Valere Labs Ai Ml Engineer Interview Questions and Answers

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
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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.
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Q1. What is OHE ( one hot encoding)
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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],...read more
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Q2. What is conditional probability
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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
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Q3. What is precision, recall
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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...read more
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Q1. Normal HR round that happens as the initial screening process
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Python basic coding questions (pandas, numpy, OOPs concept.
AI ML theory questions

Q1. Normal salary discussion
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30 MCQs where 15 Need to be answered correctly to get shortlisted.
Sanfoundary source is very helpful in cracking it.

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...read more
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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...read more
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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
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Q4. Coding question on pandas which had 10 followup questions
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Q1. Simple discussion on compensation
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Q1. Describe any NLP project end to end.
Ans. 
Developed a sentiment analysis model using NLP to analyze customer reviews for a product.
• Collected and preprocessed text data from various sources

• Performed tokenization, stopword removal, and lemmatization

• Built a machine learning model using techniques like TF-IDF and LSTM

• Evaluated the model's performance using metrics like accuracy and F1 score

• Deployed the model for real-time sentiment analysis of new reviews
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Q2. What is cosine similarity?
Ans. 
Cosine similarity is a measure of similarity between two non-zero vectors in an inner product space.
• It measures the cosine of the angle between the two vectors.

• Values range from -1 (completely opposite) to 1 (exactly the same).

• Used in recommendation systems, text mining, and clustering algorithms.
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Q3. What is difference between iterator and iterable?
Ans. 
Iterator is an object that allows iteration over a collection, while iterable is an object that can be iterated over.
• Iterator is an object with a next() method that returns the next item in the collection.

• Iterable is an object that has an __iter__() method which returns an iterator.

• Example: List is iterable, while iter(list) returns an iterator.
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Q4. Write a python function for cosine similarity.
Ans. 
Python function to calculate cosine similarity between two vectors.
• Define a function that takes two vectors as input.

• Calculate the dot product of the two vectors.

• Calculate the magnitude of each vector and multiply them.

• Divide the dot product by the product of magnitudes to get cosine similarity.
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Q5. How did you evaluate your model. what is F1 score.
Ans. 
F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall.
• F1 score is the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall).

• It is a better metric than accuracy when dealing with imbalanced datasets.

• A high F1 score indicates a model with both high precision and high recall.

• F1 score ranges from 0 to 1, where ...read more
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Q1. Ask me on project
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Q2. Aws, sql, python, flask ask me
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Q1. Confusion matrix
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