Answer:
They are not always right or the most accurate.
Explanation:
Judgments and decisions based on heuristics are simply good enough to satisfy a pressing need in situations of uncertainty, where information is incomplete.
Answer:
Offer second chances/clean slates.
Be resourceful.
Make learning active.
Be an advocate.
Pursue lifelong learning.
Answer:
i think you can layer it :)
Explanation:
nice drawing btw!
Answer:
To create a public key signature, you would use the <u>_private_</u> key.
Explanation:
To create a public key signature, a private key is essential to enable authorization.
A private key uses one key to make data unreadable by intruders and for the data to be accessed the same key would be needed to do so.
The login details and some important credentials to access user data contains both the user's public key data and private key data. Both private key and public key are two keys that work together to accomplish security goals.
The public key uses different keys to make data readable and unreadable.
The public key is important to verify authorization to access encrypted data by making sure the access authorization came from someone who has the private key. In other words, it's a system put in place to cross-check the holder of the private key by providing the public key of the encrypted data that needed to be accessed. Though, it depends on the key used to encrypt the data as data encrypted with a public key would require a private key for the data to be readable.
Answer:
Collaborative filtering
Explanation:
This is one out of five on the Recommender system apart from most popular items, Association and Market Basket based Analysis, Content-based analysis, self and hybrid analysis where we use both content-based and collaborative based approach together. And the Recommender system is a very important topic in Data science. For this question, remember that Collaborative filtering focuses on user and various other user's choices which are mathematically alike to concerned users, and which we find with the study of a large data set. Thus, we can predict from our above study that what are going to be likes of concerned users, and at the item level, whether that item will be liked by the concerned user or not. And this is prediction, and we use this approach in Machine learning these days. For this question, and as mentioned in question the requirements, answer is Collaborative filtering.
