Machine Learning with Big Data complete course is currently being offered by UC San Diego through Coursera platform.

This course is part of the Big Data Specialization.

About this Course

This course provides an overview of machine learning techniques to explore, analyze, and leverage data.  You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

At the end of the course, you will be able to:

Design an approach to leverage data using the steps in the machine learning process.
Apply machine learning techniques to explore and prepare data for modeling.
Identify the type of machine learning problem in order to apply the appropriate set of techniques.
Construct models that learn from data using widely available open source tools.
Analyze big data problems using scalable machine learning algorithms on Spark.


- Machine Learning Concepts
- Knime
- Machine Learning
- Apache Spark

Machine Learning with Big Data Week 1 Quiz Answers!

Machine Learning with Big Data Quiz 1 Answers!  

Machine Learning Overview

1. What is NOT machine learning?

  • Learning from data
  • Explicit, step-by-step programming
  • Data-driven decisions
  • Discover hidden patterns

2. Which of the following is NOT a category of machine learning?

  • Cluster Analysis
  • Classification
  • Regression
  • Association Analysis
  • Algorithm Prediction

3. Which categories of machine learning techniques are supervised?

  • classification and regression
  • regression and association analysis
  • classification and cluster analysis
  • cluster analysis and association analysis

4. In unsupervised approaches,

  • the target is unlabeled.
  • the target is unknown or unavailable.
  • the target is provided.
  • the target is what is being predicted.

5. What is the sequence of the steps in the machine learning process?

  • Acquire -> Prepare -> Analyze -> Report -> Act
  • Acquire -> Prepare -> Analyze -> Act -> Report
  • Prepare -> Acquire -> Analyze -> Report -> Act
  • Prepare -> Acquire -> Analyze -> Act -> Report

6. Are the steps in the machine learning process apply-once or iterative?

  • Apply-once
  • Iterative
  • The first two steps, Acquire and Prepare, are apply-once, and the other steps are iterative.

7. Phase 2 of CRISP-DM is Data Understanding. In this phase,

  • we acquire as well as explore the data that is related to the problem.
  • we define the problem or opportunity to be addressed.
  • we prepare the data for analysis.

8. What is the main difference between KNIME and Spark MLlib?

  • KNIME requires programming, while Spark MLlib does not.
  • KNIME requires programming in Java, while Spark MLlib requires programming in Python.
  • KNIME is a graphical user interface-based machine learning tool, while Spark MLlib provides a programming-based distributed platform for scalable machine learning algorithms.
  • KNIME originated in Germany, while Spark MLlib was created in California, USA.

 Also Check: Introduction to Back-End Development Quiz Answers - Coursera!

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