Rectras – Machine Learning Interview Questions
Here is the list Machine Learning Interview Questions which are recently asked in Rectras company. These questions are included for both Freshers and Experienced professionals.
1. How do you handle missing or corrupted data in a dataset?Methods in missing or corrupted data in a dataset are:
- Method 1 is deleting rows or columns. We usually use this method when it comes to empty cells.
- Method 2 is replacing the missing data with aggregated values.
- Method 3 is creating an unknown category.
- Method 4 is predicting missing values.
2. Do you have experience with Spark or big data tools for machine learning?
Yes, most of the companies do ask for big data and spark knowledge and experience before taking anyone as data scientist and so I feel it is very much required.
3. Pick an algorithm. Write the psuedo-code for a parallel implementation.The psuedo-code for a parallel implementation are:
- Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions.
- Accuracy and/or Interpretability of the output.
- Speed or Training time.
- Number of features.
4. What are some differences between a linked list and an array?
Array elements are independent of the each other.
Linked list elements are dependent on each other. As each node contains the address of the next node so to access the next node, we need to access its previous node.
5. Describe a hash table.
A hash table is a data structure that implements an associative array abstract data type, a structure that can map keys to values. A hash table uses a hash function to compute an index, also called a hash code, into an array of buckets or slots, from which the specified value can be found.
6. Which data visualization libraries do you use? What are your thoughts on the best data visualization tools?The Best Data Visualization Libraries are:
- React-vis. GitHub stars: 4,612. React-vis is a React visualization library created by Uber.
- Chart. js. GitHub stars: 40,141.
- VX. GitHub stars: 5,263.
- Rechart. GitHub stars: 10,229.
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7. How can we use your machine learning skills to generate revenue?Machine learning skills to generate revenue are:
- Bringing personalization to customer service.
- Making recruitment process convenient and successful.
- Improving finance management and handling methods.
- Marketing and Management.
8. What do you think of our current data process?
Data processing, manipulation of data by a computer. It includes the conversion of raw data to machine-readable form, flow of data through the CPU and memory to output devices, and formatting or transformation of output. Any use of computers to perform defined operations on data are often included under data processing.
9. What are the last machine learning papers you’ve read?
Artificial Intelligence is one of the most rapidly growing fields in the science and is one of the most sought skills of the past few years, commonly labeled as the Data Science. The area has far-reaching applications, being usually divided by input type: text, audio, image, video, or graph; or by problem formulation: supervised, unsupervised, and reinforcement learning.
10. Do you have research experience in machine learning?
Start with in a clear idea of why you want to research a given machine learning algorithm, and then pick those sources that can best answer the questions that you have. There are five different sources that you can use in your research of a machine learning algorithm, we will review each in turn.
11. What are your favorite use cases of machine learning models?Favorite use cases of machine learning models are:
- Applications in Smartphones.
- Transportation Optimization.
- Popular Web Services.
- Sales and Marketing.
- Financial Domain.
- Other Popular Use Cases.
12. How would you approach the “Netflix Prize” competition?
The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict the user ratings for films, based on previous ratings without any other information about the users or films, without the users or the films being identified except by numbers assigned for the contest.
13. How do you think Google is training data for self-driving cars?
The driverless car uses data from all eight sensors, interpreted by Google's software, to keep you safe and get you from A to B. The data that Google's software receives is used to accurately identify other road users and their behavior the patterns, plus commonly used highway signals.
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TOP MNC's MACHINE LEARNING INTERVIEW QUESTIONS & ANSWERS
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