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Chapter 1: Core Machine Learning and AI Knowledge -Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.
Chapter 2: Core Machine Learning and AI Knowledge -Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
Chapter 3: Core Machine Learning and AI Knowledge -Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
Chapter 5: Core Machine Learning and AI Knowledge -Familiarity with the fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).
Chapter 6: Core Machine Learning and AI Knowledge -Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
Chapter 7: Core Machine Learning and AI Knowledge -Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.
Chapter 8: Core Machine Learning and AI Knowledge -Select and use models to create text embeddings.
Chapter 9: Core Machine Learning and AI Knowledge -Use prompt engineering principles to create prompts to achieve desired results
Chapter 10: Core Machine Learning and AI Knowledge -Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses
Chapter 11: Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
Chapter 12: Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
Chapter 13: Conduct data analysis under the supervision of a senior team member.
Chapter 14: Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
Chapter 15: Identify relationships and trends or any factors that could affect the results of research.
Chapter 16: Experimentation-Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
Chapter 17: Experimentation-Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
Chapter 18: Experimentation-Conduct data analysis under the supervision of a senior team member.
Chapter 19: Experimentation-Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
Chapter 20: Experimentation-Identify relationships and trends or any factors that could affect the results of research.
Chapter 21: Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of senior team member.
Chapter 22: Build LLM use cases such as RAGs, chatbots, and summarizers.
Chapter 23: Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
Chapter 24: Identify system data, hardware, or software components required to meet user needs.
Chapter 25: Monitor functioning of data collection, experiments, and other software processes.
Chapter 26: Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
Chapter 27: Write software components or scripts under the supervision of a senior team member.
Chapter 28: Trustworthy AI -Describe the ethical principles of trustworthy AI.
Chapter 29: Trustworthy AI -Describe the balance between data privacy and the importance of data consent.
Chapter 30: Trustworthy AI -Describe how to use NVIDIA and other technologies to improve AI trustworthiness.
Chapter 31: Trustworthy AI -Describe how to minimize bias in AI systems.

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