LLMs for Predictive Analytics

• An understanding of basic machine learning concepts
• Experience with Python programming
• Familiarity with data analysis and visualization tools

Audience

• Data scientists
• Business analysts
• IT professionals seeking to understand LLM applications in analytics

Predictive analytics is the practice of extracting information from existing data sets to determine patterns and predict future outcomes and trends.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists and business analysts who wish to utilize large language models (LLMs) to forecast trends and behaviors in various industries.

By the end of this training, participants will be able to:

• Understand the fundamentals of LLMs and their role in predictive analytics.
• Implement LLMs to analyze and forecast data in various industries.
• Evaluate the effectiveness of predictive models using LLMs.
• Integrate LLMs with existing data processing pipelines.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Predictive Analytics

• Overview of predictive analytics
• Role of LLMs in predictive modeling
• Case studies: Successful predictive analytics projects

Fundamentals of Large Language Models

• Understanding the architecture of LLMs
• Training and fine-tuning LLMs
• LLMs vs. traditional statistical models

Data Preparation and Processing

• Data collection and cleaning
• Feature engineering for predictive modeling
• Using LLMs for data enrichment

Building Predictive Models with LLMs

• Selecting the right 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
• Analyzing results and iterating on the model

Summary and Next Steps