Which of the Following Is Also a Requirement for Dependent Family Members Ages 14

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Machine Learning with Python Coursera Quiz Answers Week 1

Question ane: Supervised learning deals with unlabeled data, while unsupervised learning deals with labelled data.

• True
• False

Question ii: Which of the following is not true most Machine Learning?

• Car Learning was inspired by the learning process of human being beings.
• Machine Learning models iteratively learn from data, and allow computers to find hidden insights.
• Automobile Learning models aid us in tasks such as object recognition, summarization, and recommendation.
• Car learning gives computers the power to make decision by writing downwardly rules and methods and existence explicitly programmed.

Question three: Which of the following groups are not Machine Learning techniques?

• Classification and Clustering
• Numpy, Scipy and Scikit-Learn
• Anomaly Detection and Recommendation Systems

Question 4: The "Regression" technique in Auto Learning is a grouping of algorithms that are used for:

• Predicting a continuous value; for example predicting the price of a house based on its characteristics.
• Prediction of course/category of a case; for example a prison cell is benign or malignant, or a customer will churn or not.
• Finding items/events that often co-occur; for instance grocery items that are usually bought together past a customer.

Question 5: When comparing Supervised with Unsupervised learning, is this sentence True or Simulated?

In dissimilarity to Supervised learning, Unsupervised learning has more models and more than evaluation methods that can exist used in order to ensure the consequence of the model is accurate.

• False
• Truthful

Machine Learning with Python Coursera Quiz Answers Week two

Question one: Multiple Linear Regression is advisable for:

• Predicting the sales amount based on month
• Predicting whether a drug is effective for a patient based on her characterestics
• Predicting tomorrow's rainfall amount based on the wind speed and temperature

Question 2: Which of the following is the pregnant of "Out of Sample Accuracy" in the context of evaluation of models?

• "Out of Sample Accuracy" is the percent of correct predictions that the model makes on data that the model has NOT been trained on.
• "Out of Sample Accuracy" is the accuracy of an overly trained model (which may captured noise and produced a not-generalized model)

Question 3: When should nosotros use Multiple Linear Regression?

• When nosotros would similar to predict impacts of changes in independent variables on a dependent variable.
• When at that place are multiple dependent variables
• When we would like to place the forcefulness of the effect that the independent variables take on a dependent variable.

Question 4: Which of the post-obit statements are True about Polynomial Regression?

• Polynomial regression can apply the same mechanism every bit Multiple Linear Regression to find the parameters.
• Polynomial regression fits a curve line to your data.
• Polynomial regression models can fit using the Least Squares method.

Question five: Which judgement is Non TRUE about Non-linear Regression?

• Nonlinear regression is a method to model non linear relationship between the dependent variable and a set of independent variables.
• For a model to be considered non-linear, y must exist a non-linear function of the parameters.
• Non-linear regression must accept more than one dependent variable.

Machine Learning with Python Coursera Quiz Answers Calendar week 3

Question 1: Which one IS Not a sample of classification problem?

• To predict the category to which a customer belongs to.
• To predict whether a customer switches to some other provider/brand.
• To predict the amount of money a customer will spend in 1 year.
• To predict whether a customer responds to a particular advertising campaign or not.

Question 2: Which of the following statements are TRUE about Logistic Regression? (select all that apply)

• Logistic regression can be used both for binary nomenclature and multi-class classification
• Logistic regression is analogous to linear regression merely takes a chiselled/discrete target field instead of a numeric ane.
• In logistic regression, the dependent variable is binary.

Question iii: Which of the post-obit examples is/are a sample application of Logistic Regression? (select all that apply)

• The probability that a person has a heart assail within a specified time flow using person'south historic period and sex.
• Client's propensity to purchase a product or halt a subscription in marketing applications.
• Likelihood of a homeowner defaulting on a mortgage.
• Estimating the claret pressure of a patient based on her symptoms and biographical data.

Question 4: Which one is TRUE virtually the kNN algorithm?

• kNN is a classification algorithm that takes a agglomeration of unlabelled points and uses them to acquire how to label other points.
• kNN algorithm tin be used to gauge values for a continuous target.

Question 5: What is "data proceeds" in determination trees?

• It is the information that can decrease the level of certainty after splitting in each node.
• It is the entropy of a tree before split minus weighted entropy later on split by an attribute.
• Information technology is the amount of information disorder, or the amount of randomness in each node.

Automobile Learning with Python Coursera Quiz Answers Week 4

Question ane: Which statement is NOT TRUE about thou-means clustering?

• k-means divides the data into non-overlapping clusters without any cluster-internal structure.
• The objective of thou-ways, is to form clusters in such a mode that similar samples go into a cluster, and dissimilar samples fall into different clusters.
• Equally yard-means is an iterative algorithm, information technology guarantees that it will always converge to the global optimum.

Question 2: Which of the following are characteristics of DBSCAN? Select all that apply.

• DBSCAN can find arbitrarily shaped clusters.
• DBSCAN tin can notice a cluster completely surrounded by a dissimilar cluster.
• DBSCANhas a notion of racket, and is robust to outliers.
• DBSCAN does not require one to specify the number of clusters such every bit grand in k-means

Question 3: Which of the following is an application of clustering?

• Customer churn prediction
• Cost estimation
• Customer segmentation
• Sales prediction

Question 4: Which approach tin can be used to summate contrast of objects in clustering?

• Minkowski distance
• Euclidian distance
• Cosine similarity
• All of the above

Question five: How is a center point (centroid) picked for each cluster in k-means?

• Nosotros can randomly choose some observations out of the data set and utilise these observations equally the initial means.
• We can create some random points every bit centroids of the clusters.
• We can select it through correlation analysis.

Machine Learning with Python Coursera Quiz Answers Week 5

Question one: What is/are the reward/south of Recommender Systems ?

• Recommender Systems provide a better feel for the users by giving them a broader exposure to many different products they might exist interested in.
• Recommender Systems encourage users towards continual usage or purchase of their product
• Recommender Systems do good the service provider by increasing potential revenue and better security for its consumers.
• All of the above.

Question 2: What is a content-based recommendation system?

• Content-based recommendation system tries to recommend items to the users based on their profile built upon their preferences and taste.
• Content-based recommendation system tries to recommend items based on similarity among items.
• Content-based recommendation organization tries to recommend items based on the similarity of users when buying, watching, or enjoying something.
• All of above.

Question 3: What is the pregnant of "Common cold start" in collaborative filtering?

• The difficulty in recommendation when nosotros do not take enough ratings in the user-item dataset.
• The difficulty in recommendation when we accept new user, and nosotros cannot brand a profile for him, or when we take a new item, which has not got any rating yet.
• The difficulty in recommendation when the number of users or items increases and the amount of data expands, so algorithms will brainstorm to suffer drops in performance.

Question 4: What is a "Memory-based" recommender system?

• In memory based approach, a recommender arrangement is created using machine learning techniques such as regression, clustering, classification, etc.
• In retentiveness based approach, a model of users is developed in attempt to larn their preferences.
• In memory based approach, nosotros use the unabridged user-detail dataset to generate a recommendation system.

Question v: What is the shortcoming of content-based recommender systems?

• Users will simply get recommendations related to their preferences in their profile, and recommender engine may never recommend any item with other characteristics.
• As information technology is based on similarity amid items and users, it is not easy to notice the neighbour users.
• It needs to find like group of users, and then suffers from drops in performance, merely due to growth in the similarity ciphering.

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