This is a practical guide to help you ace classical Machine Learning interviews.
The book contains sample of theoretical questions asked on interviews on Data Science position.
What you will get:
- 6 chapters 47 pages covering topics ranging from Statistics and probability, Supervised machine learning, Gradient Descent Algorithm, Loss measure
- 77 interview questions
- Usefull links and preparation repository
Table of Contents:
- Statistics and probability
- - Distribution
- Supervised machine learning
- - Binary classification
- - Regression
- - Singular Value Decomposition (SVD)
- - Logistic Regression
- Gradient Descent
- Loss measure
Data Science Interview Questions
Usefull links and preparation repository
If you have any questions feel free to contact me at firstname.lastname@example.org or on Twitter alimbekovkz
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