Answer:
Supervised and Unsupervised Learning:
a. Unsupervised learning
b. Supervised learning
3. Supervised learning
4. Unsupervised learning
Explanation:
The key difference between supervised machine learning and unsupervised machine learning is that with supervised machine learning there is a training dataset (labeled data) on which the algorithm is trained to predict patterns. With unsupervised machine learning on the other hand, there is no training data. So, the algorithm discovers patterns on itself without reference to another labeled data or training dataset.
<span>This invisible barrier is called the glass ceiling. There are multiple factors that enable such a thing, including (but not limited to) prejudices against women in the work place, lack of recruitment of women to certain types of jobs that are historically performed by men (i.e. science, engineering, etc), and lack of mentoring on the job.</span>
Answer:
B)secure industries that are expected to grow.
Explanation:
The other person was right but just accidently said A) instead of B)
Hope this helps! :D
The suggestion suitable for Juan’s situation is for Juan to
check out the enterprises zones in the Colorado. Having to check this out will
help him to know where to locate his small business and to have a solution in
terms of limiting his tax liability in his business.
Answer:
A. The seller would be primarily liable.
Explanation:
Subject to basis is a form of home buying options in real estate. It is a situation where the buyer takes over existing loan of a seller and make commitment to seller to continue repaying the loan to the lender.
Though the buyer will taken over the loan from the seller and make repayment to the lender, there is no legal obligation on buyer`s part that makes him/her liable to the lender. The seller still remain liable despite the the taking over. So option A is right while B to D is wrong because it`s only the seller that is primarily liable to the lender.
