Which Of The Following Statements About Older Preschoolers' Drawings Is True

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Machine Learning with Python Coursera Quiz Answers Week 1

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

• True
• False

Question two: Which of the following is not truthful about Automobile Learning?

• Machine Learning was inspired by the learning process of homo beings.
• Auto Learning models iteratively learn from information, and allow computers to find hidden insights.
• Machine Learning models help u.s.a. in tasks such every bit object recognition, summarization, and recommendation.
• Motorcar learning gives computers the ability to make decision by writing down rules and methods and being 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 Machine Learning is a group of algorithms that are used for:

• Predicting a continuous value; for example predicting the toll of a firm based on its characteristics.
• Prediction of class/category of a case; for example a jail cell is beneficial or malignant, or a client volition churn or non.
• Finding items/events that often co-occur; for example grocery items that are unremarkably bought together by a customer.

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

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

• Faux
• Truthful

Machine Learning with Python Coursera Quiz Answers Calendar week two

Question one: Multiple Linear Regression is advisable for:

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

Question ii: 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 percent of correct predictions that the model makes on data that the model has Not been trained on.
• "Out of Sample Accurateness" 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 like to predict impacts of changes in independent variables on a dependent variable.
• When there are multiple dependent variables
• When we would like to identify the strength of the effect that the independent variables have on a dependent variable.

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

• Polynomial regression tin can utilise the same machinery as Multiple Linear Regression to find the parameters.
• Polynomial regression fits a curve line to your information.
• Polynomial regression models can fit using the Least Squares method.

Question 5: Which sentence is Not True about Not-linear Regression?

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

Motorcar Learning with Python Coursera Quiz Answers Week 3

Question 1: Which one IS NOT a sample of classification trouble?

• To predict the category to which a client belongs to.
• To predict whether a customer switches to another provider/brand.
• To predict the amount of money a customer will spend in one year.
• To predict whether a customer responds to a particular advertisement entrada or not.

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

• Logistic regression tin be used both for binary nomenclature and multi-class nomenclature
• Logistic regression is analogous to linear regression simply takes a categorical/discrete target field instead of a numeric one.
• In logistic regression, the dependent variable is binary.

Question 3: Which of the following examples is/are a sample application of Logistic Regression? (select all that apply)

• The probability that a person has a eye attack inside a specified time menstruum using person's age and sex activity.
• Customer's propensity to purchase a production or halt a subscription in marketing applications.
• Likelihood of a homeowner defaulting on a mortgage.
• Estimating the blood pressure of a patient based on her symptoms and biographical data.

Question four: Which one is TRUE about the kNN algorithm?

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

Question 5: What is "information gain" in conclusion trees?

• It is the information that tin subtract the level of certainty later on splitting in each node.
• Information technology is the entropy of a tree before split minus weighted entropy after carve up past an attribute.
• It is the amount of data disorder, or the corporeality of randomness in each node.

Machine Learning with Python Coursera Quiz Answers Calendar week 4

Question 1: Which statement is Non True about m-means clustering?

• g-ways divides the data into non-overlapping clusters without any cluster-internal construction.
• The objective of grand-ways, is to form clusters in such a way that similar samples become into a cluster, and unlike samples autumn into different clusters.
• As one thousand-means is an iterative algorithm, information technology guarantees that it will always converge to the global optimum.

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

• DBSCAN can find arbitrarily shaped clusters.
• DBSCAN tin find a cluster completely surrounded by a different cluster.
• DBSCANhas a notion of noise, and is robust to outliers.
• DBSCAN does not crave one to specify the number of clusters such equally 1000 in 1000-means

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

• Customer churn prediction
• Price interpretation
• Customer segmentation
• Sales prediction

Question iv: Which approach can be used to calculate dissimilarity of objects in clustering?

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

Question five: How is a middle indicate (centroid) picked for each cluster in k-ways?

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

Machine Learning with Python Coursera Quiz Answers Calendar week 5

Question 1: What is/are the advantage/s of Recommender Systems ?

• Recommender Systems provide a better experience for the users past 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 benefit the service provider by increasing potential revenue and improve security for its consumers.
• All of the above.

Question two: 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 organization tries to recommend items based on similarity among items.
• Content-based recommendation system tries to recommend items based on the similarity of users when buying, watching, or enjoying something.
• All of higher up.

Question iii: What is the significant of "Cold start" in collaborative filtering?

• The difficulty in recommendation when we practice non have plenty ratings in the user-item dataset.
• The difficulty in recommendation when nosotros have new user, and nosotros cannot make a contour for him, or when nosotros have a new item, which has not got whatever rating yet.
• The difficulty in recommendation when the number of users or items increases and the amount of data expands, and so algorithms volition begin to suffer drops in performance.

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

• In retentiveness based arroyo, a recommender system is created using machine learning techniques such as regression, clustering, nomenclature, etc.
• In memory based approach, a model of users is developed in effort to larn their preferences.
• In retentivity based approach, we use the entire user-item dataset to generate a recommendation system.

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

• Users will merely become 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 among items and users, it is not like shooting fish in a barrel to find the neighbour users.
• It needs to observe like group of users, so suffers from drops in performance, simply due to growth in the similarity ciphering.

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