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Module 1 - Introduction to Machine Learning.md

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Module Introduction

In this module, you will learn about the applications of Machine Learning in different fields such as healthcare, banking, telecommunication, and others. You'll gain a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you will understand the advantage of using Python libraries for implementing Machine Learning models.

Learning Objectives

  • To give examples of Machine Learning.
  • To demonstrate the Python libraries for Machine Learning.
  • To classify Supervised vs. Unsupervised algorithms.

Module 1 - Graded Quiz

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

  • A. [ ] True
  • B. [X] False

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

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

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

  • A. [X] Numpy, Scipy and Scikit-Learn.
  • B. [ ] Anomaly Detection and Recommendation Systems.
  • C. [ ] Classification and Clustering.

Question 4: The "Regression" technique in Machine Learning is a group of algorithms that are used for:

  • A. [ ] Prediction of class/category of a case; for example a cell is benign or malignant, or a customer will churn or not.
  • B. [X] Predicting a continuous value; for example predicting the price of a house based on its characteristics.
  • C. [ ] 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.

  • A. [ ] True
  • B. [X] False