**Enroll Here: Machine Learning with Python IBM Coursera Certificate**

**Machine Learning with Python Coursera Quiz Answers Week 1**

**Question 1: Supervised learning deals with unlabeled data, while unsupervised learning deals with labelled data.**

- True
**False**

**Question 2: Which of the following is not true about Machine Learning?**

- Machine Learning was inspired by the learning process of human beings.
- Machine Learning models iteratively learn from data, and allow computers to find hidden insights.
- Machine Learning models help us in tasks such as object recognition, summarization, and recommendation.
**Machine learning gives computers the ability to make decision by writing down rules and methods and being explicitly programmed.**

**Question 3: Which of the following groups are not Machine Learning techniques?**

- Classification and Clustering
**Numpy, Scipy and Scikit-Learn**- Anomaly Detection and Recommendation Systems

**Question 4: 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.

**Question 5: 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 Coursera Quiz Answers Week 2**

**Question 1: Multiple Linear Regression is appropriate for:**

- Predicting the sales amount based on month
- Predicting whether a drug is effective for a patient based on her characterestics
**Predicting tomorrow’s rainfall amount based on the wind speed and temperature**

**Question 2: 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)

**Question 3: When should we use Multiple Linear Regression?**

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

**Question 4: Which of the following statements are TRUE about Polynomial Regression?**

**Polynomial regression can use the same mechanism as Multiple Linear Regression to find the parameters.****Polynomial regression fits a curve line to your data.****Polynomial regression models can fit using the Least Squares method.**

**Question 5: 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 Coursera Quiz Answers Week 3**

**Question 1: Which one IS NOT a sample of classification problem?**

- To predict the category to which a customer belongs to.
- To predict whether a customer switches to another provider/brand.
**To predict the amount of money a customer will spend in one year.**- To predict whether a customer responds to a particular advertising campaign or not.

**Question 2: Which of the following statements are TRUE about Logistic Regression? (select all that apply)**

**Logistic regression can be used both for binary classification and multi-class classification****Logistic regression is analogous to linear regression but takes a categorical/discrete target field instead of a numeric one.****In logistic regression, the dependent variable is binary.**

**Question 3: 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.

**Question 4: 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.**

**Question 5: 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 Coursera Quiz Answers Week 4**

**Question 1: Which statement 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: Which of the following are characteristics of DBSCAN? Select all that apply.**

**DBSCAN can find arbitrarily shaped clusters.****DBSCAN can find a cluster completely surrounded by a different cluster.****DBSCANhas a notion of noise, and is robust to outliers.****DBSCAN does not require one to specify the number of clusters such as k in k-means**

**Question 3: Which of the following is an application of clustering?**

- Customer churn prediction
- Price estimation
**Customer segmentation**- Sales prediction

**Question 4: Which approach can be used to calculate dissimilarity of objects in clustering? **

- Minkowski distance
- Euclidian distance
- Cosine similarity
**All of the above**

**Question 5: 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 Coursera Quiz Answers Week 5**

**Question 1: What is/are the advantage/s of Recommender Systems ?**

- Recommender Systems provide a better experience for the users by giving them a broader exposure to many different products they might be interested in.
- Recommender Systems encourage users towards continual usage or purchase of their product
- Recommender Systems benefit the service provider by increasing potential revenue and better security for its consumers.
**All of the above.**

**Question 2: 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.
- All of above.

**Question 3: 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.

**Question 4: 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, a model of users is developed in attempt to learn their preferences.
**In memory based approach, we use the entire user-item dataset to generate a recommendation system.**

**Question 5: What is the shortcoming of content-based recommender systems?**

**Users will only get recommendations related to their preferences in their profile, and recommender engine may never recommend any item with other characteristics.**- 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.