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

Dropout is a regularization technique to prevent overfitting by randomly setting some neuron outputs to zero during training. Batch normalization is a technique to improve the training of deep neural networks by normalizing the input of each layer.

• Dropout randomly sets a fraction of neuron outputs to zero during training to prevent overfitting.

• Batch normalization normalizes the input of each layer to improve training of deep neural networks.

• Dropout helps in reducing overfitti...read more

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

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