Python ML ecosystem overview in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key libraries for data manipulation in Python ML

Which Python library is primarily used for handling and analyzing structured data like tables?

A NumPy

B Pandas

C Scikit-learn

D Matplotlib

❓ Predict Output

intermediate

Output of a simple NumPy array operation

What is the output of this code?

ML Python

import numpy as np
arr = np.array([1, 2, 3])
result = arr * 2
print(result.tolist())

A [2, 4, 6]

B [1, 2, 3, 1, 2, 3]

C [1, 4, 9]

D TypeError

❓ Model Choice

advanced

Choosing the right library for deep learning

You want to build a deep neural network with GPU support and easy model building. Which Python library is best suited?

A TensorFlow

B Scikit-learn

C Pandas

D Matplotlib

❓ Metrics

advanced

Understanding model evaluation metrics in scikit-learn

Which metric from scikit-learn is best to evaluate a classification model's ability to balance precision and recall?

A Accuracy Score

B Mean Squared Error

C F1 Score

D R-squared

🔧 Debug

expert

Identifying the error in a scikit-learn pipeline code

What error does this code raise?

ML Python

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', LogisticRegression())
])

pipe.fit([[1, 2], [3, 4]], [0, 1])

A ValueError: Found input variables with inconsistent numbers of samples

B TypeError: fit() missing 1 required positional argument

C AttributeError: 'Pipeline' object has no attribute 'fit'

D No error, code runs successfully