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|
|Eligibility||Anyone interested in Machine Learning and Python|
|Machine Learning with Python||Click Here|
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.
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