Course Catalog  > Data Science, AI, and ML  > AI and Generative AI  > AI for Data Scientists
WA3515

Introduction to Generative AI Engineering for Data Scientists and ML Engineers Training

This Introduction to Generative AI (GenAI) training is tailored for Machine Learning (ML) and Data Science professionals who want to gain a practical understanding of Generative AI and large language models (LLMs).

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Course Details

Duration

3 days

Prerequisites

  • Practical experience (6+ months) minimum in Python - functions, loops, control flow
    • Data Science basics - NumPy, pandas, scikit-learn
  • Solid understanding of machine learning concepts and algorithms
    • Regression, Classification, Unsupervised learning (clustering, Neural Networks)
  • Strong foundations in probability, statistics, and linear algebra
  • Practical experience with at least one deep learning framework (e.g., TensorFlow or PyTorch) recommended
  • Familiarity with natural language processing (NLP) concepts and techniques, such as text preprocessing, word embeddings, and language models

Skills Gained

  • Understand the architecture, training techniques, and evaluation methods for Large Language Models (LLMs)
  • Develop prompts for various NLP tasks
  • Evaluate and compare LLMs for a specific NLP task
  • Fine-tune and adapt open-source LLMs for domain-specific tasks and applications
Course Outline
  • LLM Foundations for ML and Data Science
    • Overview of Generative AI and LLMs
    • LLM Architecture and Training Techniques
      • Deep dive into the transformer architecture and its components
      • Exploring pre-training, fine-tuning, and transfer learning techniques
  • Prompt Engineering for LLMs
    • Introduction to Prompt Engineering
      • Techniques for creating effective prompts
      • Best practices for prompt design and optimization
    • Developing prompts for various NLP tasks
      • Text classification, sentiment analysis, named entity recognition
  • LLM Evaluation and Comparison
    • Overview of metrics and benchmarks for evaluating LLM performance
    • Techniques for comparing LLMs and selecting the best model for a given task
    • Evaluating and comparing LLMs for a specific NLP task
  • Fine-Tuning and Domain Adaptation
    • Introduction to Open-Source LLMs
      • Advantages and limitations in ML and data science projects
    • Preparing domain-specific datasets for fine-tuning LLMs
    • Techniques for adapting LLMs to new domains and tasks using transfer learning
    • Fine-tuning and adapting an open-source LLM for a specific domain