There are 14 instances stored in the database described with several attributes: day, outlook, temperature, humidity, wind and 'playTennis'. Each instance describes the facts of the day and the action of the observed person (played or not played tennis). Based on the given record we can assess which factors affected the person's decision about playing tennis. Training Examples Outlook Day Sunny Temp. Humidity High High Wind Play Tennis No D1 Hot Weak D2 D3 Strong Weak Weak Weak Strong Weak Weak No Sunny Overcast Rain Hot Hot Yes D4 D5 High High Normal Normal Mild Yes Yes No Rain |Cool D6 Rain Сol D7 Overcast Cool Normal Yes D8 Sunny Sunny Mild No High Normal Normal Normal High Normal D9 Cool Yes Weak Strong Strong Strong Weak D10 Rain Mild Yes D11 Sunny D12 Overcast D13 Overcast Rain Mild Mild Yes Yes Yes Hot D14 Mild High Strong No

The author claims that "machine learning is an empirical science." What does this sentence actually mean?

Does the current age of machine learning still have a place for data structures and algorithms? Do you think that's the case? What precisely connects the two circumstances?

Describe the idea of heredity as well as the many connections that may be made between different things.