Question 1

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  Among the many metrics for regression estimators is the model's coefficient of determination, which is also called the R2 score. 
B)  To calculate an estimator's R2 score, use the sklearn.metrics module's r2_score function with the arrays representing the expected and predicted results, as in: In [44]: from sklearn import metrics
In [45]: metrics.r2_score(expected, predicted)
Out[45]: 0.6008983115964333 
C)  R2 scores range from 0.0 to 1.0 with 1.0 being the best. An R2 score of 1.0 indicates that the estimator perfectly predicts the independent variable's value, given the dependent variable(s) value(s). An R2 score of 0.0 indicates the model cannot make predictions with any accuracy, based on the independent variables' values. 
D)  All of the above statements are true. 
A)  Among the many metrics for regression estimators is the model's coefficient of determination, which is also called the R2 score. 
B)  To calculate an estimator's R2 score, use the sklearn.metrics module's r2_score function with the arrays representing the expected and predicted results, as in: In [44]: from sklearn import metrics
In [45]: metrics.r2_score(expected, predicted)
Out[45]: 0.6008983115964333 
C)  R2 scores range from 0.0 to 1.0 with 1.0 being the best. An R2 score of 1.0 indicates that the estimator perfectly predicts the independent variable's value, given the dependent variable(s) value(s). An R2 score of 0.0 indicates the model cannot make predictions with any accuracy, based on the independent variables' values. 
D)  All of the above statements are true.

Question 2

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  The following code uses function xe "sklearn.model_selection module:cross_val_score function"xe "cross_val_score function sklearn.model_selection"cross_val_score to train and test a model: from sklearn.model_selection import cross_val_score
Scores = cross_val_score(estimator=knn, X=digits.data,
Y=digits.target, cv=kfold) 
B)  The keyword arguments in Part (a) are: $\bullet$estimator=knn, which specifies the estimator you'd like to validate.
$\bullet$X=digits.data, which specifies the samples to use for training and testing.
$\bullet$y=digits.target, which specifies the targets for the samples.
$\bullet$cv=kfold, which specifies the cross-validation generator that defines how to split the samples and targets for training and testing. 
C)  Function cross_val_score returns a single overall accuracy score for the model. 
D)  All of the above statements are true. 
A)  The following code uses function xe "sklearn.model_selection module:cross_val_score function"xe "cross_val_score function sklearn.model_selection"cross_val_score to train and test a model: from sklearn.model_selection import cross_val_score
Scores = cross_val_score(estimator=knn, X=digits.data,
Y=digits.target, cv=kfold) 
B)  The keyword arguments in Part (a) are: $\bullet$estimator=knn, which specifies the estimator you'd like to validate.
$\bullet$X=digits.data, which specifies the samples to use for training and testing.
$\bullet$y=digits.target, which specifies the targets for the samples.
$\bullet$cv=kfold, which specifies the cross-validation generator that defines how to split the samples and targets for training and testing. 
C)  Function cross_val_score returns a single overall accuracy score for the model. 
D)  All of the above statements are true.

Question 3

In the context of the California Housing dataset, which of the following statements is false?

Accepted Answer

A)  The following code creates a LinearRegression estimator and invokes its xe "scikit-learn (sklearn) machine-learning library:fit method of an estimator"xe "fit method:of a scikit-learn estimator"fit method to train the estimator using X_train (the samples) and y_train (the targets): from sklearn.linear_model import LinearRegression
Linear_regression = LinearRegression()
Linear_regression.fit(X=X_train, y=y_train) 
B)  Multiple linear regression produces separate coefficients for each feature (stored in coeff_) in the dataset and one intercept (stored in intercept_). 
C)  For positive coefficients, the median house value increases as the feature value increases. For negative coefficients, the median house value decreases as the feature value decreases. 
D)  You can use the coefficient and intercept values with the following equation to make predictions: y = m1x1 + m2x2 + … mnxn + b
Where
$\bullet$m1, m2, …, mn are the feature coefficients
$\bullet$b is the intercept
$\bullet$x1, x2, …, xn are the feature values (that is, the values of the independent variables)
$\bullet$y is the predicted value (that is, the xe "dependent variable"dependent variable) 
A)  The following code creates a LinearRegression estimator and invokes its xe "scikit-learn (sklearn) machine-learning library:fit method of an estimator"xe "fit method:of a scikit-learn estimator"fit method to train the estimator using X_train (the samples) and y_train (the targets): from sklearn.linear_model import LinearRegression
Linear_regression = LinearRegression()
Linear_regression.fit(X=X_train, y=y_train) 
B)  Multiple linear regression produces separate coefficients for each feature (stored in coeff_) in the dataset and one intercept (stored in intercept_). 
C)  For positive coefficients, the median house value increases as the feature value increases. For negative coefficients, the median house value decreases as the feature value decreases. 
D)  You can use the coefficient and intercept values with the following equation to make predictions: y = m1x1 + m2x2 + … mnxn + b
Where
$\bullet$m1, m2, …, mn are the feature coefficients
$\bullet$b is the intercept
$\bullet$x1, x2, …, xn are the feature values (that is, the values of the independent variables)
$\bullet$y is the predicted value (that is, the xe "dependent variable"dependent variable)

Question 4

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  One way to learn more about your data is to see how the features relate to one another. 
B)  The samples in the Iris dataset each have four features. 
C)  We cannot graph one feature against the other three in a single graph. But we can plot pairs of features against one another in a pairplot. 
D)  All of the above statements are true. 
A)  One way to learn more about your data is to see how the features relate to one another. 
B)  The samples in the Iris dataset each have four features. 
C)  We cannot graph one feature against the other three in a single graph. But we can plot pairs of features against one another in a pairplot. 
D)  All of the above statements are true.

Question 5

Which of the following statements is false?

Accepted Answer

A)  Scikit-learn supports many classification algorithms, including the simplest-k-nearest neighbors (k-NN). 
B)  The k-nearest neighbors algorithm attempts to predict a test sample's class by looking at the k training samples that are nearest (in distance) to the test sample. 
C)  Always pick an even value of k for the k-nearest neighbors algorithm. 
D)  In the k-nearest neighbors algorithm, the class with the most "votes" wins. 
A)  Scikit-learn supports many classification algorithms, including the simplest-k-nearest neighbors (k-NN). 
B)  The k-nearest neighbors algorithm attempts to predict a test sample's class by looking at the k training samples that are nearest (in distance) to the test sample. 
C)  Always pick an even value of k for the k-nearest neighbors algorithm. 
D)  In the k-nearest neighbors algorithm, the class with the most "votes" wins.

Question 6

Which of the following statements is false?

Accepted Answer

A)  Another way to check a classification estimator's accuracy is via a confusion matrix, which shows only the incorrect predicted values (also known as the misses) for a given class. 
B)  To create a confusion matrix imply call the function confusion_matrix from the sklearn.metrics module, passing the expected classes and the predicted classes as arguments, as in: from sklearn.metrics import confusion_matrix
Confusion = confusion_matrix(y_true=expected, y_pred=predicted) 
C)  The y_true keyword argument in Part (b) specifies the test samples' actual classes. 
D)  The y_pred keyword argument in Part (b) specifies the predicted classes for the test samples. 
A)  Another way to check a classification estimator's accuracy is via a confusion matrix, which shows only the incorrect predicted values (also known as the misses) for a given class. 
B)  To create a confusion matrix imply call the function confusion_matrix from the sklearn.metrics module, passing the expected classes and the predicted classes as arguments, as in: from sklearn.metrics import confusion_matrix
Confusion = confusion_matrix(y_true=expected, y_pred=predicted) 
C)  The y_true keyword argument in Part (b) specifies the test samples' actual classes. 
D)  The y_pred keyword argument in Part (b) specifies the predicted classes for the test samples.

Question 7

Which of the following are related to compressing a dataset's large number of features down to two for visualization purposes.

Accepted Answer

A)  dimensionality reduction 
B)  TSNE estimator 
C)  PCA estimator 
D)  All of the above. 
A)  dimensionality reduction 
B)  TSNE estimator 
C)  PCA estimator 
D)  All of the above.

Question 8

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  The PCA estimator (from the sklearn.decomposition module), like TSNE, performs dimensionality reduction. The PCA estimator uses an algorithm called xe "principal components analysis (PC
A) "principal component analysis to analyze a dataset's features and reduce them to the specified number of dimensions. 
B)  Like TSNE, a PCA estimator uses the keyword argument n_components to specify the number of dimensions, as in: from sklearn.decomposition import PCA
Pca = PCA(n_components=2, random_state=11) 
C)  The following snippet trains the PCA estimator and produces the reduced data by calling the PCA estimator's fit and transform methods: pca.fit(iris.data)
Iris_pca = pca.transform(iris.data) 
D)  All of the above statements are true. 
A)  The PCA estimator (from the sklearn.decomposition module), like TSNE, performs dimensionality reduction. The PCA estimator uses an algorithm called xe "principal components analysis (PC
A) "principal component analysis to analyze a dataset's features and reduce them to the specified number of dimensions. 
B)  Like TSNE, a PCA estimator uses the keyword argument n_components to specify the number of dimensions, as in: from sklearn.decomposition import PCA
Pca = PCA(n_components=2, random_state=11) 
C)  The following snippet trains the PCA estimator and produces the reduced data by calling the PCA estimator's fit and transform methods: pca.fit(iris.data)
Iris_pca = pca.transform(iris.data) 
D)  All of the above statements are true.

Question 9

Which of the following are not steps in a typical machine-learning case study?

Accepted Answer

A)  loading the dataset and exploring the data with pandas and visualizations 
B)  transforming your data (converting non-numeric data to numeric data because scikit-learn requires numeric data) and splitting the data for training and testing 
C)  creating, training and testing the model; tuning the model, evaluating its accuracy and making predictions on live data that the model hasn't seen before. 
D)  All of the above are steps in a typical machine-learning case study. 
A)  loading the dataset and exploring the data with pandas and visualizations 
B)  transforming your data (converting non-numeric data to numeric data because scikit-learn requires numeric data) and splitting the data for training and testing 
C)  creating, training and testing the model; tuning the model, evaluating its accuracy and making predictions on live data that the model hasn't seen before. 
D)  All of the above are steps in a typical machine-learning case study.

Question 10

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  Unsupervised machine learning and visualization can help you get to know your data by finding patterns and relationships among unlabeled samples. 
B)  Using Matplotlib, Seaborn and other visualization libraries, you can plot datasets with two or three variables using 2D and 3D visualizations, respectively. 
C)  In the Digits dataset, every sample has 64 features (and a target value), so there is no way to visualize the dataset. 
D)  All of the above statements are true. 
A)  Unsupervised machine learning and visualization can help you get to know your data by finding patterns and relationships among unlabeled samples. 
B)  Using Matplotlib, Seaborn and other visualization libraries, you can plot datasets with two or three variables using 2D and 3D visualizations, respectively. 
C)  In the Digits dataset, every sample has 64 features (and a target value), so there is no way to visualize the dataset. 
D)  All of the above statements are true.

Question 11

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  Once we've loaded our data into s KNeighborsClassifier, we can use it with the test samples to make predictions. Calling the estimator's predict method with the test samples (X_test) as an argument returns an array containing the predicted class of each sample: predicted = knn.predict(X=X_test) 
B)  If predicted and expected are arrays containing the predictions and expected target values, respectively, evaluating the following code snippets in IPython interactive mode displays the predicted and expected target values for the first 20 test samples: predicted[:20]
Expected[:20] 
C)  If predicted and expected are arrays containing the predictions and expected target values, respectively, the following list comprehension locates all the incorrect predictions for the entire test set-that is, the cases in which the predicted and expected values do not match: wrong = [(p, e) for (p, e) in zip(predicted, expected) if p != e] 
D)  All of the above statements are true. 
A)  Once we've loaded our data into s KNeighborsClassifier, we can use it with the test samples to make predictions. Calling the estimator's predict method with the test samples (X_test) as an argument returns an array containing the predicted class of each sample: predicted = knn.predict(X=X_test) 
B)  If predicted and expected are arrays containing the predictions and expected target values, respectively, evaluating the following code snippets in IPython interactive mode displays the predicted and expected target values for the first 20 test samples: predicted[:20]
Expected[:20] 
C)  If predicted and expected are arrays containing the predictions and expected target values, respectively, the following list comprehension locates all the incorrect predictions for the entire test set-that is, the cases in which the predicted and expected values do not match: wrong = [(p, e) for (p, e) in zip(predicted, expected) if p != e] 
D)  All of the above statements are true.

Question 12

Which of the following statements is false?

Accepted Answer

A)  Scikit-learn provides the KFold class and the cross_val_score function (both in the module sklearn.model_selection) to help you perform the training and testing cycles. 
B)  The following code creates a KFold object: from sklearn.model_selection import KFold
Kfold = KFold(n_folds=10, random_state=11, shuffle=True) 
C)  The keyword argument random_state=11 seeds the random number generator for xe "reproducibility"reproducibility. 
D)  The keyword argument shuffle=True causes the KFold object to randomize the data by shuffling it before splitting it into folds. This is particularly important if the samples might be ordered or grouped. 
A)  Scikit-learn provides the KFold class and the cross_val_score function (both in the module sklearn.model_selection) to help you perform the training and testing cycles. 
B)  The following code creates a KFold object: from sklearn.model_selection import KFold
Kfold = KFold(n_folds=10, random_state=11, shuffle=True) 
C)  The keyword argument random_state=11 seeds the random number generator for xe "reproducibility"reproducibility. 
D)  The keyword argument shuffle=True causes the KFold object to randomize the data by shuffling it before splitting it into folds. This is particularly important if the samples might be ordered or grouped.

Question 13

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  In real-world machine-learning applications, it can often take minutes, hours, days or even months to train your models-special-purpose, high-performance hardware called GPUs and TPUs can significantly reduce model training time. 
B)  The fit method returns the estimator object. 
C)  For simplicity, we generally use the default estimator settings-by default, a KNeighborsClassifier looks at the three nearest neighbors to make its predictions. 
D)  All of the above statements are true. 
A)  In real-world machine-learning applications, it can often take minutes, hours, days or even months to train your models-special-purpose, high-performance hardware called GPUs and TPUs can significantly reduce model training time. 
B)  The fit method returns the estimator object. 
C)  For simplicity, we generally use the default estimator settings-by default, a KNeighborsClassifier looks at the three nearest neighbors to make its predictions. 
D)  All of the above statements are true.

Question 14

Which of the following statements about scikit-learn and the machine-learning models you'll build with it is false?

Accepted Answer

A)  It's difficult to know in advance which model(s) will perform best on your data, so you typically try many models and pick the one that performs best-scikit-learn makes this convenient for you. 
B)  You'll rarely get to know the details of the complex mathematical algorithms in the scikit-learn estimators, but with experience, you'll be able to intuit the best model for each new dataset. 
C)  It generally takes at most a few lines of code for you to create and use each scikit-learn model. 
D)  The models report their performance so you can compare the results and pick the model(s) with the best performance. 
A)  It's difficult to know in advance which model(s) will perform best on your data, so you typically try many models and pick the one that performs best-scikit-learn makes this convenient for you. 
B)  You'll rarely get to know the details of the complex mathematical algorithms in the scikit-learn estimators, but with experience, you'll be able to intuit the best model for each new dataset. 
C)  It generally takes at most a few lines of code for you to create and use each scikit-learn model. 
D)  The models report their performance so you can compare the results and pick the model(s) with the best performance.

Question 15

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  You typically train a machine-learning model with a subset of a dataset. 
B)  Generally, you should train your model with the smallest amount of data that makes the model perform well. 
C)  It's important to set aside a portion of your data for testing, so you can evaluate a model's performance using data that the model has not yet seen. Once you're confident that the model is performing well, you can use it to make predictions using new data. 
D)  All of the above statements are true. 
A)  You typically train a machine-learning model with a subset of a dataset. 
B)  Generally, you should train your model with the smallest amount of data that makes the model perform well. 
C)  It's important to set aside a portion of your data for testing, so you can evaluate a model's performance using data that the model has not yet seen. Once you're confident that the model is performing well, you can use it to make predictions using new data. 
D)  All of the above statements are true.

Question 16

Consider the confusion matrix for the Digits dataset's predictions: array([[45, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 45, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 54, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 42, 0, 1, 0, 1, 0, 0],
[ 0, 0, 0, 0, 49, 0, 0, 1, 0, 0],
[ 0, 0, 0, 0, 0, 38, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 42, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 45, 0, 0],
[ 0, 1, 1, 2, 0, 0, 0, 0, 39, 1],
[ 0, 0, 0, 0, 1, 0, 0, 0, 1, 41]])
Which of the following statements is false?

Accepted Answer

A)  The correct predictions are shown on the diagonal from top-left to bottom-right-this is called the principal diagonal. 
B)  The nonzero values that are not on the principal diagonal indicate incorrect predictions (that is, misses). 
C)  Each row represents one distinct class-that is, one of the digits 0-9. 
D)  The columns within a row specify how many of the test samples were classified incorrectly into each distinct class 0-9. 
A)  The correct predictions are shown on the diagonal from top-left to bottom-right-this is called the principal diagonal. 
B)  The nonzero values that are not on the principal diagonal indicate incorrect predictions (that is, misses). 
C)  Each row represents one distinct class-that is, one of the digits 0-9. 
D)  The columns within a row specify how many of the test samples were classified incorrectly into each distinct class 0-9.

Question 17

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  Each estimator has a score method that returns an indication of how well the estimator performs for the test data you pass as arguments. 
B)  For classification estimators, the score method returns the xe "prediction:accuracy"prediction accuracy for the test data. 
C)  You can perform hyperparameter tuning to try to determine the optimal value for k. 
D)  All of the above statements are true. 
A)  Each estimator has a score method that returns an indication of how well the estimator performs for the test data you pass as arguments. 
B)  For classification estimators, the score method returns the xe "prediction:accuracy"prediction accuracy for the test data. 
C)  You can perform hyperparameter tuning to try to determine the optimal value for k. 
D)  All of the above statements are true.

Question 18

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  The following code tests a model by calling the estimator's xe "scikit-learn (sklearn) machine-learning library:predict method of an estimator"xe "predict method of a scikit-learn estimator"predict method with the test samples as an argument: predicted = linear_regression.predict(X_test) 
B)  Assuming the array expected contains the expected values for the samples used to make predictions in Part (a)'s snippet, evaluating the following snippets displays the first five predictions and their corresponding expected values: In [32]: predicted[:5]
Out[32]: array([1.25396876, 2.34693107, 2.03794745, 1.8701254 , 2.53608339])
In [33]: expected[:5]
Out[33]: array([0.762, 1.732, 1.125, 1.37 , 1.856]) 
C)  With classification, we saw that the predictions were distinct classes that matched existing classes in the dataset. With regression, it's tough to get exact predictions, because you have continuous outputs. Every possible value of x1, x2 … xn in the calculation y = m1x1 + m2x2 + … mnxn + b
Predicts a different value. 
D)  All of the above statements are true. 
A)  The following code tests a model by calling the estimator's xe "scikit-learn (sklearn) machine-learning library:predict method of an estimator"xe "predict method of a scikit-learn estimator"predict method with the test samples as an argument: predicted = linear_regression.predict(X_test) 
B)  Assuming the array expected contains the expected values for the samples used to make predictions in Part (a)'s snippet, evaluating the following snippets displays the first five predictions and their corresponding expected values: In [32]: predicted[:5]
Out[32]: array([1.25396876, 2.34693107, 2.03794745, 1.8701254 , 2.53608339])
In [33]: expected[:5]
Out[33]: array([0.762, 1.732, 1.125, 1.37 , 1.856]) 
C)  With classification, we saw that the predictions were distinct classes that matched existing classes in the dataset. With regression, it's tough to get exact predictions, because you have continuous outputs. Every possible value of x1, x2 … xn in the calculation y = m1x1 + m2x2 + … mnxn + b
Predicts a different value. 
D)  All of the above statements are true.

Question 19

Which of the following statements a), b) or c) is false?

Accepted Answer

A)  The Iris dataset bundled with scikit-learn is commonly analyzed with both classification and clustering. 
B)  Although the Iris dataset is labeled, we can ignore those labels to demonstrate clustering. Then, we can use the labels to determine how well the k-means algorithm clusters the samples. 
C)  The Iris dataset is referred to as a "toy dataset" because it has only 150 samples and four features. The dataset describes 50 samples for each of three Iris flower species-xe "Iris setosa"Iris setosa, xe "Iris versicolor"Iris versicolor and xe "Iris virginica"Iris virginica. 
D)  All of the above statements are true. 
A)  The Iris dataset bundled with scikit-learn is commonly analyzed with both classification and clustering. 
B)  Although the Iris dataset is labeled, we can ignore those labels to demonstrate clustering. Then, we can use the labels to determine how well the k-means algorithm clusters the samples. 
C)  The Iris dataset is referred to as a "toy dataset" because it has only 150 samples and four features. The dataset describes 50 samples for each of three Iris flower species-xe "Iris setosa"Iris setosa, xe "Iris versicolor"Iris versicolor and xe "Iris virginica"Iris virginica. 
D)  All of the above statements are true.

Question 20

Which of the following statements is false?

Accepted Answer

A)  k-means clustering is perhaps the simplest unsupervised machine learning algorithm. 
B)  The k-means clustering algorithm analyzes unlabeled samples and attempts to place them in clusters that appear to be related. 
C)  The k in "k-means" represents the number of clusters to impose on the data. 
D)  The k-means clustering algorithm organizes samples into the number of clusters you specify in advance, using distance calculations similar to the k-nearest neighbors clustering algorithm. 
A)  k-means clustering is perhaps the simplest unsupervised machine learning algorithm. 
B)  The k-means clustering algorithm analyzes unlabeled samples and attempts to place them in clusters that appear to be related. 
C)  The k in "k-means" represents the number of clusters to impose on the data. 
D)  The k-means clustering algorithm organizes samples into the number of clusters you specify in advance, using distance calculations similar to the k-nearest neighbors clustering algorithm.

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

In the context of the California Housing dataset, which of the following statements is false?

Which of the following statements a), b) or c) is false?

Which of the following statements is false?

Which of the following statements is false?

Which of the following are related to compressing a dataset's large number of features down to two for visualization purposes.

Which of the following statements a), b) or c) is false?

Which of the following are not steps in a typical machine-learning case study?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements is false?

Which of the following statements a), b) or c) is false?

Which of the following statements about scikit-learn and the machine-learning models you'll build with it is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements is false?

Introduction to Computers and Python

Introduction to Python Programming

Control Statements and Program Development

Functions

Sequences: Lists and Tuples

Dictionaries and Sets

Array-Oriented Programming With Num

Strings: a Deeper Look

Files and Exceptions

Object-Oriented Programming

Computer Science Thinking: Recursion, Searching, Sorting and Big O

Natural Language Processing

Data Mining Twitter

Ibm Watson and Cognitive Computing

Deep Learning

Big Data: Hadoop, Spark, Nosql and Iot

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Exam 15: Machine Learning: Classification, Regression and Clustering

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

In the context of the California Housing dataset, which of the following statements is false?

Which of the following statements a), b) or c) is false?

Which of the following statements is false?

Which of the following statements is false?

Which of the following are related to compressing a dataset's large number of features down to two for visualization purposes.

Which of the following statements a), b) or c) is false?

Which of the following are not steps in a typical machine-learning case study?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements is false?

Which of the following statements a), b) or c) is false?

Which of the following statements about scikit-learn and the machine-learning models you'll build with it is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements is false?

Introduction to Computers and Python

Introduction to Python Programming

Control Statements and Program Development

Functions

Sequences: Lists and Tuples

Dictionaries and Sets

Array-Oriented Programming With Num

Strings: a Deeper Look

Files and Exceptions

Object-Oriented Programming

Computer Science Thinking: Recursion, Searching, Sorting and Big O

Natural Language Processing

Data Mining Twitter

Ibm Watson and Cognitive Computing

Deep Learning

Big Data: Hadoop, Spark, Nosql and Iot

Filters