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.
SKILLS YOU WILL GAIN
- Machine Learning Concepts
- Knime
- Machine Learning
- Apache Spark
Also Check: How to Apply for Coursera Financial Aid
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.
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