AI For Everyone Answers Week 2 | Coursera

DeepLearning.AI: AI For Everyone Week 2 Quiz Answer

About the course:

AI for Everyone is an insightful and comprehensive online course developed by DeepLearning.AI in collaboration with Coursera. This course provides learners with a holistic understanding of artificial intelligence (AI) and its impact on various industries. Designed for individuals with non-technical backgrounds, this course equips learners with the knowledge necessary to navigate the AI landscape effectively.

Throughout the course, learners will engage with engaging video lectures, hands-on exercises, and interactive quizzes to develop a solid foundation in AI concepts. They will gain a practical understanding of AI technologies, including machine learning, deep learning, and neural networks. By exploring real-world case studies and industry applications, participants will discover the transformative potential of AI in domains such as healthcare, finance, marketing, and more.

Key Learning Objectives:

• Understand AI Fundamentals: Gain a clear understanding of AI, its core principles, and its relevance in today's world.
• Explore AI Technologies: Learn about machine learning algorithms, neural networks, and deep learning architectures, and understand how these technologies power AI applications.
• Discover Real-World AI Applications: Explore diverse industries where AI is making a significant impact, including healthcare, finance, customer service, and transportation.
• Ethical and Social Considerations: Explore the ethical challenges and implications of AI, such as bias, fairness, privacy, and accountability, and learn strategies to address them responsibly.
• Foster AI Literacy: Develop the ability to speak and engage in discussions about AI, understand AI-related terminology, and make informed decisions regarding AI implementation.

Course Structure:

AI for Everyone is a self-paced course, allowing learners to progress at their own speed. The course consists of high-quality video lectures delivered by industry experts, practical exercises, and quizzes to reinforce learning. Additionally, learners will have the opportunity to connect and collaborate with a global community of AI enthusiasts, enabling valuable discussions and peer-to-peer learning.

Completion of the course provides learners with a certificate, highlighting their newfound AI knowledge and skills. Whether you are a business professional, manager, policymaker, or simply curious about AI, AI for Everyone is the perfect starting point to unlock the potential of artificial intelligence in today's rapidly evolving world.

Question 1)

Machine learning is an “iterative” process, meaning that an AI team often has to try many ideas before coming up with something that’s good enough, rather than have the first thing they try work.

• True
• False

Question 2)

Say you want to use Machine Learning to help your sales team with automatic lead sorting. I.e., Input A (a sales prospect) and output B (whether your sales team should prioritize them). The 3 steps of the workflow, in scrambled order, are:

(i) Deploy a trained model and get data back from users

(ii) Collect data with both A and B

(iii) Train a machine learning system to input A and output B

What is the correct ordering of these steps?

• (i) (ii) (iii)
• (ii) (iii) (i)
• (ii) (i) (iii)
• (i) (iii) (ii)

Question 3)

What are the key steps of a Data Science project?

• Collect data
• Analyze the data
• Suggest hypothesis or actions
• All of the above

Question 4)

Machine Learning programs can help: (select all that apply)

•  Automate resume screening
•  Automate lead sorting in sales
•  Customize product recommendations
•  Automate visual inspection in a manufacturing line

Question 5)

Unless you have a huge dataset (“Big Data”), it is generally not worth attempting machine learning or data science projects on your problem.

• True
• False

Question 6)

Say you want to build an AI system to help recruiters with automated resume screening. Which of these steps might be involved in “technical diligence” process? (Select all that apply.)

• Making sure that an AI system can meet the desired performance
• Making sure you can get enough data for this project

• Ensuring that this is valuable for your business (e.g., estimating the project ROI)

• Defining an engineering timeline

Question 7)

Which of these statements about “business diligence” do you agree with?

• Business diligence applies only if you are launching new product lines or businesses.
• Business diligence is the process of ensuring that the envisioned AI technology is feasible.
• Business diligence is the process of ensuring that the AI technology, if it is built, is valuable for your business.
• Business diligence can typically be completed in less than a day.

Question 8)

You want to use supervised learning for automated resume screening, as in the example above. Which of the following statements about the Training Set are true? (Select all that apply.)

• The Training set and Test set can be the same dataset.
• It should give examples of the input A (resume) but not necessarily the desired output B (whether to move forward with a candidate).
• It will be used by the AI team to train the supervised learning algorithm.
• It should give examples of both the input A (resume) and the desired output B (whether to move forward with a candidate).

Question 9)

For your automated resume screening application, you are now providing a Test Set to the AI team. Which of the following statements about the Test Set are true? (Select all that apply.)

• It should give examples of the input A (resume) but not necessarily the desired output B (whether to move forward with a candidate).
• The Test Set should ideally be identical to the Training Set.
• It will be used by the AI team to evaluate the performance of the algorithm.
• It should give examples of both the input A (resume) and the desired output B (whether to move forward with a candidate)

Question 10)

Which of these are reasons that it’s often unrealistic to expect an ML system to be 100% accurate?

• Data can be mislabeled
• You might not have enough data
• Data can be ambiguous
• All of the above.

Conclusion

 

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