning Fundamentals for eDiscovery Natural Language Processing (NLP) in Document Review AI-Powered Document Classification and Tagging
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Module 3: Predictive Coding & Clustering
Understanding Predictive Coding (Technology Assisted Review - TAR) Active Learning Workflows Clustering for Conceptual Grouping Sampling and Validation Techniques
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Module 4: Producing with Precision: Bates Stamping & Redaction
Automated Bates Stamping with AI AI-Assisted Redaction for PII/PHI Quality Control and Verification of AI Outputs
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Module 5: Integrating with LegalGPTs eDiscovery Tools
Overview of LegalGPTs Platform Hands-on with LegalGPTs AI Features Workflow Integration Strategies Case Studies: Success Stories with LegalGPTs
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Module 6: Cost Savings and Compliance Considerations
Quantifying ROI of AI in eDiscovery Ethical Considerations of AI in Legal Practice Data Security and Privacy with AI Tools Regulatory Compliance and Best Practices
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AI for eDiscovery: From Collection to Production

Natural Language Processing (NLP) is a branch of AI that focuses on enabling computers to understand, interpret, and generate human language. NLP is crucial for eDiscovery as it allows AI systems to analyze the textual content of documents. Applications of NLP in document review include:

Text Classification: Categorizing documents based on their content (e.g., contracts, emails, pleadings).
Named Entity Recognition (NER): Identifying and extracting specific entities like names, organizations, dates, and locations from text.
Sentiment Analysis: Determining the emotional tone or sentiment expressed in a document.
Topic Modeling: Discovering abstract topics within a collection of documents.