40+ Machine Learning Intern Interview Questions and Answers

There are various NER libraries available with different performances.

• Stanford NER - high accuracy but slow processing

• SpaCy - fast and accurate, supports multiple languages

• NLTK - widely used, but lower accuracy compared to others

• Flair - contextual embeddings for better accuracy

• BERT - pre-trained models for NER tasks

• CRF++ - Conditional Random Fields for NER

• GATE - rule-based and machine learning-based NER

• OpenNLP - Java-based NER library

Yes, I have experience working with Python and knowledge of Convolutional Neural Networks (CNN).

• I have used Python for various projects, including data analysis and machine learning.

• I have implemented CNNs for image classification tasks using libraries like TensorFlow and Keras.

• I am familiar with concepts like convolutional layers, pooling layers, and fully connected layers in CNNs.

To build a regression model for a time series dataset, several steps need to be followed.

• Preprocess the data by checking for missing values, outliers, and transforming the data if necessary.

• Split the data into training and testing sets.

• Select a suitable regression algorithm such as linear regression, decision trees, or neural networks.

• Train the model on the training set and evaluate its performance on the testing set.

• Tune the hyperparameters of the model to improve its perfor...read more

NER process identifies and extracts named entities from text data.

• NER stands for Named Entity Recognition.

• It involves identifying and classifying entities such as people, organizations, locations, and dates.

• NER can be performed using rule-based systems or machine learning algorithms.

• Examples of NER applications include information extraction, sentiment analysis, and chatbots.

• Popular NER tools include spaCy, NLTK, and Stanford NER.

Sampling is the process of selecting a subset of data from a larger population for analysis.

• Types of sampling include random sampling, stratified sampling, cluster sampling, and systematic sampling.

• Sampling is necessary when it is not feasible or practical to analyze the entire population.

• Sampling can help reduce costs and time required for analysis.

• Sampling can also help reduce bias in the analysis by ensuring that the sample is representative of the population.

• Examples of s...read more

Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled data.

• Supervised learning requires a target variable for training, while unsupervised learning does not.

• In supervised learning, the model learns from labeled examples to make predictions on new data, while unsupervised learning finds patterns and relationships in data.

• Examples of supervised learning include classification and regression tasks, while unsupervised learning includ...read more

Some optimizers and loss functions used in machine learning

• Optimizers: Adam, SGD, RMSprop

• Loss functions: Mean Squared Error (MSE), Cross Entropy, Hinge Loss

Inferential statistics infers properties of a population from a sample, while descriptive statistics describes the sample itself.

• Descriptive statistics summarizes and organizes data, while inferential statistics makes predictions and inferences about a larger population based on a sample.

• Descriptive statistics includes measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation), while inferential statistics includes hypothesis tes...read more

Techniques to handle missing values in time series data.

• Imputation using mean, median or mode of the previous or next values.

• Interpolation using linear or spline methods.

• Extrapolation using regression models.

• Dropping missing values if they are insignificant in number.

• Using deep learning models like LSTM to predict missing values.

BOW and Count Vectorizer are both techniques used for text representation in NLP.

• BOW stands for Bag of Words and represents text as a collection of words without considering the order.

• Count Vectorizer is a technique that counts the frequency of each word in a document and represents it as a vector.

• BOW is a simpler technique and is used for tasks like sentiment analysis, while Count Vectorizer is used for more complex tasks like topic modeling.

• Both techniques are used in NLP f...read more

I have worked on projects involving image classification, natural language processing, and predictive modeling.

• Image classification using convolutional neural networks

• Sentiment analysis using recurrent neural networks

• Predictive modeling for sales forecasting

Elbow curve helps in determining the optimal number of clusters in K-means clustering.

• Elbow curve is a plot of the number of clusters against the within-cluster sum of squares.

• The point where the curve shows a sharp decrease and starts to flatten out is considered as the optimal number of clusters.

• It helps in finding the right balance between overfitting and underfitting in clustering.

• For example, if the elbow curve shows a clear bend at 3 clusters, then 3 clusters would be t...read more

Support Vector Machine is a supervised learning algorithm used for classification and regression analysis.

• SVM finds the best hyperplane that separates the data into different classes.

• It maximizes the margin between the hyperplane and the closest data points.

• SVM can handle both linear and non-linear data using kernel functions.

• It is widely used in image classification, text classification, and bioinformatics.

• SVM can also be used for outlier detection and feature selection.

Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled data.

• Supervised learning requires labeled data with input-output pairs for training, while unsupervised learning does not require labeled data.

• In supervised learning, the model learns to map input data to the correct output during training, whereas in unsupervised learning, the model finds patterns and relationships in the data without explicit guidance.

• Examples of supervised ...read more

An outlier is a data point that differs significantly from other observations in a dataset.

• Outliers can be identified using statistical methods such as Z-score, IQR, or visualization techniques like box plots.

• Handling outliers can involve removing them, transforming them, or using robust statistical methods.

• Examples of handling outliers include winsorizing, log transformation, or using algorithms that are robust to outliers like Random Forest.

I have worked on computer vision projects for 6 months during my coursework.

• Completed a computer vision project on object detection using YOLOv3 during a computer vision course

• Implemented facial recognition using OpenCV in a personal project

• Familiar with image processing techniques such as edge detection and image segmentation

Different types of learning in Machine learning include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and self-supervised learning.

• Supervised learning: Training data is labeled, algorithm learns to map input to output.

• Unsupervised learning: Training data is unlabeled, algorithm learns patterns and relationships in data.

• Semi-supervised learning: Combination of labeled and unlabeled data for training.

• Reinforcement learning: Agent ...read more

Yes, I have worked on machine learning before.

• I have completed several online courses on machine learning.

• I have also worked on a project where I used machine learning algorithms to predict customer churn for a telecom company.

• I have experience with Python libraries such as scikit-learn and TensorFlow.

K means Clustering is a unsupervised machine learning algorithm used to group similar data points together.

• K means clustering is used to partition a dataset into K clusters based on their similarity.

• It is an iterative algorithm that starts with K random centroids and assigns each data point to the nearest centroid.

• The centroids are then recalculated based on the mean of the data points in each cluster and the process is repeated until convergence.

• It is widely used in image se...read more

Convolutional neural network (CNN) is a deep learning algorithm commonly used for image recognition and classification.

• CNN is designed to automatically and adaptively learn spatial hierarchies of features from input data.

• It uses convolutional layers to apply filters to input data, extracting features at different spatial locations.

• Pooling layers are used to reduce the spatial dimensions of the input data while retaining important information.

• CNNs are commonly used in computer...read more

Lists are mutable, tuples are immutable in Python.

• Lists are enclosed in square brackets [], tuples are enclosed in parentheses ().

• Elements in a list can be changed, added, or removed, while elements in a tuple cannot be changed.

• Lists are typically used for collections of similar items, tuples are used for fixed collections of items.

• Example: list_example = [1, 2, 3], tuple_example = (4, 5, 6)

Machine learning is a subset of artificial intelligence that enables machines to learn from data and improve their performance.

• Machine learning involves training algorithms to make predictions or decisions based on data

• It uses statistical techniques to identify patterns and relationships in data

• Examples include image recognition, speech recognition, and recommendation systems

• It can be supervised, unsupervised, or semi-supervised

• It has applications in various fields such as fi...read more

Linear Regression is a statistical method to model the relationship between a dependent variable and one or more independent variables.

• It is used to predict a continuous outcome variable based on one or more predictor variables.

• It assumes a linear relationship between the dependent and independent variables.

• It is commonly used in fields like finance, economics, and social sciences.

• It can be simple linear regression (one independent variable) or multiple linear regression (mor...read more

Supervised learning uses labeled data to make predictions, while unsupervised learning finds patterns in unlabeled data.

• Supervised learning requires labeled data to train the model and make predictions on new data.

• Examples of supervised learning include classification and regression.

• Unsupervised learning finds patterns in unlabeled data without any predefined output.

• Examples of unsupervised learning include clustering and dimensionality reduction.

SVM stands for Support Vector Machine, RBF stands for Radial Basis Function. RBF can have infinite dimensions.

• SVM is a supervised machine learning algorithm used for classification and regression tasks.

• RBF is a kernel function used in SVM to map data into a higher-dimensional space.

• RBF can have infinite dimensions, allowing it to capture complex relationships in the data.

NumPy is a powerful library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices.

• NumPy uses C and Fortran libraries in the background for numerical computations, making it faster than pure Python.

• It provides a powerful N-dimensional array object and functions for performing various mathematical operations on arrays.

• NumPy arrays are stored in contiguous blocks of memory, allowing efficient access and manipulation of data.

• Broadca...read more

Deep learning is a subset of machine learning that uses neural networks to model and solve complex problems.

• Deep learning involves training neural networks with multiple layers to learn representations of data

• It is used for tasks such as image and speech recognition, natural language processing, and autonomous driving

• Popular deep learning frameworks include TensorFlow, PyTorch, and Keras

To check for stationarity, we need to look for constant mean, variance, and autocovariance over time.

• Check for constant mean by plotting rolling statistics and performing Dickey-Fuller test.

• Check for constant variance by plotting the moving average of the squared series and performing statistical tests.

• Check for constant autocovariance by plotting autocorrelation function (ACF) and partial autocorrelation function (PACF).

• If the mean, variance, and autocovariance are constant ...read more

A confusion matrix is a table used to evaluate the performance of a classification model.

• It shows the number of true positives, false positives, true negatives, and false negatives.

• It helps in calculating various evaluation metrics like accuracy, precision, recall, and F1 score.

• It is useful in identifying the strengths and weaknesses of a model and improving its performance.

• Example: A confusion matrix for a binary classification problem would look like this: Actual Positive A...read more

Types of regression models include linear regression, polynomial regression, ridge regression, lasso regression, and logistic regression.

• Linear regression: Fits a linear relationship between the independent and dependent variables.

• Polynomial regression: Fits a polynomial relationship between the independent and dependent variables.

• Ridge regression: Adds a penalty term to the linear regression to prevent overfitting.

• Lasso regression: Similar to ridge regression but uses the ab...read more

Logistic regression is used for binary classification while linear regression is used for regression tasks.

• Logistic regression predicts the probability of a binary outcome (0 or 1), while linear regression predicts a continuous outcome.

• Logistic regression uses a sigmoid function to map predicted values between 0 and 1, while linear regression uses a linear function.

• Logistic regression is more suitable for classification tasks, such as predicting whether an email is spam or no...read more

Resume projects and Python coding showcase practical skills and experience relevant to machine learning.

• Resume projects demonstrate hands-on experience with machine learning algorithms and techniques.

• Python coding skills are essential for implementing machine learning models and analyzing data.

• Examples of resume projects could include building a recommendation system, image classification model, or natural language processing application.

Machine learning types include supervised, unsupervised, semi-supervised, and reinforcement learning.

• Supervised learning involves labeled data and predicting outcomes based on that data.

• Unsupervised learning involves finding patterns in unlabeled data.

• Semi-supervised learning is a combination of both supervised and unsupervised learning.

• Reinforcement learning involves learning through trial and error with a reward-based system.

• Examples include image classification (supervised...read more

LLM stands for Latent Language Model, which is a type of machine learning model used for natural language processing tasks.

• LLM is a type of language model that learns to predict the next word in a sentence based on the context provided.

• It uses latent variables to capture the underlying structure of the language.

• LLM can be trained using unsupervised learning techniques such as autoencoders or variational autoencoders.

• Examples of LLM include GPT (Generative Pre-trained Transfor...read more

Random partition is a method of dividing a dataset into random subsets for training and testing purposes.

• Random partition helps in evaluating the performance of a machine learning model by training it on one subset and testing it on another.

• It helps in preventing overfitting by ensuring that the model is tested on unseen data.

• Random partition is commonly used in techniques like k-fold cross-validation where the dataset is divided into k random subsets.

Implementing CNN code on notepad

• Start by defining the CNN architecture with layers like Conv2D, MaxPooling2D, Flatten, and Dense

• Compile the model with appropriate loss function and optimizer

• Train the model on a dataset using fit() function

• Evaluate the model's performance using test data and metrics like accuracy

Yes, I have used Streamlit for building interactive machine learning applications.

• Streamlit is a Python library used for creating web applications with interactive visualizations.

• I have used Streamlit to build a dashboard for visualizing and analyzing machine learning models.

• Streamlit provides easy-to-use APIs for creating interactive UI components like sliders, dropdowns, and plots.

• With Streamlit, I was able to quickly prototype and deploy machine learning models as web appl...read more

I collected a diverse set of images including animals, landscapes, objects, and people.

• Images of various animals such as cats, dogs, birds, and elephants

• Landscapes including mountains, beaches, forests, and deserts

• Objects like cars, bicycles, books, and computers

• People from different cultures and backgrounds