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I applied via Referral and was interviewed in Oct 2024. There were 2 interview rounds.
Machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data.
Machine learning involves training algorithms to learn patterns and make predictions from data.
It can be supervised, unsupervised, or semi-supervised learning.
Examples include image recognition, natural language processing, and recommendation system
Developed a machine learning model to predict customer churn for a telecom company.
Used historical customer data to train the model
Implemented various classification algorithms such as Random Forest and Logistic Regression
Evaluated model performance using metrics like accuracy, precision, and recall
I applied via Naukri.com and was interviewed in Dec 2024. There was 1 interview round.
Lemmatization produces the base or dictionary form of a word, while stemming reduces words to their root form.
Lemmatization considers the context and meaning of the word, resulting in a valid word that makes sense.
Stemming simply chops off prefixes or suffixes, potentially resulting in non-existent words.
Example: Lemmatization of 'better' would result in 'good', while stemming would reduce it to 'bet'.
Types of prompts include text prompts, image prompts, audio prompts, and video prompts.
Text prompts: prompts that are in written form
Image prompts: prompts that are in visual form
Audio prompts: prompts that are in audio form
Video prompts: prompts that are in video form
Symmetric encryption uses the same key for both encryption and decryption, while asymmetric encryption uses different keys for encryption and decryption.
Symmetric encryption is faster and more efficient than asymmetric encryption.
Asymmetric encryption provides better security as it uses a public key for encryption and a private key for decryption.
Examples of symmetric encryption algorithms include AES and DES, while ex...
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.
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
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...
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
I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.
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
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.
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.
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.
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
I applied via Company Website and was interviewed in Jul 2023. There were 4 interview rounds.
Python basic coding questions (pandas, numpy, OOPs concept.
AI ML theory questions
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
I applied via Naukri.com and was interviewed in Jan 2024. There was 1 interview round.
I was interviewed in Oct 2024.
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],
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
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...
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