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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:
Get ready to do more learning than your machine!
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?
Question: Which of the following statements best describes the Python scikit library?
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?
Module 3 – Classification
Question: In K-Nearest Neighbors, which of the following is true:
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??
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?
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?
Question: Which one is correct about user-based and item-based collaborative filtering?
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.)
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?
Question: Which of the following is true about hierarchical linkages?
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)
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:
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?
Question: Which one of the following statements is the most accurate?
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:
Question: What is a content-based recommendation system?
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.)
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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