Deep learning is a form of machine learning that mimics how the human brain works. In deep learning, machines use hundreds or even thousands of layers of artificial neural networks to learn and adapt through experience. Driverless cars, voice-controlled consumer devices, and many other technologies rely on deep learning.
The best Coursera deep learning courses cover deep learning concepts and best practices. Course materials, including video lectures, readings, and graded programming assignments, are detailed, clear, and engaging. Finally, the best Coursera deep learning courses are taught by highly rated instructors who are also practitioners in their fields.
Best Coursera Deep Learning Courses of 2021
- Best Overall: Deep Learning Specialization
- Best for Beginners: AI for Everyone
- Best for Intermediate: Getting Started With TensorFlow 2
- Best for Advanced: Probabilistic Deep Learning With TensorFlow 2
- Best for Professional Certificate: DeepLearning.AI TensorFlow Developer Professional Certificate
- Best for Advancing Career: Introduction to Deep Learning
- Best for Specialization: Machine Learning Engineering for Production (MLOps) Specialization
- Best for Hands-On Learning: Detecting COVID-19 With Chest X-Ray Using PyTorch
Best Overall : Deep Learning Specialization
DeepLearning.AI's Deep Learning Specialization teaches students foundational deep learning concepts and how to apply them, earning it our top spot overall.
Combines theory and practice
Python skills required
Cannot purchase courses individually
The Deep Learning Specialization from DeepLearning.AI combines lectures and readings on deep learning with practical instruction on how to build and train neural network architectures. It perfectly balances theory and practice, making it the best Coursera deep learning program overall.
We suggest completing all five courses in this highly rated specialization. But If you are looking for a single class on deep learning, we recommend Neural Networks and Deep Learning. It's the first course in the specialization and it focuses on core concepts in deep learning. In 2019, it was one of the most popular classes on the Coursera platform. You can enroll for $49 a month, which gets you access to all courses in the Deep Learning Specialization. Unfortunately, you cannot purchase the classes individually.
The Deep Learning Specialization is taught by three of Coursera's top instructors: Andrew Ng, Younes Bensouda Mourri, and Kian Katanforoosh. Ng is the founder of DeepLearning.AI and an adjunct instructor at Stanford University. He's also one of the co-founders of Coursera and teaches some of the platform's most popular courses.
Best for Beginners : AI for Everyone
Instructor Andrew Ng's ability to explain complex technical subjects clearly and concisely makes AI for Everyone our top Coursera deep learning course for beginners.
Good for non-technical professionals
Proven career benefits
Limited course content
Limited technical training
AI for Everyone addresses deep learning within the context of artificial intelligence (AI). The course focuses on broad concepts, such as AI's impact on business and society. It's an accessible course for project managers, business analysts, and other non-technical professionals, making it the best Coursera deep learning course for beginner students who want to understand AI fundamentals.
AI for Everyone includes only six hours of content, leading some reviewers to complain that it is not worth the $49 price tag. However, other students praise instructor Andrew Ng's lucid lectures on AI and other complex topics. Also, 29% of graduates say they started a new career after completing the course, suggesting it can be a useful stepping stone toward a career in AI or machine learning.
AI for Everyone course materials include video lectures and quizzes. Over 600,000 students have enrolled in AI for Everyone, and in 2019 it was one of Coursera's most popular classes.
Best for Intermediate : Getting Started With TensorFlow 2
This course is highly rated and provides a comprehensive introduction to TensorFlow, a popular open-source software library used to create large-scale neural networks.
Highly rated instructor
Hands-on capstone project
Technical problems with capstone
Does not use the latest version of TensorFlow
Getting Started With TensorFlow 2 is the first of three courses in the TensorFlow 2 for Deep Learning Specialization offered by Imperial College London. The course is for students experienced in machine learning but just starting out with TensorFlow, a popular open-source framework used in deep learning. Understanding how to use TensorFlow is critical for machine learning professionals who want to advance in the field. This course provides a clear and comprehensive introduction to TensorFlow, making it the best option for intermediate students.
The 26-hour course is broken up into five weekly sessions. During the final week, students create their own image classifier deep learning models. Getting Started With TensorFlow 2 includes video lectures, practice quizzes, and graded programming assignments.
Former students say Getting Started With TensorFlow 2 is straightforward and comprehensive. However, some reviewers report running into technical problems while completing their capstone projects. Still, course reviews are largely positive, and the instructor, Dr. Kevin Webster, currently has a 4.92 out of 5-star rating.
Best for Advanced : Probabilistic Deep Learning With TensorFlow 2
Probabilistic Deep Learning With TensorFlow 2 is a challenging, statics-heavy course that takes on critical issues in deep learning, making it our top choice for advanced students.
Addresses important topics in deep learning
Significant time commitment
Heavy focus on statistics
Does not use the latest version of TensorFlow
Probabilistic Learning With TensorFlow 2 is the third course in Imperial College London's TensorFlow 2 for Deep Learning Specialization. It focuses on probabilistic approaches to deep learning used to quantify uncertainty and noise in datasets. Quantifying uncertainty is crucial for deep learning models used in self-driving cars, medical devices, and other high-stakes applications. The importance of the course's subject matter, taught by a leading researcher in the field, makes it our top choice for advanced students.
Probabilistic Learning With TensorFlow 2 focuses heavily on statistical analysis. The course follows from the first two courses in the specialization, Gettings Started With TensorFlow 2 and Customising Your Models With TensorFlow 2. Before enrolling, you should know Python 3 and understand the basics of machine learning and deep learning. You should also have experience with probability and statistics.
This 53-hour course takes five weeks to complete. In week five, students train a variational autoencoder algorithm on a dataset of images. Course materials include graded assignments with peer feedback, graded programming assignments, and hands-on tutorials guided by teaching assistants.
Best for Professional Certificate : DeepLearning.AI TensorFlow Developer Professional Certificate
This specialization prepares students for the TensorFlow Developer Certificate exam, one of the top deep learning credentials in the industry.
Practical training focus
Prepares students for certification exam
Less focus on deep learning concepts
Oversimplified course content
The DeepLearning.AI TensorFlow Developer Professional Certificate Specialization prepares students for the TensorFlow Developer Certificate exam. Over four courses, students learn TensorFlow best practices and how to use TensorFlow tools to build AI-powered algorithms. This specialization's effectiveness in preparing students to earn a highly respected certification is why we chose it as Coursera's best deep learning professional certificate program.
The first course in the specialization introduces students to TensorFlow for AI, while the final course covers sequences, time series, and prediction. Students complete 16 Python programming assignments throughout the program. During the last week, they train models to create original poetry.
Course materials include video lectures, readings, graded quizzes, and assignments. Several former students say the courses are well designed and provide plenty of practical instruction. However, a few reviewers complain that the course materials don't delve deep enough into the complexities of TensorFlow and deep learning.
Best for Advancing Career : Introduction to Deep Learning
Introduction to Deep Learning is challenging, but former students report significant career benefits from taking the course.
Career benefits for students
Knowledge of probability and linear algebra required
Mistakes in course materials
Despite the title, Introduction to Deep Learning isn't for beginners. It's an advanced course for students who know Python and have studied linear algebra and probability. The course is the first of seven classes in the Advanced Machine Learning Specialization from Higher School of Economics (HSE), a top research university in Russia. We chose Introduction to Deep Learning as the best option for students seeking career advancement because of its proven track record in accelerating students' careers.
Introduction to Deep Learning focuses on applying modern neural networks in computer vision and natural language processing. Over six weeks, HSE instructors cover optimization, deep learning for images, unsupervised representation learning, and deep learning for sequences. For their final projects, students implement deep neural networks to generate descriptions of images.
Some students have reported finding mistakes in course exercises. However, most reviewers rate Introduction to Deep Learning highly. Introduction to Deep Learning also boasts impressive learner outcomes. According to Coursera, 29% of students began a new career after taking the course, while 54% say completing the course helped them secure pay increases, promotions, and other benefits.
Best for Specialization : Machine Learning Engineering for Production (MLOps) Specialization
This course combines discussion of deep learning concepts with functional training in software development and engineering, helping students gain the knowledge and skills needed to succeed in AI.
Practical training focus
Top Coursera instructors
Prior experience deep learning knowledge required
DeepLearning.AI's Deep Learning Specialization provides the best deep learning instruction overall. However, the Machine Learning Engineering for Production (MLOps) Specialization, also from DeepLearning.AI, is an excellent option for advanced students who already have a solid grasp of deep learning concepts. Its emphasis on the practical application of deep learning concepts makes it Coursera's best specialized deep learning program.
The specialization program includes four courses: Introduction to Machine Learning in Production, Machine Learning Data Lifecycle in Production, Machine Learning Modeling Pipelines in Production, and Deploying Machine Learning Models in Production. The specialization's goal is to transform students' conceptual knowledge of machine learning and deep learning into a practical skill set. So, you should have a strong theoretical understanding of both before you enroll.
In the Machine Learning Engineering for Production (MLOps) Specialization, you'll learn how to conceptualize, develop, and maintain integrated systems that operate continuously in production. Course materials include online video lectures, readings, quizzes, and assignments.
Best for Hands-On Learning : Detecting COVID-19 With Chest X-Ray Using PyTorch
Detecting COVID-19 With Chest X-Ray Using PyTorch provides detailed, practical instructions and is highly rated by students. It also demonstrates the importance of deep learning in medicine.
Provides practical training
Can be completed in two hours
Little discussion of deep learning concepts
Prior deep learning knowledge required
Coursera's guided projects are short tutorials that take students step-by-step through practical exercises. In Detecting COVID-19 With Chest X-Ray Using PyTorch, students train a ResNet-18 model on a dataset of nearly 3,000 chest X-rays. The objective is to create a model that can accurately classify each X-ray within one of three categories: normal, viral pneumonia, or COVID-19. This guided project's popularity with students and clear, easy-to-follow instructions make it our top choice for hands-on learning.
Students who enroll in this guided project follow instructor Amit Yadav as he creates and trains the model. Some reviewers complain that Yadav doesn't provide enough explanation of deep learning concepts. However, guided projects focus on skills training, not theory, so students should have a strong grasp of convolutional neural networks before enrolling.
To enroll in Detecting COVID-19 With Chest X-Ray Using PyTorch, you can pay a flat fee of $9.99. You'll have two hours to complete the project but can begin it at any point within 180 days of purchase. Or, you can sign up for Coursera Plus; for $50 a month, Coursera Plus members get unlimited access to Coursera courses and guided projects.
Coursera offers some of the best deep learning courses on the web. AI for Everyone helps non-technical professionals understand basic concepts in AI, machine learning, and deep learning. Getting Started With TensorFlow 2 provides intermediate students with detailed instructions on using a popular open-source software library for machine learning. But overall, the Deep Learning Specialization from DeepLearning.AI offers the best education in deep learning on the Coursera platform.
The Deep Learning Specialization course gives students a comprehensive overview of deep learning concepts. However, it also offers practical instruction in building and training neural network architectures. By the end of the specialization, students should be able to use Python and TensorFlow to develop and train models used in speech recognition, music synthesis, chatbots, and more.
Compare the Best Coursera Deep Learning Courses
|Course||Company||Price||Time to Complete||Skill Level||Platform|
|Deep Learning Specialization
|DeepLearning.AI||$49 per month||140 hours over 5 months||Intermediate||Coursera: Includes web-based video lectures, readings, practice quizzes, graded assignments, and an applied learning project|
|AI For Everyone
Best for Beginners
|DeepLearning.AI||$49 for 180 days||6 hours over 4 weeks||Beginner||Coursera: Includes web-based video lectures and quizzes|
|Getting Started With TensorFlow 2
Best for Intermediate
|Imperial College London||$49 per month||26 hours over 5 weeks||Intermediate||Coursera: Includes web-based video lectures, quizzes, and graded assignments|
|Probabilistic Deep Learning With TensorFlow 2
Best for Advanced
|Imperial College London||$49 per month||53 hours over 4 weeks||Advanced||Coursera: Includes web-based video lectures, quizzes, and graded assignments|
|DeepLearning.AI TensorFlow Developer Professional Certificate
Best for Professional Certificate
|DeepLearning.AI||$49 per month||80 hours over 4 months||Intermediate||Coursera: Includes web-based video lectures, quizzes, graded assignments, and an applied learning project|
|Introduction to Deep Learning
Best for Advancing Career
|HSE University||$49 per month||34 hours over 6 weeks||Advanced||Coursera: Includes web-based video lectures, quizzes, and practice exercises|
|Machine Learning Engineering for Production (MLOps) Specialization
Best for Specialization
|DeepLearning.AI||$49 per month||48 hours over 3 months||Advanced||Coursera: Includes web-based video lectures, quizzes, and graded assignments|
|Detecting COVID-19 With Chest X-Ray Using PyTorch
Best for Hands-On Learning
|Coursera Project Network||$9.99 for 180 days||2 hours||Intermediate||Coursera: Split-screen featuring instructions and online workspace|
How to Choose the Best Coursera Deep Learning Courses
When choosing an online deep learning course, there are several factors to keep in mind:
- Time commitment: Some Coursera courses take weeks to complete, while other courses and guided projects take only a few hours. Before enrolling in a course or project, make sure you can devote the time required to get the most out of your investment.
- Objectives: Some deep learning courses on Coursera focus on broad, foundational concepts, while others emphasize hands-on skill-building. Reflect on your goals in taking a Coursera deep learning course and choose one that matches your objectives.
- Prerequisites: Some course descriptions recommend students have prior knowledge or experience before enrolling. Choose a Coursera course that's appropriate for your current knowledge and skill set.
- Reviews: Look at the reviews of the courses you are considering. They can offer insight into their strengths and weaknesses.
Coursera Deep Learning Courses vs. In-Person Instruction
Coursera courses aren't taught in person. Instead, instructors record video lectures and, in some cases, interact with students virtually.
Online learning platforms are transforming education. But some experts warn that individuals who struggle academically may find online courses challenging. This is partly because students in online classes sometimes receive less guidance from instructors than students who take courses in person.
However, online classes often provide greater scheduling flexibility than in-person classes, and Coursera courses are more affordable than many in-person college courses. Students who are self-motivated and good at working independently may excel in Coursera's online classes.
Frequently Asked Questions (FAQs)
Can I Complete Coursera Deep Learning Courses at My Own Pace?
All of the Coursera courses on our list have flexible deadlines or self-paced learning options. If you purchase a standalone course or guided project, you'll have access to it for 180 days. If you sign up for a specialization course, you'll pay a monthly fee and have access to the class as long as your subscription remains active.
Will I Advance My Career With Coursera Deep Learning Courses?
Completing certain Coursera courses can significantly boost your career skills and may help you advance in your profession. According to Coursera's 2020 Impact Report, 87% of learners say that taking classes on the platform improved their careers. Among students without bachelor's degrees, 88% reported career benefits from completing Coursera courses, as did 84% of unemployed students.
Are Coursera Deep Learning Courses Worth It?
Coursera courses are a flexible and affordable alternative to traditional, in-person college classes. Coursera partners with highly respected universities and industry leaders to offer courses on various subjects, including deep learning.
A certificate from Coursera is not equivalent to a university degree. However, a significant percentage of Coursera graduates say taking Coursera courses advanced their careers. Also, according to Coursera's 2020 Impact Report, 18% of students earned college credit for courses they completed on the platform.
We reviewed over a dozen Coursera deep learning courses to identify the best ones in the above categories. We considered course length, cost, quality and variety of learning materials, and instructors' reputations. We also looked at online reviews from current and former students. In general, we chose classes that cover fundamental deep learning concepts while also including practical instruction and hands-on exercises.