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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
progiez/nptel-assignment-answers
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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!
List of Courses
Here's a list of courses currently available in this repository:
- Artificial Intelligence Search Methods for Problem Solving
- Cloud Computing
- Computer Architecture
- Cyber Security and Privacy
- Data Science for Engineers
- Data Structure and Algorithms Using Java
- Database Management System
- Deep Learning for Computer Vision
- Deep Learning IIT Ropar
- Digital Circuits
- Ethical Hacking
- Introduction to Industry 4.0 and Industrial IoT
- Introduction to Internet of Things
- Introduction to Machine Learning IIT KGP
- Introduction to Machine Learning
- Introduction to Operating Systems
- ML and Deep Learning Fundamentals and Applications
- Problem Solving Through Programming in C
- Programming DSA Using Python
- Programming in Java
- Programming in Modern C
- Python for Data Science
- Soft Skill Development
- Soft Skills
- Software Engineering
- Software Testing
- The Joy of Computation Using Python
- Theory of Computation
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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🚀 About Progiez
Progiez is an online educational platform aimed at providing solutions to various online courses offered by NPTEL, Coursera, LinkedIn Learning, and more. Explore our resources for detailed answers and solutions to enhance your learning experience.
Disclaimer: This repository is intended for educational purposes only. All content is provided for reference and should not be submitted as your own work.
COMMENTS
This video explains how we can upload programming assignments in coursera.
To submit a programming assignment: Open the assignment. Read the instructions and download any starter files. Complete the coding tasks in your local coding environment. Submit the assignment. Programming assignments will use one of two submission methods: Script submission: Run your code in your local coding environment, then enter the ...
How to submit coursera 'Machine Learning' Andrew Ng Assignment. Here is complete guidance of submission in matlab environment.Best suggestion to do it in Ma...
Here is complete guid ...more WATCH MODIFIED VIDEO: https://www.youtube.com/edit?video_id... How to submit coursera 'Machine Learning' Andrew Ng Assignment.
Coursera Help Center Loading × Sorry to interrupt CSS Error Refresh
To submit a peer-graded assignment: Navigate to the week or module that the peer-graded assignment is in, then open it. Review the Instructions for the assignment. The course instructor typically provides requirements, submission instructions, and tips. Complete the assignment by responding to each prompt.
There is no guarantee that the programming assignment submit method will work with older versions. Before you attempt the first programming exercise, watch all of the video lectures for Week 1 and Week 2.
Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera - nsoojin/coursera-ml-py
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 Win...
Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions. - dibgerge/ml-coursera-python-assignments
This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning ...
In this video, I am talking about how to upload your machine learning by Andrew Ng Course assignment in Coursera.Octave Download link for Windows Users:-http...
This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of ...
Coursera: Machine Learning - All weeks solutions [Assignment + Quiz] - Andrew NG. by Akshay Daga (APDaga) - April 25, 2021. 4. The complete week-wise solutions for all the assignments and quizzes for the course "Coursera: Machine Learning by Andrew NG" is given below: Recommended Machine Learning Courses:
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural ...
In this video, I teach you how to upload Coursera machine learning assignment without any error. This video is all about Stanford University Machine Learning Course.
It gives insights into the application of AI in the upstream sector of the industry, exploring its use in exploration, drilling and production optimization. Advances in AI technology specific to the oil and gas industry is covered, including the integration of machine learning with sensor data, predictive maintenance and anomaly detection.
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG - greyhatguy007 ...
If you are unable to complete the week 2 assignment Linear Regression Ex1 of Coursera Machine Learning, then You are in the right place to complete it with the easiest and fastest process. #2021 ...
How to Submit all Machine Learning Coursera Practical/Programming Assignment|Watch Full Video| TECH GHOST 104 subscribers Subscribed 95 5.6K views 2 years ago #coursera #courses #machinelearning
Coursera - Practical Machine Learning - Prediction Assignment Writeup The goal of your project is to predict the manner in which they did the exercise. This is the "classe" variable in the training set. You may use any of the other variables to predict with.
Solutions to the 'Applied Machine Learning In Python' Coursera course exercises - amirkeren/applied-machine-learning-in-python
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This repository consists my personal solutions to the programming assignments of Andrew Ng's Machine Learning course on Coursera.
NPTEL Assignment Answers and Solutions 2024 (July-Dec). ... introduction-to-machine-learning-iit-kgp ... Coursera, LinkedIn Learning, and more. Explore our resources for detailed answers and solutions to enhance your learning experience. Disclaimer: This repository is intended for educational purposes only. All content is provided for reference ...