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StartupTN Ai Ml Engineer Interview Questions and Answers
StartupTN Ai Ml Engineer Interview Experiences
1 interview found
Anonymous
Selected
I applied via Referral and was interviewed in Mar 2024. There were 2 interview rounds.
To do a task of summarizing startup applications using ai model
(1 Question)
- Q1. Explain the task
- Ans.
The task involves explaining a specific problem or project related to artificial intelligence and machine learning.
Provide a detailed overview of the problem or project
Explain the goals and objectives of the task
Discuss the data sources, algorithms, and techniques used
Present any challenges faced and how they were overcome
Highlight the results and impact of the AI/ML solution
Interview questions from similar companies
Mithun work
I was interviewed in Oct 2024.
(3 Questions)
- Q1. What is OHE ( one hot encoding)
- Ans.
OHE is a technique used in machine learning to convert categorical data into a binary format.
OHE is used to convert categorical variables into a format that can be provided to ML algorithms.
Each category is represented by a binary vector where only one element is 'hot' (1) and the rest are 'cold' (0).
For example, if we have a 'color' feature with categories 'red', 'blue', 'green', OHE would represent them as [1, 0, 0],
- Q2. What is conditional probability
- Ans.
Conditional probability is the likelihood of an event occurring given that another event has already occurred.
Conditional probability is calculated using the formula P(A|B) = P(A and B) / P(B)
It represents the probability of event A happening, given that event B has already occurred
Conditional probability is used in various fields such as machine learning, statistics, and finance
- Q3. What is precision, recall
- Ans.
Precision and recall are evaluation metrics used in machine learning to measure the performance of a classification model.
Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.
Recall is the ratio of correctly predicted positive observations to the all observations in actual class.
Precision is important when the cost of false positives is high, while recall is i...
Skills evaluated in this interview
Discussion on Gradient, SGD, K Mean ++, Silhouette Score, How to Handle High Variation Data,
Coding asked to code KNN, Hyper-Parameter Tuning, Two Difficult Questions on Coding...DSA Based Stumped on Those.
Verdict... Not Selected
Simple Coding No Chat GPT Support Should Be There
Interview Preparation Tips
I applied via Approached by Company and was interviewed in Feb 2024. There were 2 interview rounds.
(1 Question)
- Q1. Questions about projects and ML concepts, with coding questions.
(1 Question)
- Q1. Questions about projects in past jobs
(2 Questions)
- Q1. Ask me on project
- Q2. Aws, sql, python, flask ask me
Interview Preparation Tips
Selected
I applied via Approached by Company and was interviewed in Jul 2024. There were 2 interview rounds.
(4 Questions)
- Q1. Explain the ML project you recently worked?
- Ans.
Developed a recommendation system for an e-commerce platform using collaborative filtering
Used collaborative filtering to analyze user behavior and recommend products
Implemented matrix factorization techniques to improve recommendation accuracy
Evaluated model performance using metrics like RMSE and precision-recall curves
- Q2. Questions related to ML fundamentals like supervised learning, unsupervised learning, evaluation and ML algorithms
- Q3. Project specific questions
- Q4. Easy-medium coding questions
(2 Questions)
- Q1. What technologies you are working on?
- Ans.
I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.
Python programming language
TensorFlow framework
scikit-learn library
- Q2. How you will approach on machine learning problem?
- Ans.
I would approach a machine learning problem by first understanding the problem, collecting and preprocessing data, selecting a suitable algorithm, training the model, evaluating its performance, and fine-tuning it.
Understand the problem statement and define the objectives clearly.
Collect and preprocess the data to make it suitable for training.
Select a suitable machine learning algorithm based on the problem type (clas...
Interview Preparation Tips
Skills evaluated in this interview
(2 Questions)
- Q1. Steps of pre-processing
- Ans.
Pre-processing steps involve cleaning, transforming, and preparing data for machine learning models.
Data cleaning: removing missing values, duplicates, and outliers
Data transformation: scaling, encoding categorical variables, and feature engineering
Data normalization: ensuring all features have the same scale
Data splitting: dividing data into training and testing sets
- Q2. What is lemmatization ?
- Ans.
Lemmatization is the process of reducing words to their base or root form.
Lemmatization helps in standardizing words for analysis.
It reduces inflected words to their base form.
For example, 'running' becomes 'run' after lemmatization.
I applied via Naukri.com and was interviewed in Jan 2022. There were 4 interview rounds.
(1 Question)
- Q1. Basic machine learning question and basic python question
(1 Question)
- Q1. It's is techno-managerial round so be confident and answer each question in calm all questions are from basic machine learning domain and also prepare project well
(1 Question)
- Q1. Basic hr questions like why want to change and salary etc....
Interview Preparation Tips
- random forest
- Python
(2 Questions)
- Q1. Question on NLP and Coding test
- Q2. Maximum coding question from list and Regex
(1 Question)
- Q1. Project based question
Interview Preparation Tips
Selected
I applied via Referral and was interviewed in Jul 2024. There were 3 interview rounds.
30 MCQs where 15 Need to be answered correctly to get shortlisted.
Sanfoundary source is very helpful in cracking it.
(4 Questions)
- Q1. Explain Oops concepts
- Ans.
Oops concepts refer to Object-Oriented Programming principles such as Inheritance, Encapsulation, Polymorphism, and Abstraction.
Inheritance: Allows a class to inherit properties and behavior from another class.
Encapsulation: Bundling data and methods that operate on the data into a single unit.
Polymorphism: Ability to present the same interface for different data types.
Abstraction: Hiding the complex implementation det
- Q2. Explain file handling
- Ans.
File handling refers to the process of managing and manipulating files on a computer system.
File handling involves tasks such as creating, reading, writing, updating, and deleting files.
Common file operations include opening a file, reading its contents, writing data to it, and closing the file.
File handling in programming languages often involves using functions or libraries specifically designed for file operations.
E...
- Q3. Explain supervised and unsupervised learning algorithms of your choice.
- Ans.
Supervised learning uses labeled data to train a model, while unsupervised learning finds patterns in unlabeled data.
Supervised learning requires input-output pairs for training
Examples include linear regression, support vector machines, and neural networks
Unsupervised learning clusters data based on similarities or patterns
Examples include k-means clustering, hierarchical clustering, and principal component analysis
- Q4. Coding question on pandas which had 10 followup questions
(1 Question)
- Q1. Simple discussion on compensation
Interview Preparation Tips
Skills evaluated in this interview
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