how to submit assignment in coursera machine learning

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Submitting Assignment on Coursera ML in Octave

Programming assignment Week 3, Machine Learning, Andrew-ng, Coursera System: Ubuntu 16.04 Octave 4.0.0

Problem: Cannot submit the code to the server. This code was successfully submitted from Windows env.

• machine-learning

• 1 as mentioned in the instructor's notes you should use octave version > 4 –  Sudip Bhandari Commented Mar 30, 2018 at 12:37
• 1 use higher version say 4.0.2, –  Sandeep_black Commented Apr 1, 2018 at 8:48

3 Answers 3

Octave 4.0.0 doesn't work well with submit scripts (on Ubuntu).

Check the version on your terminal:

if it's 4.0.0 update it.

There is also warning in the discussions.

• Yeah, it seems problem with version. Thank you it helped. –  Muhriddin Ismoilov Commented Aug 31, 2017 at 8:26
• 2 Do not use Octave 4.0.0. It has a defect in one of the functions that are used to submit your work. We should NOT be using Octave 3.8.x - it is obsolete and the grader process does not work correctly with certain linux-derived operating systems. More infor at the following thread on Coursera coursera.org/learn/machine-learning/discussions/all/threads/… –  Praneesh Commented Oct 20, 2017 at 10:02

Upgrading to a new version worked in my case. 4.0.0 is not working and giving the error persistently. I installed GNU Octave, version 4.2.1 and it worked.

Although Vaibhav Pandey 's answer is a sound advice (upgrade to 4.2.x or higher), I believe this specific error message is caused by not explicitly typing in your email address.

The "submit" prompt is a bit misleading, as Login (email address): can be misinterpreted as "your email has been stored somewhere as a default", when in fact you have to input it explicitly again.

After some hair pulling on OSX, I solved this:

by simply explicitly entering my email address.

Not the answer you're looking for? Browse other questions tagged machine-learning submit octave or ask your own question .

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Coursera: Machine Learning - All weeks solutions [Assignment + Quiz] - Andrew NG

Recommended Machine Learning Courses: Coursera: Machine Learning    Coursera: Deep Learning Specialization Coursera: Machine Learning with Python Coursera: Advanced Machine Learning Specialization Udemy: Machine Learning LinkedIn: Machine Learning Eduonix: Machine Learning edX: Machine Learning Fast.ai: Introduction to Machine Learning for Coders

=== Week 1 ===

Assignments: .

• No Assignment for Week 1
• Machine Learning (Week 1) Quiz ▸  Introduction
• Machine Learning (Week 1) Quiz ▸  Linear Regression with One Variable
• Machine Learning (Week 1) Quiz ▸  Linear Algebra

=== Week 2 ===

Assignments:.

• Machine Learning (Week 2) [Assignment Solution] ▸ Linear regression and get to see it work on data.
• Machine Learning (Week 2) Quiz ▸  Linear Regression with Multiple Variables
• Machine Learning (Week 2) Quiz ▸  Octave / Matlab Tutorial

=== Week 3 ===

• Machine Learning (Week 3) [Assignment Solution] ▸ Logistic regression and apply it to two different datasets
• Machine Learning (Week 3) Quiz ▸  Logistic Regression
• Machine Learning (Week 3) Quiz ▸  Regularization

=== Week 4 ===

• Machine Learning (Week 4) [Assignment Solution] ▸ One-vs-all logistic regression and neural networks to recognize hand-written digits.
• Machine Learning (Week 4) Quiz ▸  Neural Networks: Representation

=== Week 5 ===

• Machine Learning (Week 5) [Assignment Solution] ▸ Back-propagation algorithm for neural networks to the task of hand-written digit recognition.
• Machine Learning (Week 5) Quiz ▸  Neural Networks: Learning

=== Week 6 ===

• Machine Learning (Week 6) [Assignment Solution] ▸ Regularized linear regression to study models with different bias-variance properties.
• Machine Learning (Week 6) Quiz ▸  Advice for Applying Machine Learning
• Machine Learning (Week 6) Quiz ▸  Machine Learning System Design

=== Week 7 ===

• Machine Learning (Week 7) [Assignment Solution] ▸ Support vector machines (SVMs) to build a spam classifier.
• Machine Learning (Week 7) Quiz ▸  Support Vector Machines

=== Week 8 ===

• Machine Learning (Week 8) [Assignment Solution] ▸ K-means clustering algorithm to compress an image. ▸ Principal component analysis to find a low-dimensional representation of face images.
• Machine Learning (Week 8) Quiz ▸  Unsupervised Learning
• Machine Learning (Week 8) Quiz ▸  Principal Component Analysis

=== Week 9 ===

• Machine Learning (Week 9) [Assignment Solution] ▸ Anomaly detection algorithm to detect failing servers on a network. ▸ Collaborative filtering to build a recommender system for movies.
• Machine Learning (Week 9) Quiz ▸  Anomaly Detection
• Machine Learning (Week 9) Quiz ▸  Recommender Systems

=== Week 10 ===

• No Assignment for Week 10
• Machine Learning (Week 10) Quiz ▸  Large Scale Machine Learning

=== Week 11 ===

• No Assignment for Week 11
• Machine Learning (Week 11) Quiz ▸  Application: Photo OCR Variables

Question 5 Your friend in the U.S. gives you a simple regression fit for predicting house prices from square feet. The estimated intercept is -44850 and the estimated slope is 280.76. You believe that your housing market behaves very similarly, but houses are measured in square meters. To make predictions for inputs in square meters, what intercept must you use? Hint: there are 0.092903 square meters in 1 square foot. You do not need to round your answer. (Note: the next quiz question will ask for the slope of the new model.) i dint get answer for this could any one plz help me with it

Please comment below specific week's quiz blog post. So that I can keep on updating that blog post with updated questions and answers.

This comment has been removed by the author.

Good day Akshay, I trust that you are doing well. I am struggling to pass week 2 assignment, can you please assist me. I am desperate to pass this module and I am only getting 0%... Thank you, I would really appreat your help.

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NPTEL Assignment Answers and Solutions 2024 (July-Dec). Get Answers of Week 1 2 3 4 5 6 7 8 8 10 11 12 for all courses. This guide offers clear and accurate answers for your all assignments across various NPTEL courses

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Nptel assignment answers 2024 with solutions (july-dec), how to use this repo to see nptel assignment answers and solutions 2024.

If you're here to find answers for specific NPTEL courses, follow these steps:

Access the Course Folder:

• Navigate to the folder of the course you are interested in. Each course has its own folder named accordingly, such as cloud-computing or computer-architecture .

Locate the Weekly Assignment Files:

• Inside the course folder, you will find files named week-01.md , week-02.md , and so on up to week-12.md . These files contain the assignment answers for each respective week.

Select the Week File:

• Click on the file corresponding to the week you are interested in. For example, if you need answers for Week 3, open the week-03.md file.

Review the Answers:

• Each week-XX.md file provides detailed solutions and explanations for that week’s assignments. Review these files to find the information you need.

By following these steps, you can easily locate and use the assignment answers and solutions for the NPTEL courses provided in this repository. We hope this resource assists you in your studies!

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• Introduction to Machine Learning IIT KGP
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Note: This repository is intended for educational purposes only. Please use the provided answers as a guide to better understand the course material.

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Disclaimer: This repository is intended for educational purposes only. All content is provided for reference and should not be submitted as your own work.

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