Feynn Labs Interview Questions, Process, and Tips

Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables. Logistic regression is used to model the probability of a binary outcome.

• Linear regression is used for predicting continuous outcomes, while logistic regression is used for predicting binary outcomes.

• Linear regression assumes a linear relationship between the independent and dependent ...read more

Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.

• Central Limit Theorem is used to make inferences about a population mean based on the sample mean.

• It allows us to use the properties of the normal distribution to estimate population parameters.

• It is essential in hypothesis testing and constructing confidence intervals.

• For example...read more

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

• Support Vector Machine finds the hyperplane that best separates different classes in the feature space

• It works by maximizing the margin between the hyperplane and the nearest data points, known as support vectors

• SVM can handle both linear and non-linear data by using different kernel functions like linear, pol

Linear regression is used for continuous variables, while logistic regression is used for binary classification.

• Linear regression predicts continuous values, while logistic regression predicts probabilities between 0 and 1.

• Linear regression uses a linear equation to model the relationship between the independent and dependent variables.

• Logistic regression uses the logistic function to model the probability of a binary ...read more

KNN algorithm is a simple, instance-based learning algorithm used for classification and regression tasks.

• KNN stands for K-Nearest Neighbors.

• It classifies a new data point based on majority class of its k nearest neighbors.

• KNN is a lazy learning algorithm as it does not learn a discriminative function from the training data.

• It is sensitive to the choice of k value and distance metric.

• Example: Classifying a flower speci...read more

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

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