WorksheetsExploring Machine Learning Concepts
Total questions: 10
Worksheet time: 5mins
What does 'Machine Learning' mean? A. Learning machines B. Algorithms that learn from data C. Robots only D. Excel formulas
D. Machines that think
C. Software for automation
B. Algorithms that learn from data
A. Data processing tools
Which library is used for ML in Python? A. NumPy B. Matplotlib C. scikit-learn D. Pandas
G. OpenCV
C. scikit-learn
F. Keras
E. TensorFlow
What is 'Training Data'? A. Raw Data B. Data used to teach the model C. Testing Data D. Random sample
E. Data for model evaluation
F. Data collected from users
B. Data used to teach the model
G. Data for model validation
Which chart shows correlation between 2 variables? A. Bar B. Pie C. Scatter D. Boxplot
B. Area
C. Scatter
A. Line
D. Histogram
Which of these tools is for dashboards? A. Power BI B. TensorFlow C. NumPy D. MySQL
Google Sheets
Apache Spark
A. Power BI
Tableau
What output does a regression model give? A. Category B. Number C. Image D. Text
A. Color
B. Number
C. Video
D. Sound
What do you expect to learn this week? Open ended —
Effective communication strategies and their applications.
Basic writing techniques and their limitations.
Advanced negotiation tactics and their drawbacks.
Fundamental presentation skills and their challenges.
What is the purpose of a validation set in ML? A. To train the model B. To evaluate the model C. To store data D. To visualize results
B. To evaluate the model
C. To preprocess data
A. To optimize hyperparameters
D. To increase model complexity
What is overfitting in machine learning? A. Model performs well on training data but poorly on unseen data B. Model performs well on unseen data but poorly on training data C. Model is too simple D. Model is too complex
Model is too rigid and cannot learn patterns
Model is accurate on both training and unseen data
Model has insufficient data for training
A. Model performs well on training data but poorly on unseen data
What is the role of features in a dataset? A. They are the output B. They are the input variables C. They are the labels D. They are the errors
A. They are the predictions
C. They are the outcomes
B. They are the input variables
D. They are the features' weights
