Machine Learning Interview Questions | Day 6
Welcome to the 6th Day of Machine Learning Interview Questions.
You can go through the question and answer for
Day 1
Day 2
Day 3
Day 4
Day 5
Machine Learning Interview Questions
In this series, all you need to do is to go through the questions and try to attempt it with whatever knowledge you have.
Before or after you attempt the questions, do go through the complete road map about what you need to study to crack an analytics role. We have covered from 3rd year college student to 8 years of analytics or non-analytic experience. Make sure you understand where to emphasize more(after all by the end of the day you want to crack an interview)
Complete Roadmap for cracking an Analytics Role in the next 30 days
Now, coming back to our questions. Remember, these are the most asked questions in any interview, if you are shy to answer the questions in the comment section, then do make sure to Google the answer and jot it down in your notebook.
Following are the topic wise questions
SQL
- List the employees with names starting with N and containing 5 alphabets. Table name – Employee, columns – Name, emp_id
Cover all the corner cases - List the detail of all the employees whose salary is less than that of Aman
Employee Table
Columns – Employee_Name, Salary, Department
Python
- When you don’t know how many arguments will be passed to a function, then you need to pass a variable number of arguments. Show by an example.
Case Study
- What are the main KPIs for OYO Rooms?
Think on the lines of revenue, operations, inventory, supply, demand, and conversion
Machine Learning
- What is the chi-square test?
- Give an example of Normal Distribution from daily life.
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12 E-books for 12 Machine Learning algorithms with 1000+ interview questions – For those candidates who want to include any Machine Learning Algorithm in their resume and to learn/revise the important concepts. These 12 e-books are a part of the 23 e-book package
Individual 50+ e-books on separate topics
Important Resources to crack interviews (Mostly Free)
There are a few things that might be very useful for your preparation
The Data Monk Youtube channel – Here you will get only those videos that are asked in interviews with Data Analysts, Data Scientists, Machine Learning Engineers, Business Intelligence Engineers, Analytics managers, etc.
Go through the watchlist which makes you uncomfortable:-
All the list of 200 videos
Complete Python Playlist for Data Science
Company-wise Data Science Interview Questions – Must Watch
All important Machine Learning Algorithm with code in Python
Complete Python Numpy Playlist
Complete Python Pandas Playlist
SQL Complete Playlist
Case Study and Guesstimates Complete Playlist
Complete Playlist of Statistics
Comment ( 1 )
SQL
1. select name from employee
where name like ‘N%’ or name like ‘n%’ and len(name)=5
2. select employee_Name, Salary, Department from Employee
where salary < (select salary from Employee where employee_Name = 'Aman')
Python
def function_name(*args) – can be used when we do not know how many arguments the function may receive.
Case Study
Problem Statement – List main KPIs for OYO rooms.
Clarification question – Do we assume the definition of KPI as the usual one (Key Process Indicators), or is it something else?
Solution (listing some I can think of) –
1. revenue – % growth in partner hotels/properties, avg revenue per room/property, avg booking value.
2. operations – No. of partner / non-zero revenue hotels currently, employee costs, property onboarding and customer acquisition costs, property management expenses.
3. conversion – no. of bookings successfully made/cancelled/refunded, no. of new users onboarded, avg. customer score/ NPS, avg. occupancy rate in properties, avg stay duration.
Machine Learning
1. chi-squared test is used for checking relationship/independence between categorical variables. (Can you help me with a more elaborate and layman definition? This is all I know right now)
2. the marks obtained by students of a particular grade across all schools, follow normal distribution.