Which Of The Following Statements Regarding Service Learning Is Not True?

Enroll Here: Machine Learning with Python IBM Coursera Document

Motorcar Learning with Python Coursera Quiz Answers Week 1

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

• True
• False

Question ii: Which of the following is not truthful about Machine Learning?

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

Question iii: Which of the following groups are not Automobile 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 price of a house based on its characteristics.
• Prediction of class/category of a case; for example a cell is benign or cancerous, or a customer will churn or not.
• Finding items/events that often co-occur; for example grocery items that are ordinarily bought together by a client.

Question 5: When comparison Supervised with Unsupervised learning, is this sentence Truthful or Faux?

In contrast to Supervised learning, Unsupervised learning has more models and more evaluation methods that tin can be used in social club to ensure the event of the model is authentic.

• False
• True

Car Learning with Python Coursera Quiz Answers Week ii

Question 1: 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 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 accuracy of an overly trained model (which may captured noise and produced a non-generalized model)

Question 3: When should nosotros use Multiple Linear Regression?

• When we would like 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 identify the force of the effect that the independent variables have on a dependent variable.

Question 4: Which of the following statements are Truthful nearly Polynomial Regression?

• Polynomial regression can use the same machinery equally Multiple Linear Regression to find the parameters.
• Polynomial regression fits a bend line to your data.
• Polynomial regression models tin can fit using the Least Squares method.

Question 5: Which judgement is NOT TRUE about Non-linear Regression?

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

Machine Learning with Python Coursera Quiz Answers Week 3

Question 1: Which i IS Non a sample of nomenclature problem?

• 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 twelvemonth.
• To predict whether a client responds to a particular advertising campaign or non.

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

• Logistic regression can exist used both for binary classification and multi-class nomenclature
• Logistic regression is coordinating to linear regression only 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 employ)

• The probability that a person has a centre set on within a specified time menstruum using person'due south age and sex activity.
• Customer's propensity to buy 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 information.

Question four: Which i is True near the kNN algorithm?

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

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

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

Motorcar Learning with Python Coursera Quiz Answers Week 4

Question 1: Which argument is Not TRUE about k-means clustering?

• k-means divides the data into not-overlapping clusters without any cluster-internal structure.
• The objective of g-means, is to class clusters in such a way that like samples go into a cluster, and dissimilar samples fall into different clusters.
• As k-means is an iterative algorithm, it guarantees that it will always converge to the global optimum.

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

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

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

• Customer churn prediction
• Price estimation
• Client sectionalization
• Sales prediction

Question 4: Which approach can be used to calculate dissimilarity of objects in clustering?

• Minkowski distance
• Euclidian distance
• Cosine similarity
• All of the to a higher place

Question v: How is a eye betoken (centroid) picked for each cluster in k-means?

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

Automobile Learning with Python Coursera Quiz Answers Calendar week 5

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

• Recommender Systems provide a ameliorate feel for the users past giving them a broader exposure to many unlike products they might be interested in.
• Recommender Systems encourage users towards continual usage or purchase of their production
• 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 organisation 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 organisation tries to recommend items based on the similarity of users when buying, watching, or enjoying something.
• All of to a higher place.

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

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

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

• In memory based approach, a recommender system is created using machine learning techniques such as regression, clustering, classification, etc.
• In memory based arroyo, a model of users is developed in effort to learn their preferences.
• In memory based approach, we employ the entire user-detail dataset to generate a recommendation arrangement.

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

• Users will only get recommendations related to their preferences in their contour, 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 easy to notice the neighbour users.
• Information technology needs to observe like group of users, and so suffers from drops in performance, only due to growth in the similarity ciphering.

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