11 Best + Free Deep Learning Courses & Certification [2020][UPDATED]

  • 1. CS231n: Convolutional Neural Networks for Visual Recognition [Stanford University] - Best Free Course
  • 2. Practical Deep Learning for Coders, v3 [fast.ai] - Best Practical Course
  • 3. Deep Learning: A Crash Course [YouTube] - Best Crash Course
  • 4. Complete Guide to TensorFlow for Deep Learning with Python [Udemy] - Best Paid Course
  • 5. MIT Deep Learning for Self-Driving Cars [YouTube]
  • 6. Deep Learning by MIT Press [MIT Press] - Best Text Based Course
  • 7. Neural Networks and Deep Learning [Coursera] - Best Advanced Course
  • 8. Deep Learning with PyTorch [Manning Publications]
  • 9. Deep Learning Nanodegree [Udacity]
  • 10. Deep Learning and the Game of Go [Manning Publications]

As featured on Harvard EDU, Stackify and Inc - CourseDuck identifies and rates the Best Deep Learning Courses, Tutorials, Providers and Certifications, based on 12,000+ student reviews, public mentions, recommendations, ratings and polling 5,000+ highly active StackOverFlow members. Learn more

💻 Which Deep Learning Course Provider is best for me?
  • Udemy and Eduonix are best for practical, low cost and high quality Deep Learning courses.
  • Coursera, Udacity and EdX are the best providers for a Deep Learning certificate, as many come from top Ivy League Universities.
  • YouTube is best for free Deep Learning crash courses.
  • PluralSight, SkillShare and LinkedIn are the best monthly subscription platforms if you want to take multiple Deep Learning courses.
  • Independent Providers for Deep Learning courses & certificates are generally hit or miss.
💼 What is Deep Learning used for?
Practically, Deep Learning is a subset of Machine Learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.
💰 How much do Deep Learning developers make?
$20,500 - $38,499
9% of jobs
$38,500 - $56,499
4% of jobs
$56,500 - $74,499
3% of jobs
$74,500 - $92,499
3% of jobs
$102,500 is the 25th percentile. Salaries below this are outliers.
$110,500 - $128,499
7% of jobs
The average salary is $137,941 a year
$128,500 - $146,499
15% of jobs
$146,500 - $164,499
20% of jobs
$170,000 is the 75th percentile. Salaries above this are outliers.
$182,500 - $200,499
9% of jobs
$200,500 - $218,500
2% of jobs
US National Average$20,500 $218,500$137,941/year
📃 Is a Deep Learning Certificate worth it?
Yes and No. Certified Deep Learning developers on average make more money. Having a Deep Learning certificate greatly increases the chance of landing an interview and can open otherwise closed doors. Coursera, Udacity and EdX offer excellent certificate options for impressing your future employers. Eduonix, Udemy and several other providers offer certificates, but they aren't as reputable. If you have a Computer Science Degree, certificates are not as important. Still, many employers won't care about certificates, but rather your interview skills, experience and/or skills assessment.
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11 Filtered Courses
Best Free Course

1 )

CS231n: Convolutional Neural Networks for Visual Recognition (2017)

4.6
CS231n is a Stanford course on using neural networks to train visual recognition. It lasts 10 weeks and takes students through the process of designing and implementing a neural network that can identify visual classifications of objects.
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Pros
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Cons
    • This is an elite class taught by an elite university. Completing this course denotes high-level proficiency in neural network design and implementation.
    • Course is available for audit and sit-in.
    • This is an active college course and comes with the professor access that entails.
    • This is an advanced class and not readily accessible. Students need to know calculus and linear algebra in addition to multiple programming languages.
    • Course is just plain hard.
    • Course access is prioritized for the Stanford community.
Best Practical Course

2 )

Practical Deep Learning for Coders, v3 (2019)

4.3
Text-based and video-based introductory Machine Learning course taught by an experienced instructor and Kaggle's #1 competitor. Using PyTorch and fastai library, this tutorial is focused on practical results rather than theory.
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Pros
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Cons
    • Experienced instructor that provides easy to understand explanations and teaches you "how-to" instead of "why".
    • Top-down learning approach perfect for students that want to apply Machine Learning fast.
    • Great community of fellow-learners to help you along the course.
    • This course uses fastai library that can be too difficult for beginners.
    • To understand the theory befind the course, further readings and additional information are necessary.
Deep Learning: A Crash Course
provider
Best Crash Course

3 )

Deep Learning: A Crash Course (2018)

4.8
This YouTube tutorial covers the entire concept of deep learning in a single three-and-a-half-hour video. It goes over all of the fundamental concepts of deep learning and equips students to design their own machine learning models and apply them effectively.
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Pros
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Cons
    • Video has a sense of humor which is great for de-stressing a long session on deep topics.
    • Course does a great job of covering legitimate applications, helping students to understand where to take their knowledge for future development.
    • Despite the video length, the course is surprisingly succinct.
    • One video is not enough to thoroughly cover deep learning. Its a big topic.
    • Course is severely lacking in supplemental information.
    • Course is really just a recording of lectures, minus the resources used in the lectures.

5 )

MIT Deep Learning for Self-Driving Cars (2019)

4.1
Learn Deep Learning from a research scientist at MIT, one the world's most reputable universities. Great collection of courses and lectures, providing informative content and real-world examples.
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Pros
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Cons
    • The instructor is a researcher from one of the most prestigious universities in the world.
    • The concepts are presented in a clear and straight-forward manner.
    • Real-world examples to help you understand how to apply the theory behind Deep Learning.
    • Too many topics covered in one tutorial, only scratches the surface of each.
    • Lacks interactivity which can be inconvenient for learners to easily comprehend key concepts.
Deep Learning by MIT Press
provider
Best Text Based Course

6 )

Deep Learning by MIT Press (2016)

4.1
Written by superstars in the field, this free and detailed introduction to Machine Learning and Deep Learning is intended for experienced practitioners as well as students. The book covers deep learning background, its techniques and algorithms, and research perspective.
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Pros
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Cons
    • Covers the latest developments of Deep Learning.
    • Clear and sophisticated presentation.
    • Considered as "the Bible" of Machine Learning.
    • Written by one of the most respected AI researchers.
    • Perfect as a reference for further learning and research.
    • The book is written in a high-level academic manner. Will be difficult to understand for some.
    • Not recommended for students that prefer step-by-step tutorials.
Neural Networks and Deep Learning
provider
Best Advanced Course

7 )

Neural Networks and Deep Learning (2017)

4.8
Learn how to build and implement your own deep neural networks in just 7 hours. Taught by an experienced instructor, this is the first course in the Deep Learning Specialization.
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Pros
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Cons
    • Offered by deeplearning.ai, a well known provider of a world-class AI education.
    • Taught in Python and Jupyter Notebook.
    • Good introduction to how to build and implement neural networks.
    • Easy to understand lectures with a mix of theory and practical application.
    • Useful tips and insights into Deep Learning.
    • Pre-written code in assignments.
    • Repetitive content.
Deep Learning with PyTorch
provider

8 )

Deep Learning with PyTorch (2020)

4.8
Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you'll discover just how effective and fun PyTorch can be. After a quick introduction to the deep learning landscape, you'll explore the use of pre-trained networks and start sharpening your skills on working with tensors. You'll find out how to represent the most common types of data with tensors and how to build and train neural networks from scratch on practical examples, focusing on images and sequences.

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Overall Score : 98 / 100

Deep Learning Nanodegree
provider

9 )

Deep Learning Nanodegree (2017)

4.0
Deep learning is driving advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to challenges like image classification and generation, time-series prediction, and model deployment.

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Overall Score : 82 / 100

Deep Learning and the Game of Go
provider

10 )

Deep Learning and the Game of Go (2019)

4.2
Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game.

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Overall Score : 86 / 100

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