12 Best Deep Learning Courses on Coursera You Need to Know in 2025

Coursera has a wide range of Machine Learning and Deep Learning courses. That’s why I thought to list the 12 Best Deep Learning Courses on Coursera. So give your few minutes and find out Best Deep Learning Courses on Coursera for you.

Best Deep Learning Courses on Coursera

Before I discuss the deep learning courses, I would like to tell you Why Deep Learning is more powerful than machine learning?

Why Deep Learning?

The main three reasons for using Deep Learning are-

• Deep Learning gives excellent results on large datasets. But Machine Learning algorithms fail to process huge datasets. Machine Learning can work only on small datasets. This is the limitation of Machine Learning. But Deep Learning can easily perform operations on large datasets.
• In Machine Learning, you need to feed all features manually to train the model. But Deep Learning automatically extracts all the features. This makes Deep Learning much more powerful than Machine Learning. Because manual feeding is a time-consuming process, especially if you have a large dataset.
• Machine Learning can’t solve complex real-world problems. But Deep Learning Algorithms can easily solve real-world problems. That’s why many fields are using Deep Learning algorithms over Machine Learning.

I hope now you understood the importance of deep learning. Now without further ado, let’s start finding the Best Deep Learning Courses on Coursera-

1. Deep Learning Specialization

Provider- deeplearning.ai

Instructor- Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh

Rating- 4.8/5

Time to Complete- 4 months ( If you spend 5 hours per week)

This is one of the best deep learning specialization programs created by Andrew Ng the Co-founder of Coursera and an Adjunct Professor of Computer Science at Stanford University.

This is a Specialization Program that contains 5 courses. Python and TensorFlow are used in this specialization program for Neural networks. This is the best follow-up to Andrew Ng’s Machine Learning Course.

This Deep Learning Specialization is an advanced course series for those who want to learn Deep Learning and Neural networks. More than 250,000 learners from all over the globe have already enrolled in this Specialization Program.

Now, let’s see all the 5 courses of this Specialization Program-

Courses Include-

1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models

Extra Benefits-

• You will get a Shareable Certificate. 
• You will get a chance to work on case studies on healthcare, autonomous driving, sign language reading, music generation, and natural language processing.
• Along with that, you will get a chance to hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.

Who Should Enroll?

NOTE- This Specialization Program is not for Beginners. This program is suitable for-

• Those who have some basic understanding of Python.
• And those who have a basic knowledge of Linear Algebra and Machine Learning.

Interested to Enroll?

If yes, then check here- Deep Learning Specialization.

2. Generative Adversarial Networks (GANs) Specialization

Provider- deeplearning.ai

Instructor- Sharon Zhou, Eda Zhou, Eric Zelikman

Rating- 4.7/5

Time to Complete- 3 months ( If you spend 8 hours per week)

A generative Adversarial Network (GAN) is a powerful algorithm of Deep Learning. Generative Adversarial Network is used in Image Generation, Video Generation, and Audio Generation. In short, GAN is a Robot Artist, who can create any kind of art perfectly.

And in this Generative Adversarial Networks (GANs) Specialization, you will learn how to build basic GANs using PyTorch and advanced DCGANs using convolutional layers.

You will use GANs for data augmentation and privacy preservation, survey GANs applications, and examine and build Pix2Pix and CycleGAN for image translation.

There are 3 courses in this Specialization program where you will gain hands-on experience in GANs. Now, let’s see all the 3 courses of this Specialization Program-

Courses Include-

1. Build Basic Generative Adversarial Networks (GANs)
2. Build Better Generative Adversarial Networks (GANs)
3. Apply Generative Adversarial Networks (GANs)

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have a working knowledge of AI, deep learning, and convolutional neural networks. And have intermediate Python skills plus familiarity with any deep learning framework (TensorFlow, Keras, or PyTorch).
• You should also be proficient in basic calculus, linear algebra, and statistics.

Interested to Enroll?

If yes, then You can Sign Up here.

3. Neural Networks and Deep Learning

Provider- deeplearning.ai

Instructor- Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh

Rating- 4.9/5

Time to Complete- 20 hours

This course is the part of Deep Learning Specialization program. And I think this is the best course to begin your deep learning journey.

In this course, you will understand the major technology trends driving Deep Learning, key parameters in a neural network’s architecture, how to build, train, and apply fully connected deep neural networks, etc.

Extra Benefits-

• You will get a Shareable Certificate.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have a basic understanding of machine learning.

Interested to Enroll?

If yes, then You can Sign Up here.

4. TensorFlow 2 for Deep Learning Specialization

Provider- Imperial College London

Instructor- Dr. Kevin Webster

Rating- 4.9/5

Time to Complete- 4 Months( If you spend 7 hours per week)

In this specialization program, there are 3 courses where you will gain fundamental concepts to build, train, evaluate, and make predictions from deep learning models.

Along with this, you will learn TensorFlow to develop fully-customized deep learning models and workflows for any application. You will also learn TensorFlow APIs to include sequence models.

In the last course, you will learn how to build probabilistic models with TensorFlow and how to use the TensorFlow Probability library.

Now, let’s see all the 3 courses of this Specialization Program-

Courses Include-

1. Getting started with TensorFlow 2
2. Customizing your models with TensorFlow 2
3. Probabilistic Deep Learning with TensorFlow 2

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who are familiar with Python 3, machine learning concepts, Probability and statistics, and the basics of deep learning.

Interested to Enroll?

If yes, then You can Sign Up here.

5. DeepLearning.AI TensorFlow Developer Professional Certificate

Provider- deeplearning.ai

Instructor- Laurence Moroney (leads AI Advocacy at Google)

Rating- 4.7/5

Time to Complete- 4 months ( If you spend 5 hours per week)

Tensorflow is one of the most popular open-source Deep Learning libraries. It is designed to perform both numeric and neural network-oriented problems.

In this Certificate program, you will learn applied machine learning skills with TensorFlow to build and train powerful models.

There are 4 courses in this certificate program, where you will learn to build NLP systems using TensorFlow, handle real-world image data, and strategies to prevent overfitting, including augmentation and dropout. There are 16 Python programming assignments throughout the certificate program.

Now, let’s see all the 4 courses of this Specialization Program-

Courses Include-

1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
2. Convolutional Neural Networks in TensorFlow
3. Natural Language Processing in TensorFlow
4. Sequences, Time Series, and Prediction

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• This is an Intermediate level program, some previous knowledge in Machine learning and in Python is required.

Interested to Enroll?

If yes, then You can Sign Up here.

6. Advanced Machine Learning Specialization

Provider- National Research University Higher School of Economics

Rating- 4.5/5

Time to Complete- 10 months (If you spend 6 hours per week)

This Specialization series is an advanced series of courses. If you want to learn more than the basics of Machine Learning, then this is the best choice for you.

This specialization program fills out all the gaps in your knowledge of Machine Learning. As this is an advanced series of courses, that’s why you need to have more math knowledge. In short, this specialization program is for those who are already in the industry. This course will sharpen their skills.

Throughout this Specialization program, you will create several projects, that will help you to build a more powerful portfolio.

This Specialization Program contains 7 Courses. Let’s see all these courses-

Courses Include-

1. Introduction to Deep Learning
2. How to Win a Data Science Competition: Learn from Top Kagglers
3. Bayesian Methods for Machine Learning
4. Practical Reinforcement Learning
5. Deep Learning in Computer Vision
6. Natural Language Processing
7. Addressing Large Hadron Collider Challenges by Machine Learning

Extra Benefits-

• You will get a Shareable Certificate.
• You will get a chance to work on a wide variety of real-world problems like image captioning and automatic game playing.
• Along with that, you will get a chance to take advice from Top Kaggle machine learning practitioners and CERN scientists.

Who Should Enroll?

• Those who have Intermediate level knowledge in Machine Learning.
• Or the one who is already in the industry and wants to sharpen Machine Learning skills.

Interested to Enroll?

If yes, then You can Sign Up here.

7. Introduction to Deep Learning

Provider- National Research University Higher School of Economics

Rating- 4.6/5

Time to Complete- 34 hours

This course is the part of Advanced Machine Learning Specialization program. In this course, you will learn the fundamentals of modern neural networks and their applications in computer vision and natural language understanding.

This course will teach you all the important building blocks of neural networks including fully connected layers, convolutional and recurrent layers.

There is a project associated with this course, where you will implement a deep neural network for image captioning which solves the problem of giving a text description for an input image.

Extra Benefits-

• You will get a Shareable Certificate.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have basic knowledge of Python, linear algebra, and probability.

Interested to Enroll?

If yes, then You can Sign Up here.

8. Convolutional Neural Networks

Rating- 4.9/5

Provider- deeplearning.ai

Time to Complete- 20 hours

This course will teach you the key features and concepts that are required to build CNN(convolutional neural network). In this course, you will learn how to apply convolutional networks to visual detection and recognition tasks.

This course is combined with small assessments or questions which makes it easier to follow along. In a nutshell, this course is best to start out in the world of CNN.

Now, let’s see the syllabus of the course-

Syllabus of the Course-

• Foundations of Convolutional Neural Networks
• Deep convolutional models: case studies
• Object detection
• Special applications: Face recognition & Neural style transfer

Extra Benefits-

• You will get Course Certificates upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have some prior knowledge in Python and Machine Learning.

Interested to Enroll?

If yes, then check out all details here- Convolutional Neural Networks

9. Self-Driving Cars Specialization

Rating- 4.7/5

Provider- University of Toronto

Time to Complete- 7 months (If you spend 5 hours/week)

This is a specialization program and gives you a deep understanding of state-of-the-art engineering practices used in the self-driving car industry. In this program, you will interact with real data sets from an autonomous vehicle.

You will do hands-on projects using the open-source simulator CARLA. After successfully completing this specialization program, you will be able to build your own self-driving software stack. This specialization program has 4 courses.

Let’s see the details of the courses-

Courses Details-

1. Introduction to Self-Driving Cars
2. State Estimation and Localization for Self-Driving Cars
3. Visual Perception for Self-Driving Cars
4. Motion Planning for Self-Driving Cars

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with this, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have prior knowledge in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming.

Interested to Enroll?

If yes, then check out all details here- Self-Driving Cars Specialization

10. Introduction to Computer Vision with Watson and OpenCV

Rating- 4.4/5

Provider- IBM

Time to Complete- 15 hours

This is a beginner-friendly course and teaches you the fundamental concepts of computer vision. In this course, you will get to know about various applications of computer vision across many industries.

The best part about this course is that it has several labs and exercises. And you will utilize Python, Watson AI, and OpenCV to process images and interact with image classification models.

In this course, you will also build, train, and test your own custom image classifiers. You will perform all practicals on the Cloud. And you will be provided access to a Cloud environment completely free of charge.

After completing this course, you will build your own computer vision web app and deploy it to the Cloud.

Now, let’s see the syllabus of the course-

Syllabus of the Course-

• Introduction to Computer Vision
• Image Classification with IBM Watson
• Custom Classifiers with Watson Visual Recognition
• Image Processing using IBM Watson and Python
• Image Processing using OpenCV and Python
• Project: Building a Web-Based Computer Vision App using IBM Cloud

Extra Benefits-

• You will get a Course Certificate upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have some prior knowledge of Python.

Interested to Enroll?

If yes, then check out all details here- Introduction to Computer Vision with Watson and OpenCV

11. AI in Healthcare Specialization

Rating- 4.8/5

Time to Complete- 9 Months (If you spend 2 hours/week)

Provider- Stanford University

Level- Beginner Level

This is another specialization program offered by Coursera. This specialization program is for both computer science professionals and healthcare professionals.

In this specialization program, you will learn how to identify the healthcare professional’s problems that can be solved by machine learning.

You will also learn the fundamentals of the U.S. healthcare system, the framework for successful and ethical medical data mining, the fundamentals of machine learning as it applies to medicine and healthcare, and much more.

This specialization program has 5 courses. Let’s see the details of the courses-

Courses List-

1. Introduction to Healthcare
2. Introduction to Clinical Data
3. Fundamentals of Machine Learning for Healthcare
4. Evaluations of AI Applications in Healthcare
5. AI in Healthcare Capstone

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with this, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.

Who Should Enroll?

• Those who have no prior experience. Anyone interested in AI for healthcare can enroll.

Interested to Enroll?

If yes, then check here- AI in Healthcare Specialization

12. Introduction to Computer Vision and Image Processing

Rating- 4.4/5

Provider – IBM

Time to Complete- 21 hours.

This is another Free to Audit Coursera course. To access the course material for Free, press-> Enroll for Free and then press-> Audit the Course.

In this course, you will learn the basics of image processing with Python libraries OpenCV and Pillow and the different Machine learning classification Methods commonly used for Computer vision, including k nearest neighbors, Logistic regression, and SoftMax Regression, and Support Vector Machines.

Then you will learn about Neural Networks, fully connected Neural Networks, and Convolutional Neural networks (CNN). After that, you will understand object detection with different methods. The first approach is using the Haar Cascade classifier, the second one is to use R-CNN and MobileNet.

At the end of this course, you will build a computer vision app that you will deploy on the cloud through Code Engine. For the project, you will create a custom classifier, train it and test it on your images.

You Should Enroll If-

• You have some knowledge of the Python programming language and high school math.

Interested to Enroll?

If yes, then check out the course details here- Introduction to Computer Vision and Image Processing

And here the list ends. I hope these Best Deep Learning Courses on Coursera will definitely help you to learn Deep Learning at your own pace. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.

Summary of Best Deep Learning Courses on Coursera

Course Name	Rating	Time to Complete
1. Deep Learning Specialization	4.8/5	4 months ( If you spend 5 hours per week)
2. Generative Adversarial Networks (GANs) Specialization	4.7/5	3 months ( If you spend 8 hours per week)
3. Neural Networks and Deep Learning	4.9/5	20 hours
4. TensorFlow 2 for Deep Learning Specialization	4.9/5	4 Months( If you spend 7 hours per week)
5. DeepLearning.AI TensorFlow Developer Professional Certificate	4.7/5	4 months ( If you spend 5 hours per week)
6. Advanced Machine Learning Specialization	4.5/5	10 months (If you spend 6 hours per week)
7. Introduction to Deep Learning	4.6/5	34 hours
8. Convolutional Neural Networks	4.9/5	20 hours
9. Self-Driving Cars Specialization	4.7/5	7 months (If you spend 5 hours/week)
10. Introduction to Computer Vision with Watson and OpenCV	4.4/5	15 hours
11. AI in Healthcare Specialization	4.8/5	9 Months (If you spend 2 hours/week)
12. Introduction to Computer Vision and Image Processing	4.4/5	21 hours

Conclusion

In this article, I tried to cover the 12 Best Deep Learning Courses on Coursera. If you have any doubts or questions regarding “Best Deep Learning Courses on Coursera“, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

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