Are you looking for **Machine Learning with Python Exam Answers** by Cognitive Class? If yes, this article will help you find all the questions and answers in the Cognitive Class Machine Learning with Python Quiz. I have followed this article to answer all the questions in this exam.

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 make a comparison of each.

Popular algorithms | Classification, Regression, Clustering, and Dimensional Reduction |

Popular models | Train/Test Split, Root Mean Squared Error, and Random Forests |

Class Start | Any time, Self-paced |

Duration | 3hr |

Eligibility | Anyone interested in Machine Learning and Python |

Level | Beginner |

Language | English |

Machine Learning with Python | Click Here |

Page Contents

### Module 1 – Supervised vs Unsupervised Learning Answer

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

**2. Which are the two types of Supervised learning techniques?**

**3. Which of the following statements best describes the Python scikit library?**

### Module 2 – Supervised Learning I Answer

**1. Train and Test on the Same Dataset might have a high training accuracy, but its out-of-sample accuracy can be low.**

**2. Which of the following matrices can be used to show the results of model accuracy evaluation or the model’s ability to correctly predict or separate the classes?**

**3. When we should use Multiple Linear Regression?**

### Module 3 – Supervised Learning II Answer

**1. In K-Nearest Neighbors, which of the following is true:**

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

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

### Module 4 – Unsupervised Learning Answer

**1. Which one is NOT TRUE about k-means clustering??**

**2. Customer Segmentation is a supervised way of clustering data, based on the similarity of customers to each other.**

**3. How is a center point (centroid) picked for each cluster in k-means?**

### Module 5 – Dimensionality Reduction & Collaborative Filtering Answer

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

**2. Which one is TRUE about Content-based recommendation systems?**

**3. Which one is correct about user-based and item-based collaborative filtering?**

### Machine Learning with Python Final Exam Answers

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

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

**3. Which of the following statements are true? (Select all that apply.)**

**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.**

**5. Which is the definition of entropy?**

**6. Which of the following is true about hierarchical linkages?**

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

**8. Which of the following statements are true about linear regression? (Select all that apply)**

**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.**

**10. In comparison to supervised learning, unsupervised learning has:**

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

12. **Which of the following is false about Simple Linear Regression?**

**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 explicitely 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.

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

**15. A Bottom-Up version of hierarchical clustering is known as Divisive clustering. It is a more popular method than the Agglomerative method.**

**16. Select all the true statements related to Hierarchical clustering and K-Means.**

**17. What is a content-based recommendation system?**

**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.**

**19. Which of the following statements are true about DBSCAN? (Select all that apply)**

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

### Wrap Up

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