10+ Feynn Labs Machine Learning Intern Interview Questions and Answers

Inference learning focuses on understanding the underlying relationships in data, while prediction learning focuses on making accurate predictions based on data.

• Inference learning involves understanding the causal relationships between variables in the data.

• Prediction learning focuses on building models that can accurately predict outcomes based on input data.

• Inference learning is more concerned with understanding the 'why' behind the data, while prediction learning is more f...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

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

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

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

• Logistic regression is used when the dependent variable is binary (0 or 1), while linear regression is used when the dependent variable is continuous.

• Logistic regression predicts the probability of a certain class or event occurring, while linear regression predicts a continuous value.

• Logistic regression uses a sigmoid function to map predicted values between 0 and 1, wh...read more

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.

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

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

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)

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.

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

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