"To get a job in machine learning, you must have the programming and mathematical knowledge to create artificial intelligence that is capable of learning new tasks without being explicitly coded. In an interview you may be asked about your experience with pertinent coding languages such as Java and C++ as well as with writing algorithms. The interview will be comprised mainly of technical questions that test your knowledge of the fundamental concepts of machine learning such as data mining and signal processing."
I really enjoyed the entire process with each and everyone at every stage of the interview. The expectations were very optimistic and mostly focused on the approach of how to tackle when a problem is at hand. 1. Architecture on how to deploy ML models at scale. 2. Deep learning architecture. 3. Building a language model along with approach to building features. 4. Approach to problem solving. 5. Handling disagreement between colleagues.
Name a type of model you typically work on
The 2hr offline technical included a long string comma-separated data. Asked for training a model on the data.
Describe your favourite machine learning algorithm and how it works. What are the weights and what exactly is the input and output of this algorithm,?
what is f1 score in ml
difference btwn AI and ML
types of learning in machine learning
They asked general question about my past experience.
describing how transformers decoders are different from encoders
In-depth Machine Learning
Viewing 291 - 300 interview questions