### Q1: What is an algorithm in the context of machine learning, and why is it important for analyzing data?  
### Q2: How does the training process of a machine learning model affect its predictive accuracy in a financial application?  
### Q3: Can you explain the difference between training and testing in machine learning and why both are essential for model performance evaluation?  
### Q4: How can selecting the right features influence the outcomes of a machine learning model in fintech?  
### Q5: What are some potential challenges in feature selection that could arise during a machine learning project in finance?  
### Q6: How does understanding machine learning terminology contribute to better decision-making in technology investments within a finance context?  
### Q7: Why is effective communication about machine learning terminology crucial when collaborating with technical and non-technical teams?  
### Q8: Can you provide an example of how clear communication of machine learning concepts impacted a project outcome in your experience?  
### Q9: In what ways can mastery of machine learning terminology enhance your career prospects in the fintech industry?