Get All Module Machine Learning with Python Quiz Answers
This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
- Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
- Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!
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Module 1 – Machine Learning
Question: Machine Learning uses algorithms that can learn from data without relying on explicitly programmed methods.
Question: Which are the two types of supervised learning techniques?
- Classification and Clustering
- Classification and K-Means
- Regression and Clustering
- Regression and Partitioning
- Classification and Regression
Question: Which of the following statements best describes the Python scikit library?
- A library for scientific and high-performance computation.
- A collection of algorithms and tools for machine learning.
- A popular plotting package that provides 2D plotting as well as 3D plotting.
- A library that provides high-performance, easy to use data structures.
- A collection of numerical algorithms and domain-specific toolboxes.
Module 2 – Regression
Question: Training and testing on the same dataset might have a high training accuracy, but its out-of-sample accuracy might be low.
Question: If the correlation coefficient is 0.7 or lower, it may be appropriate to fit a non-linear regression.
Question: When we should use Multiple Linear Regression?
- When we would like to identify the strength of the effect that the independent variables have on a dependent variable.
- When there are multiple dependent variables.
Module 3 – Classification
Question: In K-Nearest Neighbors, which of the following is true:
- A very high value of K (ex. K = 100) produces an overly generalised model, while a very low value of k (ex. k = 1) produces a highly complex model.
- A very high value of K (ex. K = 100) produces a model that is better than a very low value of K (ex. K = 1)
- A very high value of k (ex. k = 100) produces a highly complex model, while a very low value of K (ex. K = 1) produces an overly generalized model.
Question: A classifier with lower log loss has better accuracy.
Question: When building a decision tree, we want to split the nodes in a way that decreases entropy and increases information gain.
Module 4 – Clustering
Question: Which one is NOT TRUE about k-means clustering??
- K-means divides the data into non-overlapping clusters without any cluster internal structure.
- The objective of k-means is to form clusters in such a way that similar 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: Customer segmentation is a supervised way of clustering data based on the similarity of customers to each other.
Question: How is a center point (centroid) picked for each cluster in k-means?
- We can randomly choose some observations out of the dataset and use these observations as the initial means.
- We can select the centroid through correlation analysis.
Module 5 – Recommender Systems
Question: Collaborative filtering is based on relationships between products and people’s rating patterns.
Question: Which one is TRUE about content-based recommendation systems?
- Content-based recommendation system tries to recommend items to the users based on their profile.
- In content-based approach, the recommendation process is based on similarity of users.
- In content-based recommender systems, similarity of users should be measured based on the similarity of the actions of users.
Question: Which one is correct about user-based and item-based collaborative filtering?
- In the item-based approach, the recommendation is based on the profile of a user that shows interest in a specific item.
- In the user-based approach, the recommendation is based on users of the same neighborhood, with whom he/she shares common preferences.
Question: You can define Jaccard as the size of the intersection divided by the size of the union of two label sets.
Question: When building a decision tree, we want to split the nodes in a way that increases entropy and decreases information gain.
Question: Which of the following statements are true? (Select all that apply.)
- K needs to be initialized in K-Nearest Neighbor.
- Supervised learning works on labelled data.
- A high value of K in KNN creates a model that is over-fit.
- KNN takes a bunch of unlabelled points and uses them to predict unknown points.
- Unsupervised learning works on unlabelled data.
Question: To calculate a model’s accuracy using the test set, you pass the test set to your model to predict the class labels, and then compare the predicted values with actual values.
Question: Which is the definition of entropy?
- The purity of each node in a decision tree.
- Information collected that can increase the level of certainty in a particular prediction.
- The information that is used to randomly select a subset of data.
- The amount of information disorder in the data.
Question: Which of the following is true about hierarchical linkages?
- Average linkage is the average distance of each point in one cluster to every point in another cluster.
- Complete linkage is the shortest distance between a point in two clusters.
- Centroid linkage is the distance between two randomly generated centroids in two clusters.
- Single linkage is the distance between any points in two clusters.
Question: The goal of regression is to build a model to accurately predict the continuous value of a dependent variable for an unknown case.
Question: Which of the following statements are true about linear regression? (Select all that apply)
- With linear regression, you can fit a line through the data.
- y=a+b_x1 is the equation for a straight line which can be used to predict the continuous value y.
- In y=θ^T.X, θ is the feature set and X is the “weight vector” or “confidences of the equation”, with both of these terms used interchangeably.
Question: The Sigmoid function is the main part of logistic regression, where Sigmoid of ?^?.?, gives us the probability of a point belonging to a class, instead of the value of y directly.
Question: In comparison to supervised learning, unsupervised learning has:
- Less tests (evaluation approaches)
- More models
- A better, controlled environment
- More tests (evaluation approaches), but less models
Question: The points that are classified by Density-Based Clustering and do not belong to any cluster are outliers.
Question: Which of the following is false about Simple Linear Regression?
- It does not require tuning parameters.
- It is highly interpretable.
- It is fast.
- It is used for finding outliers.
Question: Which one of the following statements is the most accurate?
- Machine Learning is the branch of AI that covers the statistical and learning part of artificial intelligence.
- Deep Learning is a branch of Artificial Intelligence where computers learn by being explicitly programmed.
- Artificial Intelligence is a branch of Machine Learning that covers the statistical part of Deep Learning.
- Artificial Intelligence is the branch of Deep Learning that allows us to create models.
Question: Which of the following are types of supervised learning?
Question: A bottom-up version of hierarchical clustering is known as divisive clustering. It is a more popular method than the Agglomerative method.
Question: Select all the true statements related to Hierarchical clustering and K-Means:
- Hierarchical clustering does not require the number of clusters to be specified.
- Hierarchical clustering always generates different clusters, whereas k-Means returns the same clusters each time it is run.
- K-Means is more efficient than Hierarchical clustering for large datasets.
Question: 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 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 buying, watching, or enjoying something.
Question: Before running Agglomerative clustering, you need to compute a distance/proximity matrix, which is an n by n table of all distances between each data point in each cluster of your dataset.
Question: Which of the following statements are true about DBSCAN? (Select all that apply.)
- DBSCAN can be used when examining spatial data.
- DBSCAN can be applied to tasks with arbitrary shaped clusters, or clusters within clusters.
- DBSCAN is a hierarchical algorithm that finds core and border points.
- DBSCAN can find any arbitrary shaped cluster without getting affected by noise.
Question: In recommender systems, a “cold start” happens when you have a large dataset of users who have rated only a limited number of items.
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