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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.
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.
What people are saying about TCS
I applied via Approached by Company and was interviewed in Jul 2024. There were 2 interview rounds.
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
I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.
Python programming language
TensorFlow framework
scikit-learn library
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...
I applied via Naukri.com and was interviewed in Dec 2024. There was 1 interview round.
I applied via Campus Placement and was interviewed before Oct 2023. There were 3 interview rounds.
Basic math and ml and dl questions
Simple project demonstration
Manager discussion and project explanation
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
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