Feynn Labs Machine Learning Intern Interview Questions, Process, and Tips

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

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.

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 classificatio...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

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 si...read more