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TCS
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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
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
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 2024. There was 1 interview round.
What people are saying about TCS
The zip function in Python is used to combine multiple iterables into a single iterable of tuples.
Zip function takes two or more iterables as arguments and returns an iterator of tuples where the i-th tuple contains the i-th element from each of the input iterables.
If the input iterables are of different lengths, the resulting iterator will only have as many elements as the shortest input iterable.
Example: zip([1, 2, 3...
NLP (Natural Language Processing) in machine learning is the ability of a computer to understand, interpret, and generate human language.
NLP enables machines to analyze and derive meaning from human language data.
It involves tasks such as text classification, sentiment analysis, named entity recognition, and machine translation.
Examples of NLP applications include chatbots, language translation services, and speech rec
I applied via Company Website and was interviewed in Apr 2024. There was 1 interview round.
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 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
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
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