Programme Parameters
Advanced Postgrad
8 Weeks (80 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This self-paced program provides an in-depth exploration of AI and ChatGPT applications in the legal field, focusing on advanced legal research tools, litigation support, and case management automation. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This self-paced program provides an in-depth exploration of AI and ChatGPT applications in the legal field, focusing on advanced legal research tools, litigation support, and case management automation.
📋 Course Objectives
1. Gain working command of litigation support.
2. Translate biotechnology theory into practical, reproducible analysis.
3. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into litigation support
🚀 Key Learning Outcomes
• Confidence to reason about litigation support in real projects.
• Tangible, reproducible biotechnology work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Managing a Matter With Analytics
• Matter planning, budgeting and resource allocation
• Litigation analytics on judges, courts and outcomes, and its predictive limits
• Settlement valuation informed by comparable outcomes
Document Review at Scale
• Technology-assisted review and predictive coding workflows
• Recall and precision targets and defensibility of the process
• Privilege review and the consequences of inadvertent disclosure
Building the Factual Record
• Chronology and issue mapping across large document sets
• Deposition and transcript analysis
• Authenticity and admissibility challenges to digital evidence
Pleadings and Submissions
• Drafting assistance with mandatory citation verification
• Consistency checking across lengthy submissions and exhibits
• Court expectations and disclosure duties regarding AI assistance
Running the Practice
• Matter data, knowledge management and precedent reuse
• Client reporting and transparency about tooling
• Risk register for AI use in contentious work
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
Frequently Asked Questions
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