Feynn Labs Machine Learning Intern Interview Questions and Answers

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 ...read more

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 ...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 out...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

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 sh...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 classificatio...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

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.

• Las...read more

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 ...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)

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 als...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 i...read more