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NPTEL Introduction to Machine Learning Assignment 8 Answer

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NPTEL Introduction to Machine Learning Assignment 8 Answer – Here All The Questions and Answers Provided to Help All The Students and NPTEL Candidate as a Reference Purpose, It is Mandetory to Submit Your Weekly Assignment By Your Own Understand Level.

Are you looking for the Assignment Answers to NPTEL Introduction to Machine Learning Assignment 8 Answer? If Yes You are in Our Great Place to Getting Your Solution, This Post Should be help you with the Assignment answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “NPTEL Introduction to Machine Learning Assignment 8 Answer”

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NPTEL Introduction to Machine Learning Assignment

ABOUT THE COURSE :
With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.
INTENDED AUDIENCE : This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD
PREREQUISITES : We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well.
INDUSTRY SUPPORT : Any company in the data analytics/data science/big data domain would value this course

Next Week Assignment Answers

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This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.

Final score = Assignment score + Unproctored programming exam score + Proctored Exam score
  • Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
  • ( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments). 
  • Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
  • Proctored Exam score =50% of the proctored certification exam score out of 100
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF ASSIGNMENT SCORE >=10/25 AND
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50. 
If any one of the 3 criteria is not met, you will not be eligible for the certificate even if the Final score >= 40/100. 

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1 point
The figure below shows a Bayesian Network with 9 variables, all of which are binary.

Which of the following is/are always true for the above Bayesian Network?

Ans  –  C,D
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1 point
Consider the following data for 20 budget phones, 30 mid-range phones, and 20 high-end phones:

Consider a phone with 2 SIM card slots and NFC but no 5G compatibility. Calculate the probabilities of this phone being a budget phone, a mid-range phone, and a high-end phone using the Naive Bayes method. The correct ordering of the phone type from the highest to the lowest probability is?

 
 
 
 
Ans  –  C
1 point
Consider the following dataset where outlook, temperature, humidity, and wind are independent features, and play is the dependent feature.

Find the probability that the student will not play given that x = (Outlook=sunny, Temperature=66, Humidity=90, Windy=True) using the Naive Bayes method. (Assume the continuous features are represented as Gaussian distributions).

 
 
 
 
Ans  –  C
1 point
Which among Gradient Boosting and AdaBoost is less susceptible to outliers considering their respective loss functions?
 
 
 
Ans  –  B
1 point
How do you prevent overfitting in random forest models?
 
 
 
 
Ans  –  D
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1 point
A dataset with two classes is plotted below.

Does the data satisfy the Naive Bayes assumption?

 
 
 
 
Ans  –  A
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1 point
Ensembling in random forest classifier helps in achieving:
 
 
 
 
Ans  –  C
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