Download Microsoft Azure AI Fundamentals.CertLibrary.AI-900.2021-03-28.1e.50q.vcex

In Azure Machine Learning, the percentage split is the available technique to split the data. In this technique, random data of a given percentage will be split to train and test data. 
Reference: 
https://www.sqlshack.com/prediction-in-azure-machine-learning/ 

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 

Model Explain Ability. 
Most businesses run on trust and being able to open the ML "black box" helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs. 
Reference: 
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/ 

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 

Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer. 
Reference: 
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles 

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