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Course Outline

Introduction to Predictive Analytics

  • Overview of predictive analytics.
  • The role of LLMs in predictive modelling.
  • Case studies: Successful predictive analytics projects.

Fundamentals of Large Language Models

  • Understanding LLM architecture.
  • Training and fine-tuning LLMs.
  • LLMs versus traditional statistical models.

Data Preparation and Processing

  • Data collection and cleaning.
  • Feature engineering for predictive modelling.
  • Utilising LLMs for data enrichment.

Building Predictive Models with LLMs

  • Selecting the appropriate LLM for your data.
  • Training LLMs for predictive tasks.
  • Evaluating model performance.

Advanced Techniques in Predictive Analytics

  • Time series forecasting with LLMs.
  • Sentiment analysis for market prediction.
  • Anomaly detection in large datasets.

Integrating LLMs into Business Processes

  • Deploying LLMs for real-time predictions.
  • Monitoring and maintaining predictive models.
  • Ethical considerations in predictive analytics.

Hands-on Lab: Predictive Analytics Project

  • Defining project objectives.
  • Implementing a predictive model with LLMs.
  • Analysing results and iterating on the model.

Summary and Next Steps

Requirements

  • A solid understanding of core machine learning concepts.
  • Proficiency in Python programming.
  • Familiarity with data analysis and visualisation tools.

Who Should Attend

  • Data scientists.
  • Business analysts.
  • IT professionals aiming to understand LLM applications in analytics.
 14 Hours

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Provisional Upcoming Courses (Require 5+ participants)

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