Download Microsoft Azure AI Fundamentals.PrepDumps.AI-900.2021-07-28.2e.73q.vcex

Box 1: 11 - 

  

TP = True Positive.  
The class labels in the training set can take on only two possible values, which we usually  refer to as positive or negative. 
The positive and negative instances that a classifier predicts  correctly are called true positives (TP) and true negatives (TN), respectively. 
Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).  
Box 2: 1,033 - 
FN = False Negative -  
Reference: 
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

Anomaly detection encompasses many important tasks in machine learning: 
Identifying transactions that are potentially fraudulent.  
Learning patterns that indicate that a network intrusion has occurred.  
Finding abnormal clusters of patients.  
Checking values entered into a system.  
Reference: 
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection

Privacy and security.  
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used. At Microsoft, we are continuing to research privacy and security breakthroughs (see next unit) and invest in robust compliance processes to ensure that data collected and used by our AI systems is handled responsibly.  
Reference: 
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

Box 3: Natural language processing 
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.  
Reference: 
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

Box 1: Reliability and safety - 
To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.  
Box 2: Fairness - 
Fairness: AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different ways. For example, when AI systems provide guidance on medical treatment, loan applications, or employment, they should make the same recommendations to everyone with similar symptoms, financial circumstances, or professional qualifications.  
We believe that mitigating bias starts with people understanding the implications and limitations of AI predictions and recommendations. Ultimately, people should supplement AI decisions with sound human judgment and be held accountable for consequential decisions that affect others.  
Box 3: Privacy and security - 
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used  
Reference: 
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

Reliability and safety: 
To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions.  
These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.  
Reference: 
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles