Core Machine Learning and AI Knowledge Understanding the fundamental concepts in machine learning and artificial intelligence (AI) is essential for anyone pursu...
Understanding the fundamental concepts in machine learning and artificial intelligence (AI) is essential for anyone pursuing the NVIDIA Certified AI Associate - GenAI LLM certification. This overview covers key areas of knowledge that will aid in the deployment and evaluation of AI models.
As a foundational skill, you will assist in the deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members. This involves understanding how to assess models in real-world scenarios and ensuring they meet performance benchmarks.
Awareness of the process of extracting insights from large datasets is crucial. Techniques such as data mining and data visualization will be explored to help you interpret complex data and derive actionable insights.
Building Large Language Model (LLM) use cases is a significant aspect of AI applications. You will learn to create applications such as retrieval-augmented generation (RAG), chatbots, and summarizers, which leverage the capabilities of LLMs.
Curating and embedding content datasets for RAGs is essential for effective model training. This involves selecting relevant data that enhances the model's ability to generate accurate and contextually appropriate responses.
Familiarity with the fundamentals of machine learning, including feature engineering, model comparison, and cross-validation, is vital. These concepts help in optimizing model performance and ensuring robust evaluations.
Understanding the capabilities of Python natural language processing packages such as spaCy, NumPy, and vector databases is important for implementing AI solutions effectively.
Reading research papers, including articles and conference papers, will help you identify emerging LLM trends and technologies, keeping you informed about the latest advancements in the field.
Selecting and using models to create text embeddings is a critical skill. This process involves transforming text into numerical representations that can be processed by machine learning algorithms.
Using prompt engineering principles to create effective prompts is essential for achieving desired results from LLMs. This skill enhances the interaction between users and AI models.
Utilizing Python packages such as spaCy, NumPy, and Keras to implement specific traditional machine learning analyses will provide you with practical experience in applying theoretical knowledge.
In conclusion, mastering these core concepts will not only prepare you for the NVIDIA Certified AI Associate certification but also equip you with the skills necessary to excel in the rapidly evolving field of AI and machine learning.