7-Day Machine Learning Interview Preparation Plan
Your saved questions plus a day-by-day plan. Add questions with Save to → 7-Day Job Prep on any question page.
Day-by-day plan
Day 1ML fundamentals
- Revise types of learning, bias-variance and overfitting with one example each
- Explain gradient descent and learning rate aloud in under two minutes
- Rewrite a train-test split and baseline DummyClassifier from memory
- Prepare a 90-second summary of your best ML project
Day 2Preprocessing and pipelines
- Build a ColumnTransformer pipeline for mixed numeric and categorical data
- List five data leakage examples and how to prevent each
- Practise encoding choices for low and high cardinality features
- Revise outlier detection with IQR and Isolation Forest
Day 3Regression and classification
- Revise linear regression assumptions, R-squared and RMSE versus MAE
- Derive precision, recall and F1 from a sample confusion matrix
- Practise threshold tuning and class imbalance answers
- Code a logistic regression with classification report in scikit-learn
Day 4Trees and ensembles
- Explain bagging versus boosting with a diagram on paper
- Train LightGBM with early stopping on a sample dataset
- Compute permutation importance and one SHAP waterfall plot
- Compare XGBoost, LightGBM and CatBoost in a short table
Day 5Unsupervised learning and evaluation
- Run k-means with elbow and silhouette, then try DBSCAN
- Apply PCA and explain the explained variance ratio
- Revise cross-validation types and when to use each
- Practise answering why CV and test scores differ
Day 6Deployment and MLOps
- Serve a saved pipeline with FastAPI and test it with curl
- Write a basic Dockerfile and explain each line
- Revise data drift, concept drift and PSI thresholds
- Prepare an answer on monitoring a model in production
Day 7Mock interview and HR
- Take a timed mock with 20 mixed questions from this page
- Prepare three STAR stories: failure, conflict and impact
- Review weak areas noted during the week
- Re-read the 30 revision points and sleep early
Frequently asked questions
Can I prepare for a Machine Learning interview in 7 days?
Seven days is enough to revise and sharpen, not to learn from zero. Use the plan to cover each core topic once, practise questions daily and finish with a mock interview.
How do I add questions to the plan?
On any question page choose Save to → 7-Day Job Prep. Saved questions show up at the top of this page.
How many hours a day should I study?
Two to four focused hours a day works for most people: one hour of revision, one to two hours of questions, and the rest on projects or mock answers.
Questions to practise each day
Company-wise Machine Learning questionsExperience-wise Machine Learning questionsRole-wise Machine Learning questionsCity-wise Machine Learning questionsMost asked Machine Learning questionsScenario-based questionsPractical / hands-on questionsHR & behavioural questionsMachine Learning mock interviewOnline assessment questionsTechnical round questionsManagerial round questionsMachine Learning topic sheets