We Can See The Idea Of Individualism In Which Of The Following Aspects Of Jan Van Eyckã¢â‚¬â„¢s Painting?

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Machine Learning with Python Coursera Quiz Answers Week ane

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

Truthful
False

Question 2: Which of the following is not true virtually Motorcar Learning?

Machine Learning was inspired by the learning process of human beings.
Car Learning models iteratively larn from information, and let computers to detect subconscious insights.
Automobile Learning models help the states in tasks such as object recognition, summarization, and recommendation.
Machine learning gives computers the ability to brand decision past writing down rules and methods and being explicitly programmed.

Question 3: Which of the following groups are not Automobile Learning techniques?

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

Question four: The "Regression" technique in Automobile 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 form/category of a case; for example a jail cell is benign or malignant, or a customer will churn or non.
Finding items/events that often co-occur; for instance grocery items that are usually bought together past a customer.

Question 5: When comparison Supervised with Unsupervised learning, is this judgement True or False?

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
True

Machine Learning with Python Coursera Quiz Answers Week two

Question 1: Multiple Linear Regression is appropriate for:

Predicting the sales corporeality based on calendar 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 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 information that the model has Non been trained on.
"Out of Sample Accurateness" is the accuracy of an overly trained model (which may captured racket 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 at that place are multiple dependent variables
When we would like to identify the strength of the result that the independent variables have on a dependent variable.

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

Polynomial regression can use the same mechanism as 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 v: Which judgement is Not True about Not-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 exist considered non-linear, y must exist a non-linear function of the parameters.
Non-linear regression must have 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 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 coin a customer volition spend in one yr.
To predict whether a customer responds to a particular advertising campaign or not.

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

Logistic regression tin be used both for binary nomenclature and multi-course classification
Logistic regression is analogous to linear regression but takes a categorical/detached target field instead of a numeric 1.
In logistic regression, the dependent variable is binary.

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

The probability that a person has a middle set on inside a specified time period using person'due south age and sexual practice.
Customer's propensity to purchase a product or halt a subscription in marketing applications.
Likelihood of a homeowner defaulting on a mortgage.
Estimating the blood force per unit area of a patient based on her symptoms and biographical data.

Question 4: Which 1 is TRUE near the kNN algorithm?

kNN is a classification algorithm that takes a agglomeration of unlabelled points and uses them to larn how to label other points.
kNN algorithm can be used to judge values for a continuous target.

Question v: What is "data proceeds" in decision trees?

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

Car Learning with Python Coursera Quiz Answers Week 4

Question ane: Which statement is NOT TRUE about grand-ways clustering?

k-means divides the information into non-overlapping clusters without whatever cluster-internal structure.
The objective of yard-means, is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into unlike clusters.
As grand-ways is an iterative algorithm, it guarantees that information technology will e'er 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 can find a cluster completely surrounded by a different cluster.
DBSCANhas a notion of noise, and is robust to outliers.
DBSCAN does not require one to specify the number of clusters such as k in chiliad-means

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

Customer churn prediction
Price estimation
Customer segmentation
Sales prediction

Question 4: Which approach can exist used to calculate 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 thou-means?

We can randomly choose some observations out of the data set and utilise these observations as the initial means.
We tin create some random points as centroids of the clusters.
We 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 ameliorate 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 buy of their product
Recommender Systems benefit the service provider by increasing potential revenue and amend security for its consumers.
All of the above.

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

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 organization tries to recommend items based on the similarity of users when buying, watching, or enjoying something.
All of in a higher place.

Question three: What is the meaning of "Common cold showtime" in collaborative filtering?

The difficulty in recommendation when we do not have enough ratings in the user-item dataset.
The difficulty in recommendation when we have new user, and we cannot make a profile for him, or when we have a new particular, which has not got any rating even so.
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 performance.

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

In retentivity based approach, a recommender system is created using machine learning techniques such as regression, clustering, classification, etc.
In memory based approach, a model of users is developed in endeavor to acquire their preferences.
In memory based approach, we use the unabridged user-item dataset to generate a recommendation system.

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

Users will merely 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 amongst items and users, it is not easy to find the neighbour users.
It needs to find similar group of users, and then suffers from drops in performance, but due to growth in the similarity computation.