Machine Learning with Python Coursera Quiz Answer – Networking Funda

All Weeks Machine Learning with Python Coursera Quiz Answer

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.

In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!

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Machine Learning with Python Week 01 Quiz Answer

Quiz : Intro to Machine Learning

Q1. Supervised learning deals with unlabeled data, while unsupervised learning deals with labelled data.

  • True
  • False

Q2. 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 malignant, or a customer will churn or not.
  • Finding items/events that often co-occur; for example grocery items that are usually bought together by a customer.

Q3. 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
  • True

Machine Learning with Python Week 02 Quiz Answer

Quiz : Regression

Q1. 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 NOT 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)

Q2. When should we use Multiple Linear Regression?

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

Q3. Which sentence 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 independent variables.
  • For a model to be considered non-linear, y must be a non-linear function of the parameters.
  • Non-linear regression must have more than one dependent variable.

Machine Learning with Python Week 03 Quiz Answer

Quiz : Classification

Q1. Which of the following examples is/are a sample application of Logistic Regression? (select all that apply)

  • The probability that a person has a heart attack within a specified time period using person’s age and sex.
  • 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 pressure of a patient based on her symptoms and biographical data.

Q2. Which one is TRUE about 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 can be used to estimate values for a continuous target.

Q3. What is “information gain” in decision 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 after split by an attribute.
  • It is the amount of information disorder, or the amount of randomness in each node.

Machine Learning with Python Week 04 Quiz Answer

Quiz : Clustering

Q1. Which of the following is an application of clustering?

  • Customer churn prediction
  • Price estimation
  • Customer segmentation
  • Sales prediction

Q2. Which approach can be used to calculate dissimilarity of objects in clustering?

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

Q3. How is a center point (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 can create some random points as centroids of the clusters.
  • We can select it through correlation analysis.

Machine Learning with Python Week 05 Quiz Answer

Quiz : Recommender System

Q1. What is the meaning of “Cold start” 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 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 begin to suffer drops in performance.

Q2. What is a “Memory-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 approach, we use the entire user-item dataset to generate a recommendation system.
  • In memory based approach, a model of users is developed in attempt to learn their preferences.

Q3. What is the shortcoming of content-based recommender systems?

  • As it is based on similarity among items and users, it is not easy to find the neighbour users.
  • It needs to find similar group of users, so suffers from drops in performance, simply due to growth in the similarity computation.
  • Users will only get recommendations related to their preferences in their profile, and recommender engine may never recommend any item with other characteristics.
Machine Learning with Python Coursera Course Review:

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This course is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisite courses.

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