Launching into Machine Learning Coursera Quiz Answers

All Weeks Launching into Machine Learning Coursera Quiz Answers

Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.

Enroll in Launching into Machine Learning Coursera

Launching into Machine Learning Week 01 Quiz Answers

Practice Quiz on Improve Data Quality Quiz Answers

Q1. Which of the following is not a Data Quality attribute?

  • Consistency
  • Accuracy
  • Auditability
  • Redundancy

Q2. Which of the following are categories of data quality tools?

  • Cleaning tools
  • Monitoring tools
  • Both A and B
  • None of the above

Q3. What are the features of low data quality?

  • Unreliable info
  • Incomplete data
  • Duplicated data
  • All of the above

Q4. Which of the following refers to the Orderliness of data?

  • The data record with specific details appears only once in the database
  • The data entered has the required format and structure
  • The data represents reality within a reasonable period
  • None of the above

Q5. Which of the following are best practices for data quality management?

  • Resolving missing values
  • Preventing duplicates
  • Automating data entry
  • All of the above

Practice Quiz on Exploratory Data Analysis Quiz Answers

Q1. Which is the correct sequence of steps in data analysis and data visualization of Exploratory Data Analysis?

  • Data Exploration -> Data Cleaning -> Present Results -> Model Building
  • Data Exploration -> Data Cleaning -> Model Building -> Present Results
  • Data Exploration -> Model Building -> Present Results -> Data Cleaning
  • Data Exploration -> Model Building -> Data Cleaning -> Present Results

Q2. What are the objectives of exploratory data analysis?

  • Check for missing data and other mistakes.
  • Gain maximum insight into the data set and its underlying structure.
  • Uncover a parsimonious model, one which explains the data with a minimum number of predictor variables.
  • All of the above

Q3. Which of the following is not true about Exploratory Data Analysis?

  • Generates a posteriori hypothesis.
  • Discovers new knowledge.
  • Does not provide insights into the data
  • deals with unknown

Q4. Exploratory Data Analysis is majorly performed using the following methods:

  • Univariate Bivariate
  • Both A and B
  • None of the above

5. Which of the following is not a component of Exploratory Data Analysis?

  • Accounting and Summarizing
  • Anomaly Detection
  • Statistical Analysis and Clustering
  • Hyperparameter tuning

Launching into Machine Learning Week 02 Quiz Answers

Supervised Learning Quiz Answers

Q1. Which of the following machine learning models have labels, or in other words, the correct answers to whatever it is that we want to learn to predict?

  • Unsupervised Model
  • Supervised Model
  • Reinforcement Model
  • None of the above.

Q2. Which statement is true?

  • Depending on the problem you are trying to solve, the data you have, explainability, etc. will not determine which machine learning methods you use to find a solution.
  • Depending on the problem you are trying to solve, the data you have, explainability, etc. will determine which machine learning methods you use to find a solution.
  • Determining which machine learning methods you use to find a solution depends only on the problem or hypothesis.
  • None of the above

Q3. What is a type of Supervised machine learning model?

  • Regression model.
  • Classification model.
  • Both a & b
  • None of the above

Q4. Which model would you use if your problem required a discrete number of values or classes?

  • Regression Model
  • Unsupervised Model
  • Supervised Model
  • Classification Model

Q5. When the data isn’t labeled, what is an alternative way of predicting the output?

  • Clustering Algorithms
  • Linear regression
  • Logistic regression
  • None of the above

Regression and Classification Quiz Answers

Q1. We can minimize the error between our predicted continuous value and the label’s continuous value using which model?

  • Regression
  • Classification
  • Both A and B
  • None of the above.

Q2. To predict the continuous value of our label, which of the following algorithm is used?

  • Classification
  • Regression
  • Unsupervised
  • None of the above

Q3. If we want to minimize the error or misclassification between our predicted class and the labels class, which of the following models can be used?

  • Regression
  • Categorical
  • Classification
  • None of the above

Q4. Let’s say we want to predict the gestation weeks of a baby, what kind of machine learning model can be used?

  • Categorical
  • Regression
  • Classification
  • None of the above

Q5. What is the most essential metric a regression model uses?

  • Mean squared error as their loss function
  • Cross entropy
  • Both a & b
  • None of the above

Linear Regression Quiz Answers

Q1. Fill in the blanks. In the video, we presented a linear equation. This hypothesis equation is applied to every _________ of our dataset, where the weight values are fixed, and the feature values are from each associated column, and our machine learning data set.

  • Row
  • Column
  • Row and Column
  • None of the above

Q2. Which of the following statements is true?

  • Typically, for linear regression problems , the loss function is Mean Squared Error.
  • Typically, for classification problems , the loss function is Mean Squared Error.
  • Both A and B
  • None of the above

3. Fill in the blanks. Fundamentally, classification is about predicting a _______ and regression is about predicting a __________.

  • Quantity, Label
  • RMSE, Label
  • Log Loss, Label
  • Label, Quantity

4. True or False: Classification is the problem of predicting a discrete class label output for an example, while regression is the problem of predicting a continuous quantity output for an example.

  • False.
  • True.

Perceptron Quiz Answers

Q1. Which of the following is an algorithm for supervised learning of binary classifiers – given that a binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class.

  • Binary classifier
  • Perceptron
  • Linear regression
  • None of the above

Q2. Which model is the linear classifier, also used in Supervised learning?

  • Neuron
  • Dendrites
  • Perceptron
  • All of the above

Q3. Which of the following statements is correct?

  • A perceptron is a type of sequential classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
  • A perceptron is a type of modular classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
  • A perceptron is a type of monitoring classifier.
  • A perceptron is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.

Q4. What are the steps involved in the Perceptron Learning Process?

  • Takes the inputs, multiplies them by their weights, and computes their sum.
  • Adds a bias factor, the number 1 multiplied by a weight.
  • Feeds the sum through the activation function.
  • All of the above

Q5. What are the elements of a perception?

  • Input function x
  • Bias b
  • Activation function
  • All of the above

Neural Networks Quiz Answers

Q1. Which of the following activation functions are used for nonlinearity?

  • Sigmoid
  • Hyperbolic tangent
  • Tanh
  • All of the above

Q2. A single unit for a non-input neuron has ____________________ a/an.

  • Weighted Sum
  • Activation function
  • Output of the activation function
  • All of the above

Q3. If we wanted our outputs to be in the form of probabilities, which activation function should I choose in the final layer?

  • Sigmoid
  • Tanh
  • ReLU
  • ELU

Q4. Which activation functions are needed to get the complex chain functions that allow neural networks to learn data distributions.

  • Linear activation functions
  • Nonlinear activation functions
  • All of the above
  • None of the above

Q5. Which activation function has a range between zero and Infinity?

  • Sigmoid
  • Tanh
  • ReLU
  • ELU

Decision Trees Quiz Answers

Q1. Decision trees are one of the most intuitive machine learning algorithms. They can be used for which of the following?

  • Classification
  • Regression
  • Both A and B
  • None of the above

Q2. In a decision classification tree, what does each decision or node consist of?

  • Linear classifier of all features
  • Mean squared error minimizer
  • Linear classifier of one feature
  • Euclidean distance minimizer

Q3. A random forest is usually more complex than an individual decision tree; this makes it harder to visually interpret?

  • True
  • False

Q4. Which of the following statements is true?

  • Mean squared error minimizer and euclidean distance minimizer are used in regression and classification.
  • Mean squared error minimizer and euclidean distance minimizer are used in regression, not classification.
  • Mean squared error minimizer and euclidean distance minimizer are not used in regression and classification.
  • Mean squared error minimizer and euclidean distance minimizer are used in classification, not regression.

Kernel Methods Quiz Answers

Q1. Which of the following is the distance between two separate vectors?

  • Margin
  • Space
  • New Line
  • None of the above

Q2. Which of the following statements is true about a decision boundary?

  • The more generalizable the decision boundary, the wider the margin.
  • The less generalizable the decision boundary, the wider the margin.
  • The more generalizable the decision boundary, the less the margin.
  • None of the above

Q3. Which of the following statements is true about Support Vector Machines (SVM)?

  • Support Vector Machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression. SVM are used for text classification tasks such as category assignment, detecting spam, and sentiment analysis.
  • SVMs are based on the idea of finding a hyperplane that best divides a dataset into two classes. Support vectors are the data points nearest to the hyperplane, the points of a data set that, if removed, would alter the position of the dividing hyperplane. As a simple example, for a classification task with only two features, you can think of a hyperplane as a line that linearly separates and classifies a set of data.
  • Both A and B
  • None of the above

Q4. What is the significance of kernel transformation?

  • It maps the data from our input vector space to a vector space that has features that can be linearly separated.
  • It transforms the data from our input vector space to a vector space.
  • Both A and B
  • None of the above

Q5. Which statement is true regarding kernel methods?

  • In machine learning, kernel methods are a class of algorithms for network infrastructure analysis, whose best known member is the support vector machine (SVM).
  • In machine learning, kernel methods are a class of algorithms for cloud protocol analysis, whose best known member is the support vector machine (SVM).
  • In machine learning, kernel methods are a class of algorithms for protocol analysis, whose best known member is the support vector machine (SVM).
  • In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM).

History of ML: Modern Neural Networks Quiz Answers

Q1. Which statement is true regarding the “dropout technique” used in neural networks?

  • Dropout is a technique used to prevent a model from overfitting. Dropout works by randomly setting the outgoing edges of hidden units (neurons that make up hidden layers) to 0 at each update of the training phase.
  • Dropout is a technique used to prevent a model from underfitting. Dropout works by randomly setting the outgoing edges of hidden units (neurons that make up hidden layers) to 0 at each update of the training phase.
  • Dropout is a technique used to prevent a model from overfitting. Dropout works by randomly setting the outgoing edges of hidden units (neurons that make up hidden layers) to 1 at each update of the training phase.
  • None of the above

Q2. Which of the following statements is true?

  • Dropout can help a model generalize by randomly setting the output for a given neuron to 0. In setting the output to 0, the cost function becomes more sensitive to neighbouring neurons changing the way the weights will be updated during the process of backpropagation.
  • Dropout can help a model generalize by randomly setting the output for a given neuron to 1. In setting the output to 1, the cost function becomes more sensitive to neighbouring neurons changing the way the weights will be updated during the process of backpropagation.
  • Both A and B
  • None of the above

Q3. Which statement is true regarding neural networks?

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.

  • Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns.
  • Neural networks interpret sensory data through a kind of machine perception, labeling or clustering raw input.
  • The patterns neural networks recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.
  • All of the above

Q4. Which of the following is not a type of modern neural network?

  • Convolutional Neural Network
  • Modular Neural Network
  • Recurrent Neural Network
  • Sine Neural Network

Q5. Which of the following are ways to improve generalization?

  • Adding dropout layers. Dropout is a technique used to prevent a model from overfitting. Dropout works by randomly setting the outgoing edges of hidden units (neurons that make up hidden layers) to 0 at each update of the training phase.
  • Performing data augmentation, which is a technique to artificially create new training data from existing training data. This is done by applying domain-specific techniques to examples from the training data that create new and different training examples.
  • Adding noise – for example, adding Gaussian noise to input variables. Gaussian noise, or white noise, has a mean of zero and a standard deviation of one and can be generated as needed using a pseudorandom number generator.
  • All of the above

Lesson Quiz Answers

Q1. For the formula used to model the relationship i.e. y = mx + b, what does ‘m’ stand for?

  • It captures the amount of change we’ve observed in our label in response to a small change in our feature.
  • It refers to a bias term that can be used for regression.
  • Both a & b
  • None of the above

Q2. What are the basic steps in an ML workflow (or process)?

  • Collect data
  • Check for anomalies, missing data and clean the data
  • Perform statistical analysis and initial visualization
  • All of the above

Q3. Which of the following statements is true?

  • To calculate the Predictiony for any Input value x we have three unknowns, the m = slope(Gradient), b = y-intercept(also called bias)andz = hyperplane.
  • To calculate the Predictiony for any Input value x we have three unknowns, the m = slope(Gradient), b = y-intercept(also called bias)andz = third degree polynomial.
  • To calculate the Predictiony for any Input value x we have two unknowns, the m = slope(Gradient) and b = y-intercept(also called bias).
  • None of the above

Launching into Machine Learning Week 03 Quiz Answers

Q1. Fill in the blanks: At its core, a ________ is a method of evaluating how well your algorithm models your dataset. If your predictions are totally off, your _________ will output a higher number. If they’re pretty good, it will output a lower number. As you change pieces of your algorithm to try and improve your model, your ______ will tell you if you’re getting anywhere.

  • Loss function
  • Activation functions
  • Bias term
  • Linear model

Q2. Fill in the blanks: Simply speaking, __________ is the workhorse of basic loss functions. ______ is the sum of squared distances between our target variable and predicted values.

  • Log loss
  • Mean Squared Error
  • Likelihood
  • None of the above

Q3. Loss functions can be broadly categorized into 2 types: Classification and Regression Loss. _____ is typically used for regression and ______ is typically used for classification.

  • Log Loss, Focus Loss
  • Cross Entropy, Log Loss
  • Mean Squared Error, Cross Entropy
  • None of the above

Q4. Which of the following loss functions is used for classification problems?

  • MSE
  • Cross entropy
  • Both a & b
  • None of the above

Lesson Quiz Answers

Q1. Select the correct statement(s) regarding gradient descent.

  • Gradient descent is an optimization algorithm used to maximize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.
  • Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.
  • In machine learning, we use gradient descent to determine if our model labels needs to be de-optimized.
  • All of the above

Q2. Select which statement is true.

  • Batch gradient descent, also called vanilla gradient descent, calculates the error for each example within the training dataset, but only before all training examples have been evaluated does the model get updated.
  • Batch gradient descent, also called vanilla gradient descent, calculates the gain for each example within the training dataset, but only before all training examples have been evaluated does the model get updated. This whole process is like a cycle and it’s called a training epoch.
  • Batch gradient descent, also called vanilla gradient descent, calculates the error for each example within the training dataset, but only after all training examples have been evaluated does the model get updated. This whole process is like a cycle and it’s called a training epoch.
  • None of the above

Q3. Fill in the blanks. In the ________________________ method, one training sample (example) is passed through the neural network at a time and the parameters (weights) of each layer are updated with the computed gradient.

  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Mini Batch Gradient Descent
  • None of the above

Q4. Which of the following gradient descent methods is used to compute the entire dataset?

  • Batch gradient descent
  • Gradient descent
  • Mini-batch gradient descent
  • None of the above

Q5. Fill in the blanks.

1. ________________: Parameters are updated after computing the gradient of error with respect to the entire training set

2. ________________: Parameters are updated after computing the gradient of error with respect to a single training example

3. ________________: Parameters are updated after computing the gradient of error with respect to a subset of the training set

  • Batch Gradient Descent, Stochastic Gradient Descent, Mini-Batch Gradient Descent
  • :Mini-Batch Gradient Descent, Stochastic Gradient Descent, Batch Gradient Descent
  • Mini Batch Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent
  • None of the above

Module Quiz Answers

Q1. What is the main difference between RMSE and MSE?

  • The loss metric output for RMSE is measured in the same units as the error making it easier to directly interpret
  • They produce the same result
  • The loss metric output for MSE is measured in the same units as the error making it easier to directly interpret
  • MSE and RMSE both square the error to avoid any negative signs and then take the square root

Q2. Which one of the following is NOT a performance metric

  • Recall
  • Accuracy
  • Precision
  • Cross Entropy

Q3. Which of the following is not a step in a typical model training loop?

  • Take the Derivative of the Loss Function
  • Take a step down the loss curve
  • Calculate the Loss
  • Increase the learning rate

Q4. Which of the following statement is true?

  • There will always be a gap between the metrics we care about and the metrics that work well with gradient descent.
  • There will never be a gap between the metrics we care about and the metrics that work well with gradient descent.
  • There will always be a gap between the metrics we care about and the metrics that will not work with gradient descent.
  • None of the above

Q5. What is the significance of Performance metrics?

  • Performance metrics will allow us to reject models that have settled into inappropriate minima.
  • Performance metrics will allow us to accept models that have settled into inappropriate minima.
  • Both A and B
  • None of the above

Q6. Which of the following are benefits of Performance metrics over loss functions?

  • Performance metrics are easier to understand.
  • Performance metrics are directly connected to business goals.
  • Both A and B

Launching into Machine Learning Week 04 Quiz Answers

Generalization and ML Models Quiz Answers

Q1. Which is the best way to assess the quality of a model?

  • Observing how well a model performs against a new dataset that it hasn’t seen before.
  • Observing how well a model performs against an existing known dataset.
  • Both A and B
  • None of the above

Q2. Which of the following statements is true?

  • Models that serialize well will have similar loss metrics or error values across training and validation.
  • Models that generalize well will have similar loss metrics or error values across training and validation.
  • Models that generalize well will have different loss metrics or error values across training and validation.
  • Models that serialize well will have different loss metrics or error values across training and validation.

Q3. How do you decide when to stop training a model?

  • When your loss metrics start to increase
  • When your loss metrics start to decrease
  • Both A and B
  • None of the above

Q4. Which of the following actions can you perform on your model when it is trained and validated?

  • You can write it once, and only once, against the independent test dataset.
  • You can write it once, and only once against the dependent test dataset.
  • You can write it multiple times against the independent test dataset.
  • You can write it multiple times against the dependent test dataset.

Module Quiz Answers

Q1. Which of the following statements is true?

  • You don’t need three separate queries to generate training, validation, and test splits. You can do it in a single query in BigQuery.
  • Splitting your dataset does not allow for testing your model against a simulated real world dataset by holding out those subsets of data from training
  • Both A and B
  • None of the above

Q2. Which of the following statements is true?

  • The RAND function in BigQuery generate a value between zero and ten.
  • The RAND function in BigQuery generate a value between zero and one.
  • Both A and B
  • None of the above

Q3. Which of the following allows you to create repeatable samples of your data?

  • Use the last few digits of a hash function on the field that you’re using to split or bucketize your data.
  • Use the first few digits of a hash function on the field that you’re using to split or bucketize your data.
  • Both A and B
  • None of the above

Q4. Which of the following allows you to split the dataset based upon a field in your data?

  • FARM_FINGERPRINT, an open-source hashing algorithm that is implemented in BigQuery SQL.
  • BUCKETIZE, an open-source hashing algorithm that is implemented in BigQuery SQL.
  • ML_FEATURE FINGERPRINT, an open-source hashing algorithm that is implemented in BigQuery SQL.
  • None of the above.

Launching into Machine Learning Course Quiz Answers

Q1. Which of the following is the most typically used loss function for regression?

  • RMSE for linear regression
  • Cross-entropy for classification
  • Both A and B
  • None of the above

Q2. Which is the most preferred way to traverse loss surfaces efficiently?

  • By analyzing the slopes of our loss functions, which provide us direction and step magnitude.
  • By analyzing the magnitude of our loss functions, which provide us direction and slope.
  • By analyzing the direction of our loss functions, which provide us magnitude and slope.
  • None of the above

Q3. Which of the following is a feature of random forests?

  • Linear sampling
  • Random sampling
  • Non-linear sampling
  • None of the above

Q4. Which core algorithm is used to construct Decision Trees?

  • Linear Regression
  • Logistic Regression
  • Greedy algorithms
  • Naive Bayes
Launching into Machine Learning Coursera Course Review:

In our experience, we suggest you enroll in the Launching into Machine Learning Course and gain some new skills from Professionals completely free and we assure you will be worth it.

I hope this Launching into Machine Learning course is available on Coursera for free, if you are stuck anywhere between quiz or graded assessment quiz, just visit Networking Funda to get Launching into Machine Learning Quiz Answers.

Conclusion:

I hope this Launching into Machine Learning Quiz Answers would be useful for you to learn something new from this Course. If it helped you then don’t forget to bookmark our site for more Coursera Quiz Answers.

This course is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisite courses.

Keep Learning!

Get all Course Quiz Answers of Machine Learning with TensorFlow on Google Cloud Specialization

How Google does Machine Learning Coursera Quiz Answers

Launching into Machine Learning Coursera Quiz Answers

Introduction to TensorFlow Coursera Quiz Answers

Feature Engineering Coursera Quiz Answers

Art and Science of Machine Learning Coursera Quiz Answers

Leave a Reply

error: Content is protected !!