40+ Machine Learning Intern Interview Questions and Answers for Freshers

Types of regression models include linear regression, polynomial regression, ridge regression, lasso regression, and logistic regression.

• Linear regression: Fits a linear relationship between the independent and dependent variables.

• Polynomial regression: Fits a polynomial relationship between the independent and dependent variables.

• Ridge regression: Adds a penalty term to the linear regression to prevent overfitting.

• Lasso regression: Similar to ridge regression but uses the ab...read more

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 classification tasks, such as predicting whether an email is spam or no...read more

Resume projects and Python coding showcase practical skills and experience relevant to machine learning.

• Resume projects demonstrate hands-on experience with machine learning algorithms and techniques.

• Python coding skills are essential for implementing machine learning models and analyzing data.

• Examples of resume projects could include building a recommendation system, image classification model, or natural language processing application.

Machine learning types include supervised, unsupervised, semi-supervised, and reinforcement learning.

• Supervised learning involves labeled data and predicting outcomes based on that data.

• Unsupervised learning involves finding patterns in unlabeled data.

• Semi-supervised learning is a combination of both supervised and unsupervised learning.

• Reinforcement learning involves learning through trial and error with a reward-based system.

• Examples include image classification (supervised...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 is divided into k random subsets.

Implementing CNN code on notepad

• Start by defining the CNN architecture with layers like Conv2D, MaxPooling2D, Flatten, and Dense

• Compile the model with appropriate loss function and optimizer

• Train the model on a dataset using fit() function

• Evaluate the model's performance using test data and metrics like accuracy

Yes, I have used Streamlit for building interactive machine learning applications.

• Streamlit is a Python library used for creating web applications with interactive visualizations.

• I have used Streamlit to build a dashboard for visualizing and analyzing machine learning models.

• Streamlit provides easy-to-use APIs for creating interactive UI components like sliders, dropdowns, and plots.

• With Streamlit, I was able to quickly prototype and deploy machine learning models as web appl...read more

I collected a diverse set of images including animals, landscapes, objects, and people.

• Images of various animals such as cats, dogs, birds, and elephants

• Landscapes including mountains, beaches, forests, and deserts

• Objects like cars, bicycles, books, and computers

• People from different cultures and backgrounds

Labeling data involves categorizing it for training machine learning models, ensuring accuracy and relevance.

• Define clear labeling criteria based on the problem domain.

• Use tools like Labelbox or VGG Image Annotator for image data.

• For text data, employ techniques like sentiment analysis to label sentiments.

• In supervised learning, ensure labels are consistent and validated by multiple annotators.

• Example: In a medical dataset, label images as 'healthy' or 'diseased' based on exp...read more

Aspiring machine learning intern with a strong foundation in algorithms, data analysis, and programming languages like Python and R.

• Educational background in computer science or related field, e.g., Bachelor's in Computer Science.

• Experience with machine learning frameworks like TensorFlow or PyTorch.

• Familiarity with data preprocessing techniques, such as normalization and feature selection.

• Hands-on projects showcasing skills, like building a predictive model for housing price...read more