** Course Name:- Machine Learning with Python**

**Module 1. Machine Learning**

**Question 1. Machine Learning uses algorithms that can learn from data without relying on explicitly programmed methods.**

**True**- False

**Question 2. 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 3. 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 1. Training and testing on the same dataset might have a high training accuracy, but its out-of-sample accuracy might be low.**

**True**- False

**Question 2. If the correlation coefficient is 0.7 or lower, it may be appropriate to fit a non-linear regression.**

**True**- False

**Question 3. 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 1.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 2. A classifier with lower log loss has better accuracy.**

**True**- False

**Question 3. When building a decision tree, we want to split the nodes in a way that decreases entropy and increases information gain.**

**True**- False

**Module 4. clustring**

**Question 1. 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 2. Customer segmentation is a supervised way of clustering data based on the similarity of customers to each other.**

- True
**False**

**Question 3. 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 System**

**Question 1. Collaborative filtering is based on relationships between products and people’s rating patterns.**

**True**- False

**Question 2.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 3. 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.**

**Machine Learning with Python Cognitive class final Exam Answers:-**

**Question 1. You can define Jaccard as the size of the intersection divided by the size of the union of two label sets.**

**True**- False

**Question 2. When building a decision tree, we want to split the nodes in a way that increases entropy and decreases information gain.**

- True
**False**

**Question 3. 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 4. 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.**

**True**- False

**Question 5. 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 6. 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 7.The goal of regression is to build a model to accurately predict the continuous value of a dependent variable for an unknown case.**

**True**- False

**Question 8. 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 9. The Sigmoid function is the main part of logistic regression, where Sigmoid of ****𝜃****^****𝑇****.****𝑋****, gives us the probability of a point belonging to a class, instead of the value of y directly.**

**True**- False

**Question 10.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 11.The points that are classified by Density-Based Clustering and do not belong to any cluster are outliers.**

**True**- False

**Question 12.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 13.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 14.Which of the following are types of supervised learning?**

- Classification
- Regression
- KNN
- K-Means
**Clustering**

**Question 15. A bottom-up version of hierarchical clustering is known as divisive clustering. It is a more popular method than the Agglomerative method.**

- True
**False**

**Question 16. 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 17. 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 18. 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.**

**True**- False

**Question 19. 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 nois**e.

**Question 20. In recommender systems, a “cold start” happens when you have a large dataset of users who have rated only a limited number of items.**

- True
**False**