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Machine Learning With Python Week 1 Quiz Answer | Intro to Machine Learning

Machine Learning With Python Week 1 Quiz Answer  Intro to Machine Learning


Machine Learning With Python Week 1 Quiz Answer | Intro to Machine Learning 


About this Course

  • This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. 
  • In this course, we will be reviewing two main components:
  • First, you will be learning about the purpose of Machine Learning and where it applies to the real world. 
  • Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning,  model evaluation, and Machine Learning algorithms. 
  • In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!

  • By just putting in a few hours a week for the next few weeks, this is what you’ll get.


1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 

2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.

3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.


If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.


This course is part of multiple programs

This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

  • IBM AI Engineering Professional Certificate
  • IBM Data Science Professional Certificate


WHAT YOU WILL LEARN

  • Give examples of Machine Learning in various industries.
  • Outline the steps machine learning uses to solve problems. 
  • Provide examples of various techniques used in machine learning. 
  • Describe the Python libraries for Machine Learning. 


SKILLS YOU WILL GAIN

  • Python Libraries
  • Machine Learning
  • regression
  • Hierarchical Clustering
  • K-Means Clustering


Go to this Course





Intro to Machine Learning Quiz Answer


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 help us in tasks such as object recognition, summarization, and recommendation.
  • Machine Learning models iteratively learn from data, and allow computers to find hidden insights.
  • 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?


  • Numpy, Scipy and Scikit-Learn
  • Classification and Clustering
  • 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.
  • Finding items/events that often co-occur; for example grocery items that are usually bought together by a customer.
  • Prediction of class/category of a case; for example a cell is benign or malignant, or a customer will churn or not.



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.


  • True
  • False