Machine Learning Software Engineer Interview Questions and Answers

Remove spaces from dataframe, concatenate first and last name, and add new column.

• Use str.replace() to remove spaces from dataframe columns.

• Use str.cat() to concatenate first and last name.

• Use df['new_column'] = concatenated_names to add new column.

Assumptions in Linear Regression and Gaussian Noise origins

• Assumptions in Linear Regression include linearity, independence of errors, homoscedasticity, and normality of errors.

• Gaussian Noise assumption comes from the assumption that the errors in the model follow a Gaussian distribution.

• The Gaussian Noise assumption allows for the use of maximum likelihood estimation in linear regression models.

• If the errors do not follow a Gaussian distribution, alternative regression model...read more

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

• Linear Regression is used to predict a continuous output based on one or more input features.

• The cost function for Linear Regression is the Mean Squared Error (MSE), which measures the average squared difference between the predicted values and the actual values.

• The goal of Linear Regression is to minimize the cost function by adjusting the pa...read more

Creating a simple Artificial Neural Network (ANN) using Tensorflow.

• Import the necessary libraries like tensorflow and numpy.

• Define the input layer, hidden layers, and output layer.

• Compile the model with appropriate loss function and optimizer.

• Train the model using training data.

• Evaluate the model using test data.

Building an xgboost model involves training a gradient boosting algorithm for classification or regression tasks.

• Install xgboost library using pip install xgboost

• Import necessary libraries like xgboost, numpy, and pandas

• Prepare the data by splitting into training and testing sets

• Instantiate an xgboost classifier or regressor object

• Fit the model on the training data using the fit method

• Make predictions on the test data using the predict method

Logistic regression is a statistical method used to analyze and model the relationship between a dependent variable and one or more independent variables.

• Logistic regression is used for binary classification problems.

• It uses a sigmoid function to map input values to a probability score.

• The model is trained using maximum likelihood estimation.

• The cost function used is the negative log-likelihood function.

• Regularization techniques like L1 and L2 can be applied to prevent overfi...read more

Sets are faster than lists because they use hash tables for storage, allowing for constant time complexity for operations like membership check.

• Sets use hash tables for storage, providing constant time complexity for operations like membership check

• Sets do not allow duplicate elements, making them efficient for tasks like removing duplicates from a list

• Lists have linear time complexity for membership check and removing elements

Bert embeddings is a language model that generates contextualized word embeddings.

• Bert stands for Bidirectional Encoder Representations from Transformers.

• It uses a transformer architecture to generate word embeddings that take into account the context of the word.

• Bert embeddings are pre-trained on large amounts of text data and can be fine-tuned for specific NLP tasks.

• They have been shown to outperform traditional word embeddings like Word2Vec and GloVe.

• Example applications i...read more