Python ML ecosystem overview in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the popular library for numerical computing in Python.

ML Python

import [1] as np

Drag options to blanks, or click blank then click option'

Anumpy

Bpandas

Cmatplotlib

Dsklearn

Attempts:

3 left

💡 Hint

Common Mistakes

Using pandas instead of numpy for numerical arrays.

Trying to import sklearn as np.

numpy is the main library for numerical computing in Python. It provides arrays and math functions.

2fill in blank

medium

Complete the code to load a dataset from scikit-learn.

ML Python

from sklearn.datasets import [1]
data = [1]()

Drag options to blanks, or click blank then click option'

Aload_iris

Bdatasets

Cload_digits

Dload_boston

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'datasets' instead of 'load_iris' as function.

Confusing load_iris with load_digits.

The load_iris function loads the Iris flower dataset, a classic ML dataset.

3fill in blank

hard

Fix the error in the code to create a simple linear regression model.

ML Python

from sklearn.linear_model import [1]
model = [1]()

Drag options to blanks, or click blank then click option'

ALogisticRegression

BRandomForestRegressor

CDecisionTreeClassifier

DLinearRegression

Attempts:

3 left

💡 Hint

Common Mistakes

Using LogisticRegression which is for classification.

Using DecisionTreeClassifier which is a classifier.

LinearRegression is the correct class to create a linear regression model.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths if length is greater than 3.

ML Python

{word: [1] for word in words if [2]

Drag options to blanks, or click blank then click option'

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for the value.

Using word > 3 which compares strings to number.

The dictionary maps each word to its length using len(word). The condition keeps words longer than 3 characters with len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than zero.

ML Python

result = { [1]: [2] for k, v in data.items() if v [3] 0 }

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys.

Using < instead of > in the condition.

The dictionary comprehension uses k.upper() for keys, v for values, and filters values greater than zero with >.

Practice

1. Which Python library is best known for simple and easy-to-use machine learning algorithms like classification and regression?

easy

A. Matplotlib

B. scikit-learn

C. Pandas

D. TensorFlow

Solution

1. Step 1: Identify libraries for ML algorithms

   scikit-learn is designed for easy access to classic ML algorithms like classification and regression.

2. Step 2: Differentiate from other libraries

   TensorFlow is for deep learning, Pandas is for data handling, and Matplotlib is for plotting.

3. Final Answer:

   scikit-learn -> Option B

4. Quick Check:

   Simple ML algorithms = scikit-learn [OK]

Hint: Classic ML? Think scikit-learn first [OK]

Common Mistakes:

• Confusing TensorFlow with scikit-learn
• Choosing Pandas as ML library
• Picking Matplotlib for modeling

2. Which of the following is the correct way to import the Pandas library in Python?

easy

A. import pandas as pd

B. import pandas as pn

C. from pandas import numpy

D. import pd from pandas

Solution

1. Step 1: Recall standard import syntax for Pandas

   The common convention is to import pandas as pd for easy reference.

2. Step 2: Check other options for syntax errors or wrong aliases

   import pandas as pn uses wrong alias, C tries to import numpy from pandas which is invalid, D has incorrect import syntax.

3. Final Answer:

   import pandas as pd -> Option A

4. Quick Check:

   Standard Pandas import = import pandas as pd [OK]

Hint: Pandas is always imported as pd [OK]

Common Mistakes:

• Using wrong alias like pn
• Incorrect import syntax
• Confusing pandas with numpy

3. What will be the output of this code snippet?

from sklearn.linear_model import LinearRegression
model = LinearRegression()
print(type(model))

medium

A. TypeError

B. <class 'LinearRegression'>

C. NameError

D. <class 'sklearn.linear_model._base.LinearRegression'>

Solution

1. Step 1: Understand the import and object creation

   The code imports LinearRegression from sklearn.linear_model and creates an instance named model.

2. Step 2: Check what print(type(model)) outputs

   It prints the full class path including module name, which is <class 'sklearn.linear_model._base.LinearRegression'>.

3. Final Answer:

   <class 'sklearn.linear_model._base.LinearRegression'> -> Option D

4. Quick Check:

   Type of model = sklearn.linear_model._base.LinearRegression [OK]

Hint: type() shows full class path in sklearn [OK]

Common Mistakes:

• Expecting just class name without module path
• Confusing with error outputs
• Missing import causing NameError

4. Identify the error in this code snippet:

import numpy as np
from sklearn.model_selection import train_test_split
X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([1, 2, 3])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
print(X_train.shape, X_test.shape)

medium

A. train_test_split is not imported correctly

B. Shapes printed are incorrect due to wrong test_size

C. No error, code runs correctly

D. X and y arrays have mismatched lengths

Solution

1. Step 1: Check imports and data arrays

   numpy and train_test_split are imported correctly; X has 3 samples, y has 3 labels matching in length.

2. Step 2: Verify train_test_split usage and output

   test_size=0.25 splits 3 samples into 2 train and 1 test samples; shapes printed are correct.

3. Final Answer:

   No error, code runs correctly -> Option C

4. Quick Check:

   Matching array lengths and correct split = no error [OK]

Hint: Check array lengths and import correctness first [OK]

Common Mistakes:

• Assuming train_test_split import is wrong
• Thinking test_size must be integer
• Ignoring matching lengths of X and y

5. You want to build a deep learning model for image classification. Which Python library should you choose to start with, considering ease of use and community support?

hard

A. TensorFlow with Keras

B. scikit-learn

C. Pandas

D. Matplotlib

Solution

1. Step 1: Identify libraries suited for deep learning

   TensorFlow with Keras is widely used for deep learning and image classification tasks.

2. Step 2: Compare with other options

   scikit-learn is for classic ML, Pandas is for data handling, Matplotlib is for plotting, none are designed for deep learning.

3. Final Answer:

   TensorFlow with Keras -> Option A

4. Quick Check:

   Deep learning + images = TensorFlow with Keras [OK]

Hint: Deep learning? Think TensorFlow + Keras [OK]

Common Mistakes:

• Choosing scikit-learn for deep learning
• Confusing Pandas or Matplotlib as modeling tools
• Ignoring Keras as high-level API