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Coursera: Machine Learning - All weeks solutions [Assignment + Quiz] - Andrew NG
![Coursera: Machine Learning - All weeks solutions [Assignment + Quiz] - Andrew NG](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEih0B0NpVY1WWh5W-aPiZ5fckewDLdTtMqzpdakJYFDfq421qiuG_LyCqsaUXx-1Eqjb65iUZUtLzYTInm6843femcLYi3owzOlYr2qvS7XRvlrkLbRLK-vPoqV5xw-Tl68jidbeUT650I/w320-h200/Coursera+Machine+Learning+-+All+weeks+solutions+%255BAssignment+%252B+Quiz%255D+-+Andrew+NG.webp "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.
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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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Category: Coursera
Data structures and performance | week 6, data structures and performance | week 5, data structures and performance | week 4, data structures and performance | week 3, data structures and performance | week 2, data structures and performance | week 1, algorithmic toolbox | week 6, algorithmic toolbox | week 5, algorithmic toolbox | week 4, algorithmic toolbox | week 3.
Don’t Pay For Coursera: How to Get Free Access to Top Courses
Coursera offers hundreds of top courses for a fee.
And while that may be the case, you don’t have to pay to access the material. There are ways to access free Coursera courses in any field, from Productivity to Data Science and more.
A few examples of these top free Coursera courses include:
- Writing in the Sciences . This course teaches beginner writers how to compose excellent scientific publications for general audiences. It’s a self-paced course that takes 30 hours to complete. Up to 20% of students have advanced their careers—for example, earning a higher salary —after taking this course.
- Supervised Machine Learning: Regression and Classification . This beginner-level course introduces students to advanced machine-learning concepts by teaching them to build prediction and classification models in Python. The course is self-paced and takes 33 hours to complete.
- Brand Management: Aligning Business, Brand, and Behavior . This 17-hour-long, self-paced course teaches students how to build a brand identity that offers a superior customer experience. Course statistics show that 33% of those who completed this course launched a new career.
- Successful Negotiation: Essential Strategies and Skills . This 17-hour course imparts the skills necessary to effectively communicate with employers, businesses, and other people to get what you want, e.g., a job opportunity , better contract terms for a partnership, or handling a difficult client .
All courses on Coursera offer a certificate upon completion. However, you can only get a certificate once you’ve paid for a class. So although you can complete a course for free, you won’t be able to share your accomplishments on LinkedIn or with your employer until you’ve paid.
That said, how do you go about enrolling for these or other top courses for free on Coursera?
Step 1: Audit a Course
Auditing a course means gaining free access to some parts of a paid class.
However, when you audit a course, you won’t receive a grade or certification. Therefore, you should only use this option if you:
- Want to explore a subject out of curiosity
- Feel that a course might provide you with extra knowledge that complements a course you’re majoring in
Auditing is only an option for specializations and Professional Certificate courses.
- A Professional Certificate Course is a collection of several standalone courses created by a company (such as Google or LinkedIn) with expertise in a professional area. For example, Google UX Design Professional Certificate has courses including Foundations of User Experience (UX) Design , Responsive Web Design in Adobe XD , and Build WireFrames and Low-Fidelity Prototypes .
- A course specialization is a collection of related courses designed to help learners master a professional field . Specializations are offered by a learning institution and not a company. A good example is the user experience specialization course provided by the University of Michigan.
To audit a course:
- Type in the course name or keyword in the search box at the top of Coursera’s home page.
- Click the “Explore” button next to the Coursera logo and select your course by goal or subject.
- Pick a course category from the “popular courses and certificates” section at the bottom of the web page. Once the category page loads, you can scroll through the courses to find one that piques your interest.
- Click on a specialization to load its landing page.
- From the course’s landing page, scroll to the section that lists the number of specialization or professional certificate classes offered.
- Click on the course you’d like to audit. This will load a landing page.
- In the hero section of the landing page, click “Enroll for Free”. This action loads a 7-day free trial pop-up window.
- You’ll find the “Audit the Course” option at the bottom of the free trial window. Click on it to get free access to all course material.
Step 2: Sign Up for the Coursera for Campus Basic Plan
Coursera for Campus is designed for university and college-level use. The program has three plans: Basic, Career Academy, and Full Catalog.
The basic plan is free for the first 500 students.
To join Coursera for Campus, a faculty member (such as a department head) needs to create an account using an email address provided by your institution. They can then admit students to the program.
If you’re one of the first 500 students registered under the Coursera for Campus basic plan, you will:
- Get unlimited access to course content and assessments
- Receive a verified certificate for every course you complete
Step 3: Enroll for a Full Paid Course Without Certification
The only way to access a course in full without paying (and without receiving certification) is to do so with a standalone course . You can’t use this method with any of the other course types on Coursera.
You’ll notice that when you choose a standalone course, two course-enrollment options will come up. The first option is Purchase Course while the second one is Full Course, No Certificate .
Choosing “Purchase Course” will prompt you to pay for the course first while “Full Course, No Certificate” will allow you to access course content and even have your work graded.
It’s worth noting, however, that you will not get a certificate after completing a full course without certification.
Once you load the web page of the standalone course you’re interested in, proceed as follows:
- Click the “Enroll for Free” option on the page. Doing so will load a pop-up showing the two enrollment options discussed above.
- Since your aim is to get free access to a course, click “Full Course, No Certificate” and then click “Continue” to be directed to the learning material.
So far, we’ve looked at the different methods you can use to access free courses on Coursera, and now you know how to level up without spending a penny.
But at this point, you must be wondering, “Is Coursera the only place I can go to learn a new skill online?”
Absolutely not.
While there’s nothing wrong with wanting to learn each and every skill you can think of on Coursera, it’s important to explore other learning options as well. After all, a single course platform may not have all the courses you need.
For example, Coursera is great for access to longer courses. But if you’re looking for a course in a niche area to complete in under an hour, Coursera may not be the right place. A platform like Udemy might be more suitable for shorter specialized courses.
To give you a better idea of what you stand to gain from Coursera or Udemy, let’s compare the two platforms below.
Coursera vs Udemy
There are several differences between Udemy and Coursera. Firstly, educators and large companies such as Google create the courses on Coursera, while individuals—like you and me—create the courses on Udemy .
Secondly, the majority of courses on Coursera are long and take months to complete.
By contrast, the courses on Udemy tend to be short, and some can even be completed within an hour. This makes Udemy an excellent alternative to Coursera when your aim is to learn and apply a skill immediately.
For example, How to Design & Prototype in Adobe XD is a short Udemy course that’s approximately one hour long. If, for example, you take this course, you will learn how to navigate around Adobe XD and other design apps, in just one hour.
The table below provides a comparison of Udemy to Coursera to help you understand these two platforms even better.
| Best for | Budget-friendly courses customized to your current work needs | Take weeks or months to gain in-depth knowledge in a career field (e.g., Machine Learning) |
| Budget-friendly | Costly, but instructors frequently discount courses to as low as $12 | Cost varies, but you should expect to pay between $49 and $79 for a standalone course |
| Partnerships | Anyone can create and upload courses on Udemy | Only partners with accredited universities and leading companies to create and offer courses |
| Community | No community, so no additional support | Accessible forum where students can share ideas and receive help |
At this point, we hope you have a clear understanding of the differences between Coursera and Udemy.
To summarize, Udemy is a great place to start if you aren’t prepared to pursue longer and more complex free courses on Coursera.
Sign up for Udemy today to acquire new skills now.
Frequently Asked Questions
Will i get a certificate after i audit a course on coursera .
You’ll have access to learning material and assessments when auditing a course on Coursera, but you will not get a certificate.
However, you will get a certificate if you pay for a course you’ve audited.
If I enroll for a full Coursera course without a certificate, will I get the certificate later?
If you enroll for a full course with no certificate, Coursera will certify you in future for that course once it’s been paid for.
Are all Coursera certificates internationally recognized?
All certificates on Coursera are internationally recognized because they’re offered through reputable universities and companies.
Can I take a degree course for free on Coursera?
Only standalone courses and courses within Specializations and Professional Certificates can be accessed for free, not degree courses.
Is there a limit to the number of courses I can access for free on Coursera?
As long as you have time, you can enroll in any number of free courses on Coursera.
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Katrina McKinnon
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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 Network…
amanchadha/coursera-deep-learning-specialization
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Deep learning specialization on coursera (offered by deeplearning.ai).
Programming assignments and quizzes from all courses in the Coursera Deep Learning specialization offered by deeplearning.ai .
Instructor: Andrew Ng
For detailed interview-ready notes on all courses in the Coursera Deep Learning specialization, refer www.aman.ai .
Run setup.sh to (i) download a pre-trained VGG-19 dataset and (ii) extract the zip'd pre-trained models and datasets that are needed for all the assignments.
This repo contains my work for this specialization. The code base, quiz questions and diagrams are taken from the Deep Learning Specialization on Coursera , unless specified otherwise.
2021 Version
This specialization was updated in April 2021 to include developments in deep learning and programming frameworks, with the biggest change being shifting from TensorFlow 1 to TensorFlow 2. This repo has been updated accordingly as well.
Programming Assignments
Course 1: neural networks and deep learning.
- Week 2 - PA 1 - Python Basics with Numpy
- Week 2 - PA 2 - Logistic Regression with a Neural Network mindset
- Week 3 - PA 3 - Planar data classification with one hidden layer
- Week 4 - PA 4 - Building your Deep Neural Network: Step by Step
- Week 4 - PA 5 - Deep Neural Network for Image Classification: Application
Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Week 1 - PA 1 - Initialization
- Week 1 - PA 2 - Regularization
- Week 1 - PA 3 - Gradient Checking
- Week 2 - PA 4 - Optimization Methods
- Week 3 - PA 5 - TensorFlow Tutorial
Course 3: Structuring Machine Learning Projects
- There are no programming assignments for this course. But this course comes with very interesting case study quizzes (below).
Course 4: Convolutional Neural Networks
- Week 1 - PA 1 - Convolutional Model: step by step
- Week 1 - PA 2 - Convolutional Neural Networks: Application
- Week 2 - PA 1 - Keras - Tutorial - Happy House
- Week 2 - PA 2 - Residual Networks
- Week 2 - PA 2 - Transfer Learning with MobileNet
- Week 3 - PA 1 - Car detection with YOLO for Autonomous Driving
- Week 3 - PA 2 - Image Segmentation Unet
- Week 4 - PA 1 - Art Generation with Neural Style Transfer
- Week 4 - PA 2 - Face Recognition
Course 5: Sequence Models
- Week 1 - PA 1 - Building a Recurrent Neural Network - Step by Step
- Week 1 - PA 2 - Dinosaur Land -- Character-level Language Modeling
- Week 1 - PA 3 - Jazz improvisation with LSTM
- Week 2 - PA 1 - Word Vector Representation and Debiasing
- Week 2 - PA 2 - Emojify!
- Week 3 - PA 1 - Neural Machine Translation with Attention
- Week 3 - PA 2 - Trigger Word Detection
- Week 4 - PA 1 - Transformer Network
- Week 3 - PA 2 - Transformer Network Application: Named-Entity Recognition
- Week 3 - PA 2 - Transformer Network Application: Question Answering
Quiz Solutions
- Week 1 Quiz - Introduction to deep learning: Text | PDF
- Week 2 Quiz - Neural Network Basics: Text | PDF
- Week 3 Quiz - Shallow Neural Networks: Text | PDF
- Week 4 Quiz - Key concepts on Deep Neural Networks: Text | PDF
- Week 1 Quiz - Practical aspects of deep learning: Text | PDF
- Week 2 Quiz - Optimization algorithms: Text | PDF
- Week 3 Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks: Text | PDF
- Week 1 Quiz - Bird recognition in the city of Peacetopia (case study): Text | PDF
- Week 2 Quiz - Autonomous driving (case study): Text | PDF
- Week 1 Quiz - The basics of ConvNets: Text | PDF
- Week 2 Quiz - Deep convolutional models: Text | PDF
- Week 3 Quiz - Detection algorithms: Text | PDF
- Week 4 Quiz - Special applications: Face recognition & Neural style transfer: Text | PDF
- Week 1 Quiz - Recurrent Neural Networks: Text | PDF
- Week 2 Quiz - Natural Language Processing & Word Embeddings: PDF
- Week 3 Quiz - Sequence models & Attention mechanism: Text | PDF
I recognize the time people spend on building intuition, understanding new concepts and debugging assignments. The solutions uploaded here are only for reference . They are meant to unblock you if you get stuck somewhere. Please do not copy any part of the code as-is (the programming assignments are fairly easy if you read the instructions carefully). Similarly, try out the quizzes yourself before you refer to the quiz solutions. This course is the most straight-forward deep learning course I have ever taken, with fabulous course content and structure. It's a treasure by the deeplearning.ai team.
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answers natural-language-processing deep-learning time-series image-processing coursera image-classification image-recognition quiz convolutional-neural-networks references sequence coursera-machine-learning prediction-model coursera-assignment deeplearning-ai coursera-solutions tendorflow coursera-answers
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 tutorial you have will learn way of grading your peer graded assignment.
Assignments with time limits. When you open an assignment, you'll see the estimated time to complete it near the top of the page. Some assignments also have a time limit that is set by your instructor. This will be listed on the page before you start the assignment. During a timed assignment, you'll see a countdown timer while you're ...
Opt out of AI grading: On the assignment page, click Switch to peer grading. This will remove the AI grade and open up your assignment for your peers to review. After you switch to peer grading, it may take 7-10 days to receive a grade on your assignment.
163. Linear regression and get to see it work on data. I have recently completed the Machine Learning course from Coursera by Andrew NG. While doing the course we have to go through various quiz and assignments. Here, I am sharing my solutions for the weekly assignments throughout the course. These solutions are for reference only.
Mastering Peer-Graded Assignments on Coursera: Tips and Tricks • Mastering Peer-Graded Assignments • Discover the secrets to successfully completing and revi...
Troubleshoot quizzes & assignments. This article will help you troubleshoot assessments in your course. If you're having a problem with peer-reviewed assignments, check our troubleshooting page for peer reviewed assignments. Some assessments use third-party tools (these will say External Tool when you view them).
Click here to see solutions for all Machine Learning Coursera Assignments. Click here to see more codes for Raspberry Pi 3 and similar Family. Click here to see more codes for NodeMCU ESP8266 and similar Family. Click here to see more codes for Arduino Mega (ATMega 2560) and similar Family. Feel free to ask doubts in the comment section. I will try my best to answer it.
everything you need to complete coursera assignments is covered in this video.. i hope you all like it.!Also check out this : https://youtu.be/JnG6W7S7yU4?si...
Some code have comments, Please read them to get your answers. All the codes follow the standard pattern prescribed by the Course instrucutor. Instruction for Quiz document - The quiz document are in the form of Word Document. Each Document has Screenshot of the right answer along with the questions.
Category: Coursera. Get your Coursera quiz, exam, assignment and guided projects solutions and answers by progiez. Get your peer grade assignment done by progiez.
This will load a landing page. In the hero section of the landing page, click "Enroll for Free". This action loads a 7-day free trial pop-up window. You'll find the "Audit the Course" option at the bottom of the free trial window. Click on it to get free access to all course material.
It's Rounak here I am here to help you with your Coursera Courses. With our help solve the quizzes and assignments from Coursera. Here I will show you how to solve Quizzes and Assignments in the ...
Coursera Support & Community. Home; More. Expand search. Close search. Login. Learner Help Center Grades & assignments. Grades & assignments. Grades, peer reviews, assignments, and Labs. Peer-graded assignments. Submit peer-graded assignments. Write peer reviews. Edit and re-submit a peer-graded assignment.
There are 4 modules in this course. This course gives you easy access to the invaluable learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. We'll learn about how the brain uses two very different learning modes and how it encapsulates ("chunks") information.
Contains Solutions and Notes for the Machine Learning Specialization by Andrew NG on Coursera Note : If you would like to have a deeper understanding of the concepts by understanding all the math required, have a look at Mathematics for Machine Learning and Data Science
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
Personality questions help an interviewer understand how you work in a team and what unique qualities you can bring to the work environment. When answering these questions, share relevant stories about your past work life and bring up skills and qualities that may help you excel in an HR generalist position.
View deadlines. To see your deadlines and incomplete assessments: Log in to Coursera. Click the In Progress tab to see a list of courses you're enrolled in. Find the course you'd like to see the schedule for and click its name. Click the Grades tab. Check a specific module to see deadlines for that module's assessments.
This repository contains all solutions for the course Algorithmic Toolbox offered on Coursera. The assignment solutions are in Python3. Disclaimer: The below solutions are for reference only.Please design and implement your own algorithms to pass the course.
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 Networks; (v) Sequence Models - amanchadha/coursera-deep ...
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
Answer: To reactivate your inactive status on Coursera, go to your "My Purchases" page, update your payment status and pay again. Once you have updated your payment status, you are able to re-enroll and continue your course. Or, if you go to Specialization or Professional Certificate main page and click on the "Enroll for free" button it will ...
Grades & assignments. Grades, peer reviews, assignments, and Labs ... Watch tutorials on the Coursera learner experience. Popular articles. Payments on Coursera. Apply for financial aid ... Troubleshooting login and account issues. Cancel a subscription. Connect with learners around the world. Ask questions and help others, discuss subjects you ...
To answer this one, think about your goals and values in the workplace. Show that you know what you are looking for by speaking clearly and confidently, and frame your questions around what you need to succeed in this role. If it feels right, you might even react to their response by reinforcing how their answers align with what you can offer.