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Automobile Learning with Python Coursera Quiz Answers Calendar week 1

Question 1: Supervised learning deals with unlabeled data, while unsupervised learning deals with labelled data.

• True
• Simulated

Question 2: Which of the following is not true most Machine Learning?

• Machine Learning was inspired by the learning process of human beings.
• Machine Learning models iteratively acquire from data, and allow computers to discover hidden insights.
• Auto Learning models assist united states in tasks such as object recognition, summarization, and recommendation.
• Machine learning gives computers the ability to make determination by writing downwards rules and methods and existence explicitly programmed.

Question 3: Which of the post-obit groups are not Machine Learning techniques?

• Nomenclature and Clustering
• Numpy, Scipy and Scikit-Acquire
• Bibelot 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 instance predicting the toll of a house based on its characteristics.
• Prediction of class/category of a case; for example a cell is benign or malignant, or a client will churn or not.
• Finding items/events that often co-occur; for example grocery items that are usually bought together by a customer.

Question v: When comparing Supervised with Unsupervised learning, is this sentence True or False?

In contrast to Supervised learning, Unsupervised learning has more models and more evaluation methods that can be used in order to ensure the outcome of the model is accurate.

• False
• Truthful

Automobile Learning with Python Coursera Quiz Answers Week 2

Question ane: Multiple Linear Regression is appropriate for:

• Predicting the sales corporeality 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 two: Which of the following is the meaning of "Out of Sample Accuracy" in the context of evaluation of models?

• "Out of Sample Accuracy" is the percentage of correct predictions that the model makes on data that the model has Non been trained on.
• "Out of Sample Accuracy" is the accurateness of an overly trained model (which may captured noise and produced a non-generalized model)

Question 3: When should we use Multiple Linear Regression?

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

Question iv: Which of the post-obit statements are True nearly Polynomial Regression?

• Polynomial regression can use the aforementioned mechanism as Multiple Linear Regression to find the parameters.
• Polynomial regression fits a bend line to your information.
• Polynomial regression models can fit using the Least Squares method.

Question v: Which judgement is Not Truthful about Non-linear Regression?

• Nonlinear regression is a method to model non linear relationship between the dependent variable and a prepare of contained variables.
• For a model to be considered non-linear, y must be a not-linear role of the parameters.
• Non-linear regression must have more than than one dependent variable.

Machine Learning with Python Coursera Quiz Answers Week iii

Question ane: Which one IS Non a sample of classification trouble?

• To predict the category to which a customer belongs to.
• To predict whether a customer switches to another provider/brand.
• To predict the corporeality of money a customer volition spend in i year.
• To predict whether a client responds to a particular advertising campaign or not.

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

• Logistic regression can exist used both for binary nomenclature and multi-class classification
• Logistic regression is analogous to linear regression but takes a categorical/detached target field instead of a numeric one.
• In logistic regression, the dependent variable is binary.

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

• The probability that a person has a middle attack inside a specified time period using person's age and sex.
• Client's propensity to purchase a production 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 four: Which one is TRUE almost the kNN algorithm?

• kNN is a classification algorithm that takes a bunch of unlabelled points and uses them to learn how to label other points.
• kNN algorithm tin can be used to estimate values for a continuous target.

Question 5: What is "data gain" in decision copse?

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

Car Learning with Python Coursera Quiz Answers Calendar week 4

Question 1: Which statement is NOT Truthful about k-means clustering?

• k-means divides the data into non-overlapping clusters without whatever cluster-internal construction.
• The objective of chiliad-means, is to form clusters in such a mode that like samples go into a cluster, and unlike samples fall into different clusters.
• As k-ways is an iterative algorithm, it guarantees that information technology will ever converge to the global optimum.

Question 2: Which of the post-obit are characteristics of DBSCAN? Select all that apply.

• DBSCAN can discover arbitrarily shaped clusters.
• DBSCAN tin can find a cluster completely surrounded past a different cluster.
• DBSCANhas a notion of noise, and is robust to outliers.
• DBSCAN does non require one to specify the number of clusters such equally k in k-ways

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

• Customer churn prediction
• Toll estimation
• Client segmentation
• Sales prediction

Question iv: Which approach can exist used to summate contrast of objects in clustering?

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

Question 5: How is a center point (centroid) picked for each cluster in k-ways?

• We can randomly choose some observations out of the data set and utilize these observations as the initial ways.
• We can create some random points as centroids of the clusters.
• We tin select it through correlation analysis.

Motorcar Learning with Python Coursera Quiz Answers Calendar week 5

Question 1: What is/are the advantage/due 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 be interested in.
• Recommender Systems encourage users towards continual usage or purchase of their production
• Recommender Systems benefit the service provider past increasing potential acquirement and better security for its consumers.
• All of the above.

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

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

Question 3: What is the meaning of "Cold start" in collaborative filtering?

• The difficulty in recommendation when nosotros do non have plenty ratings in the user-item dataset.
• The difficulty in recommendation when nosotros have new user, and we cannot brand a profile for him, or when we take a new particular, which has not got any rating nonetheless.
• The difficulty in recommendation when the number of users or items increases and the amount of information expands, so algorithms will begin to suffer drops in operation.

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

• In retention based approach, a recommender system is created using machine learning techniques such as regression, clustering, classification, etc.
• In retention based approach, a model of users is adult in endeavour to learn their preferences.
• In retention based approach, nosotros use the entire user-item dataset to generate a recommendation system.

Question 5: What is the shortcoming of content-based recommender systems?

• Users will only get recommendations related to their preferences in their profile, and recommender engine may never recommend any item with other characteristics.
• As it is based on similarity amongst items and users, it is not easy to find the neighbour users.
• It needs to observe similar grouping of users, so suffers from drops in performance, simply due to growth in the similarity ciphering.

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