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What is Coursera and How Does It Work?

Techboomers is your online source for learning the basics of using the most popular websites and applications on the Internet in simple, easy-to-understand steps (or at least, we hope we are!).  Now, wouldn’t it be nice if there was a website like us that could teach you skills normally reserved for post-secondary education, without the monetary and logistic conundrums of actually setting foot on a campus?  One of the best websites in that model is Coursera.org .

Just a heads-up that some of the services we’re reviewing here have affiliate partnerships with us, so we may earn a commission if you visit one of them and buy something. You can read more about how this works at https://techboomers.com/how-to-support-techboomers .

Coursera has partnered with top universities and educational organizations across the United States and beyond to make post-secondary learning available through the Internet via massively-open online courses (M.O.O.C.s).  Coursera offers courses in subjects such as business, engineering, computer technology, social science, medicine, and more!  These courses are available in several countries around the world, and some can even count as credits towards completing a degree!

So what exactly is Coursera?

Coursera is an open online course website featuring post-secondary courses from top global academic institutions.  Most courses are free to take, and consist of watching lecture videos and presentations, doing readings, holding discussions with other students, and completing assignments and quizzes.

Accreditation is available for some courses (and groups of courses), but requires additional processing, such as verifying your identity on assignments and paying money.

How does Coursera work?  3 steps to start learning

1. sign up for a free account, and choose the subject fields that you’re interested in..

Signing up for Coursera is easy and costs you no money.  Simply enter your name, email address, and a password, or log in through your Facebook account.  Then tell verify your email address and tell Coursera what you’d like to learn about, and you’re done!

2. Pick the courses that you want to take, and decide whether you want accreditation from them, or just the knowledge.

Coursera has nearly 1500 university-level courses for you to discover, spread across 9 broad academic disciplines.  You can get recommendations based on your preferred disciplines, or search for a particular course on your own.  Some courses are open year-round, while others have specific enrollment dates and run times, so be sure to look for this information before you sign up!

Many courses allow you to earn a “learner’s certificate” that you can show off on your résumé, or (in rare cases) credit towards a university degree.  And some courses are part of “specializations”, groups of related courses that let you master a particular field of study all in one go!  (Note that both of these things cost money , though.)

3. Get down to studying with readings, lectures, class discussions, assignments and quizzes, and more!

Once you’ve joined a course or two on Coursera, it’s time to hit the books!  Check the weekly modules for course information, readings, lecture videos, and presentation slides.  Head to the discussion forums and chat with your fellow Coursera classmates about what you’re learning.  Demonstrate your skills by completing assignments and quizzes.  Each course is different, so the assignments that you receive may work a bit differently for each one.  Just stick to the deadlines, and you’ll be fine!

It’s not quite the same as the on-campus treatment, but Coursera is one of the next best ways to get a higher learning experience from the comfort of your own home.  It features real courses taught by real professors at top colleges and universities around the world.  Best of all, you can use most of it totally for free!  So if you’re ready to pursue your academic aspirations, follow along with our Coursera course as we walk you through everything from signing up to registering for courses to completing your course work and getting that feeling (or perhaps a physical symbol) of achievement!  We won’t be doing your homework for you, though.  Sorry.

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Help Articles

Assessment deadlines, learner help center feb 13, 2023 • knowledge, article details.

Most courses generate deadlines based on a personalized schedule that begins when you enroll in a course. If you’re taking a limited availability course, the info in this article may apply to you.  Learn more about assessment deadlines for limited availability courses.

View deadlines

Missed deadlines, reset your course 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 week to see deadlines for that week's assessments.

Back to top

Missed deadlines don’t affect your grade in most courses. You'll still be able to earn a Course Certificate once you complete all your work.

If you submit a peer-reviewed assignment after your personalized schedule ends, you might not get enough peer reviews. If you need more peer reviews, you can post in the forums asking for more peer feedback.

Note: Degree courses have hard deadlines which can include late penalties. For more information, see Degree course schedules and deadlines.

If you miss two assessment deadlines in a row or miss an assessment deadline by two weeks, you'll see a Reset deadlines option on the Grades page. Click it to switch to a new schedule for the course with updated deadlines. You can use this option as many times as you need.

This won’t remove any progress you’ve already made in the course, but you may see new course content if the instructor updated the course after you started.

If you cancel a subscription and then reactivate it, your deadlines will automatically reset.

Note: Degree courses have hard deadlines which you can’t reset. You may be able to switch sessions if you fall behind. For more information, see Degree course schedules and deadlines.

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Deep Learning Specialization

Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques!

Taught in English

Some content may not be translated

Instructors: Andrew Ng +2 more

Instructors

Top Instructor

Financial aid available

844,494 already enrolled

Specialization - 5 course series

(133,056 reviews)

Recommended experience

Intermediate level

Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures

A basic grasp of linear algebra & ML

What you'll learn

Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications

Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow

Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data

Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

Skills you'll gain

• Recurrent Neural Network
• Convolutional Neural Network
• Artificial Neural Network
• Transformers

Details to know

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Advance your subject-matter expertise

• Learn in-demand skills from university and industry experts
• Master a subject or tool with hands-on projects
• Develop a deep understanding of key concepts
• Earn a career certificate from DeepLearning.AI

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

Applied Learning Project

By the end you’ll be able to:

• Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications

• Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow

• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning

• Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data

• Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering

Neural Networks and Deep Learning

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.

By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Structuring Machine Learning Projects

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Convolutional Neural Networks

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.

By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Sequence Models

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.

By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.

DeepLearning.AI is an education technology company that develops a global community of AI talent. DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.

Get a head start on your degree

When you complete this Specialization, you can earn college credit if you are admitted and enroll in one of the following online degree programs.¹

Ball State University

Master of Science in Computer Science

Degree · 24 months

University of North Texas

Bachelor of Applied Arts and Sciences

Degree · 15+ hours of study/wk per course

Illinois Tech

Master of Data Science

Degree · 12-15 months

Master of Science in Data Science

University of Massachusetts Global

Bachelor of Arts in Psychology

International Institute of Information Technology, Hyderabad

Master of Science in Information Technology

Degree · 2-4 years

¹Each university determines the number of pre-approved prior learning credits that may count towards the degree requirements according to institutional policies.

Degree credit eligible

This Specialization has ACE® recommendation. It is eligible for college credit at participating U.S. colleges and universities. Note: The decision to accept specific credit recommendations is up to each institution. Learn more

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Frequently asked questions

What is deep learning why is it relevant.

Deep Learning is a subset of machine learning where artificial neural networks, algorithms based on the structure and functioning of the human brain, learn from large amounts of data to create patterns for decision-making. Neural networks with various (deep) layers enable learning through performing tasks repeatedly and tweaking them a little to improve the outcome. 

Over the last few years, the availability of computing power and the amount of data being generated have led to an increase in deep learning capabilities. Today, deep learning engineers are highly sought after, and deep learning has become one of the most in-demand technical skills as it provides you with the toolbox to build robust AI systems that just weren’t possible a few years ago. Mastering deep learning opens up numerous career opportunities.

What is the Deep Learning Specialization about?

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

What will I be able to do after completing the Deep Learning Specialization?

By the end of the Deep Learning Specialization, you will be able to:

1. Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications. 2. Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow 3. Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning 4. Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data 5. Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering

What background knowledge is necessary for the Deep Learning Specialization?

Learners should have intermediate Python experience (e.g., basic programming skills, understanding of for loops, if/else statements, data structures such as lists and dictionaries).

Recommended: 

Learners should have a basic knowledge of linear algebra (matrix-vector operations and notation).

Learners should have an understanding of machine learning concepts (how to represent data, what an ML model does, etc.)

Who is the Deep Learning Specialization for?

The Deep Learning Specialization is for early-career software engineers or technical professionals looking to master fundamental concepts and gain practical machine learning and deep learning skills.

How long does it take to complete the Deep Learning Specialization?

The Deep Learning Specialization consists of five courses. At the rate of 5 hours a week, it typically takes 5 weeks to complete each course except course 3, which takes about 4 weeks.

Who is the Deep Learning Specialization by?

The Deep Learning Specialization has been created by Andrew Ng, Kian Katanforoosh, and Younes Bensouda Mourri. 

Andrew Ng Opens in a new tab is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world's largest MOOC platform. 

Kian Katanforoosh Opens in a new tab is the co-founder and CEO of Workera and a lecturer in the Computer Science department at Stanford University. Workera allows data scientists, machine learning engineers, and software engineers to assess their skills against industry standards and receive a personalized learning path. Kian is also the recipient of Stanford’s Walter J. Gores award (Stanford’s highest teaching award) and the Centennial Award for Excellence in teaching.

Younes Bensouda Mourri Opens in a new tab completed his Bachelor's in Applied Mathematics and Computer Science and Master's in Statistics from Stanford University. Younes helped create 3 AI courses at Stanford - Applied Machine Learning, Deep Learning, and Teaching AI - and taught two of them for a few years.

Is this a standalone course or a Specialization?

The Deep Learning Specialization is made up of 5 courses.

Do I need to take the courses in a specific order?

We recommend taking the courses in the prescribed order for a logical and thorough learning experience. Course 3 can also be taken as a standalone course.

Can I apply for financial aid?

Yes, Coursera provides financial aid to learners who cannot afford the fee.

Can I audit the Deep Learning Specialization?

You can audit the courses in the Deep Learning Specialization for free. 

Note that you will not receive a certificate at the end of the course if you choose to audit it for free instead of purchasing it.

How do I get a receipt to get this reimbursed by my employer?

Go to your Coursera account. 

Click on My Purchases and find the relevant course or Specialization.

Click Email Receipt and wait up to 24 hours to receive the receipt. 

You can read more about it here Opens in a new tab .

I want to purchase this Specialization for my employees! How can I do that?

Visit coursera.org/business Opens in a new tab for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.

The Deep Learning Specialization was updated in April 2021. What is different in the new version?

All existing assignments and autograders have been refactored and updated to TensorFlow 2 across Courses 1, 2, 4, and 5.

Three new network architectures are presented with new lectures and programming assignments:

Course 4 includes MobileNet (transfer learning) and U-Net (semantic segmentation).

Course 5, once updated, will include Transformers (Network Architecture, Named Entity Recognition, Question Answering).

For a detailed list of changes, please check out the DLS Changelog Opens in a new tab .

I’m currently enrolled in one or more courses in the Deep Learning Specialization. What does this mean for me?

• Your certificates will carry over for any courses you’ve already completed.

• If your subscription is currently active, you can access the updated labs and submit assignments without paying for the month again.

• If you go to the Specialization, you will see the original version of the lecture videos and assignments. You can complete the original version if so desired (this is not recommended).

• If you would like to update to the new material, reset your deadlines Opens in a new tab . If you’re in the middle of a course, you will lose your notebook work when you reset your deadlines . Please save your work by downloading your existing notebooks before switching to the new version.

• If you do not see the option to reset deadlines, contact Coursera via the Learner Help Center Opens in a new tab .

I’ve already completed one or more courses in the Deep Learning Specialization but don’t have an active subscription. What does this mean for me?

• If your subscription is currently inactive, you will need to pay again to access the labs and submit assignments for those courses.

Can I get college credit for taking the Deep Learning Specialization?

Those planning to attend a degree program can utilize ACE®️ recommendations Opens in a new tab , the industry standard for translating workplace learning to college credit. Learners can earn a recommendation of 10 college credits for completing the Deep Learning Specialization. This aims to help open up additional pathways to learners who are interested in higher education, and prepare them for entry-level jobs.

To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credly Opens in a new tab badge, which contains the ACE®️ credit recommendation.  Once claimed, they will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed. 

How do I pursue the ACE credit recommendation?

To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credly badge, which contains the ACE®️ credit recommendation.  Once claimed, they will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed.

How do I know which colleges and universities grant credit for the Deep Learning Specialization?

The Deep Learning Specialization is eligible for college credit at participating colleges and universities nationwide. The decision to accept specific credit recommendations is up to each institution and not guaranteed. Read more about  ACE Credit College & University Partnerships here Opens in a new tab .

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

What is the refund policy?

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy Opens in a new tab .

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid Opens in a new tab .

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

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Solutions to the Assignments for the Algorithmic Toolbox course offered by UCSanDiego on Coursera.

prantosky/coursera-algorithmic-toolbox

Folders and files, repository files navigation, algorithmic toolbox.

• Sum of Two Digits
• Maximum Pairwise Product
• Fibonacci Number
• Last Digit of a Large Fibonacci Number
• Greatest Common Divisor
• Least Common Multiple
• Fibonacci Number Again
• Last Digit of the Sum of Fibonacci Numbers
• Last Digit of the Sum of Fibonacci Numbers Again
• Last Digit of the Sum of Squares of Fibonacci Numbers
• Money Change
• Maximum Value of the Loot
• Car Fueling
• Maximum Advertisement Revenue
• Collecting Signatures
• Maximum Number of Prizes
• Maximum Salary
• Binary Search
• Majority Element
• Improving Quick Sort
• Number of Inversions
• Organizing a Lottery
• Closest Points
• Money Change Again
• Primitive Calculator
• Edit Distance
• Longest Common Subsequence of Two Sequences
• Longest Common Subsequence of Three Sequences
• Maximum Amount of Gold
• Approach 1 (Brute Force)
• Approach 2 (Dynamic Programming)
• Maximum Value of an Arithmetic Expression

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