LLMs for Predictive Analytics Training Course
Predictive analytics involves the extraction of information from existing data sets to identify patterns and forecast future outcomes and trends.
This instructor-led, live training (available online or onsite) is designed for intermediate-level data scientists and business analysts who want to leverage large language models (LLMs) to predict trends and behaviors across different industries.
By the end of this training, participants will be able to:
- Grasp the fundamentals of LLMs and their significance in predictive analytics.
- Apply LLMs to analyze and forecast data in various sectors.
- Assess the effectiveness of predictive models using LLMs.
- Integrate LLMs into existing data processing pipelines.
Course Format
- Interactive lecture and discussion sessions.
- Plenty of exercises and practical activities.
- Hands-on implementation in a live-lab environment.
Customization Options for the Course
- To request a customized training session for this course, please contact us to arrange.
Course Outline
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
Requirements
- 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
Open Training Courses require 5+ participants.
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