Machine Learning Software Engineer

Machine Learning Software Engineer Interview Questions and Answers

Updated 1 Nov 2024

Q1. Remove space from given dataframe and concat the first and last name and add new column to dataframe.

Ans.

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.

Q2. What are the assumptions in Linear Regressions? Where does the assumptions of Gaussian Noise come from?

Ans.

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

Machine Learning Software Engineer Interview Questions and Answers for Freshers

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Q3. What is Linear Regression? Details of Cost function and Derivation.

Ans.

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

Q4. Create simple ANN network using Tensorflow.

Ans.

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.

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Q5. Create the virtual environment using cmd.

Ans.

Creating a virtual environment in CMD helps manage dependencies for Python projects.

Open Command Prompt (cmd) on your computer.
Navigate to your project directory using 'cd path_to_your_project'.
Run 'python -m venv env' to create a virtual environment named 'env'.
Activate the virtual environment with 'env\Scripts\activate' on Windows.
To deactivate, simply type 'deactivate' in the command prompt.

Q6. Build xg bost model(actaul code)

Ans.

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

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Q7. What is dropout and batch normalization?

Ans.

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

Q8. Explain working method of logistic regression with Maths

Ans.

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

Q9. How is set faster than lists?

Ans.

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

Q10. How does Bert embeddings work

Ans.

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