12 Best Courses to Learn Deep Learning You Need to Know- 2025

Are you looking for the Best Courses to Learn Deep Learning? If yes, then you are in the right place. In this article, you will find the 12 Best Courses to Learn Deep Learning. So give few minutes and find out the best deep learning course for you.

Deep learning is more powerful than machine learning due to its ability to process large numbers of features when dealing with unstructured data. And Deep Learning gives excellent results on large datasets. Deep learning knowledge is essential in the data science field too.

That’s why I thought to share some Best Courses to Learn Deep Learning with you. And I have filtered these courses on the following criteria-

Criteria-

1. Rating of these Courses.
2. Coverage of Topics.
3. Engaging trainer and Interesting lectures.
4. Number of Students Benefitted.
5. Good Reviews from various aggregators and forums.

Now without any further ado, let’s dive into the deep learning courses-

Best Courses to Learn Deep Learning

1. Deep Learning Specialization– Coursera

Provider- deeplearning.ai

Instructor- Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh

Rating- 4.8/5

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

This course is taught 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.

This Deep Learning Specialization is one of the best advanced deep learning course series especially for those who want to learn Deep Learning and Neural Network.

In this specialization program, you will learn Python and TensorFlow for Neural networks. And this is the best follow-up to Andrew Ng’s Machine Learning Course. 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 from 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. Deep Learning– Udacity

Time to Complete- 4 months (If you spend 12 hours per week)

Rating- 4.7/5

This Nano-Degree program from Udacity will give you a complete understanding of Deep Learning. In this program, you will build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation.

You will also learn how to implement gradient descent and backpropagation using NumPy matrix multiplication, how to prevent overfitting of training data and minimize the error of a network, how to define and train neural networks for sentiment analysis, etc.

This Nanodegree program will also teach you how to use Amazon’s GPUs to train neural networks faster. The instructor Sebastian Thrun will explain about detecting skin cancer with CNN. There are 5 courses in this Nanodegree program. Now let’s see the details of the courses-

Courses Include-

1. Neural Networks
2. Convolutional Neural Networks
3. Recurrent Neural Networks
4. Generative Adversarial Networks
5. Updating a Model

Extra Benefits-

• You will get a chance to work on Real-world projects.
• You will get Technical mentor support.
• Along with that, you will get Resume services, Github review, LinkedIn profile review.

Who Should Enroll?

• Those who have intermediate-level Python programming knowledge and experience with NumPy and pandas.
• And those who have math knowledge, including- algebra and some calculus.

Interested to Enroll?

If yes, then check it out here– Deep Learning (Udacity)

3. Deep Learning in Python– Datacamp

Time to Complete- 20 hours

Type- Skill Track

This is a skill track offered by Datacamp. In this skill track, there are 5 courses. In these 5 courses, you will learn the fundamentals of neural networks, how to use deep learning with Keras 2.0, TensorFlow 2.4, and PyTorch.

You will also learn about convolutional neural networks and how to use them to build much more powerful models which give more accurate results. Throughout these courses, you will learn how to accurately predict housing prices, credit card borrower defaults, and images of sign language gestures.

In the last course, you will learn some advanced topics such as category embeddings and multiple-output networks. 

Who Should Enroll?

• Those who have previous knowledge in Machine Learning and Python Programming.

Interested to Enroll?

If yes, then check it out here– Deep Learning in Python

4. Deep Learning A-Z™: Hands-On Artificial Neural Networks– Udemy

Rating- 4.5/5

Time to Complete- 22.5 hours

This is another best course to learn deep learning. In this course, you will learn Artificial neural networks, convolutional neural networks, recurrent neural networks, self-organizing maps, Boltzmann machines, AutoEncoders, and the basics of regression and classification.

Throughout this course, you will work on Real-World datasets, to solve 6 Real-World business problems- Customer Churn problem, Image Recognition, Stock Price Prediction, Fraud Detection, and Recommender Systems. The instructor of this course, Kirill is an amazing instructor and explains each topic very clearly.

Extra Benefits-

• You will get a Certificate of Completion.
• You will also get 37 articles and 5 downloadable resources.
• Along with that, you will get lifetime access to the course material.

Who Should Enroll?

• Those who have basic Python programming knowledge and High school mathematics.

Interested to Enroll?

If yes, then check it out here– Deep Learning A-Z™: Hands-On Artificial Neural Networks

5. TensorFlow 2 for Deep Learning Specialization– Coursera

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, Graded Programming Assignments.

Who Should Enroll?

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

Interested to Enroll?

If yes, then check it out here– TensorFlow 2 for Deep Learning Specialization

6. Professional Certificate in Deep Learning– edX

Provider- IBM

Time to Complete- 8 months(If you spend 2 – 4 hours per week)

This is a professional certificate program for Deep Learning offered by IBM. In this program, you will learn the fundamentals of deep learning and learn how to build, train, and deploy different types of Deep learning algorithms such as Convolutional Networks, Recurrent Networks, and Autoencoders.

You will use Python libraries like Keras, PyTorch, and Tensorflow and work on hands-on labs, assignments, and projects. There are 6 courses in this professional certificate. Now, let’s see all the 6 courses of this Specialization Program-

Courses Include-

1. Deep Learning Fundamentals with Keras
2. PyTorch Basics for Machine Learning
3. Deep Learning with Python and PyTorch
4. Deep Learning with Tensorflow
5. Using GPUs to Scale and Speed-up Deep Learning
6. Applied Deep Learning Capstone Project

Extra Benefits-

• You will get a Shareable Certificate.

Who Should Enroll?

• Those who have basic understanding of Python and machine learning.

Interested to Enroll?

If yes, then check here- Professional Certificate in Deep Learning

7. Introduction to Deep Learning in Python– Datacamp

Time to Complete- 4 hours

This course is part of the Deep Learning in Python Career Track. In this course, you will learn the fundamental concepts and terminology used in deep learning and how to optimize a neural network with backward propagation.

You will also learn how to use the Keras library to build deep learning models for both regression and classification. Then you will learn how to fine-tune the keras models.

Who Should Enroll?

• Those who have understanding of supervised learning and Python Programming.

Interested to Enroll?

If yes, then check it out here– Introduction to Deep Learning in Python

8. Generative Adversarial Networks (GANs) Specialization– Coursera

Provider- deeplearning.ai

Instructor- Sharon Zhou, Eda Zhou, Eric Zelikman

Rating- 4.7/5

Time to Complete- 3 months ( If you spend 8 hour 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, 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 proficient in basic calculus, linear algebra, and statistics.

Interested to Enroll?

If yes, then check it out here– Generative Adversarial Networks (GANs) Specialization

9. Complete Guide to TensorFlow for Deep Learning with Python– Udemy

Rating- 4.5/5

Time to Complete- 14 hours

In this course, you will learn how to use Google’s TensorFlow framework to create artificial neural networks for deep learning. This course will also provide complete Jupyter notebook guides of code.

Throughout this course, you will learn neural network and Tensorflow basics, convolutional neural network, recurrent neural network, reinforcement Learning, AutoEncoders, etc. But the drawback of this course is that they use Tensorflow 1.

Extra Benefits-

• You will get a Certificate of Completion.
• You will also get 7 articles and 5 downloadable resources.
• Along with that, you will get lifetime access to the course material.

Who Should Enroll?

• Those who have some knowledge of Python programming and basic knowledge of math (mean, standard deviation, etc).

Interested to Enroll?

If yes, then check it out here– Complete Guide to TensorFlow for Deep Learning with Python

10. Deep Learning: Convolutional Neural Networks in Python– Udemy

Rating- 4.7/5

Time to Complete- 12 hours

This course is focused on Convolutional Neural Network (CNN), a powerful algorithm of Deep Learning. In this course, you will learn the fundamentals of CNN and how to build a CNN using Tensorflow 2. This course will also teach how to do image classification in Tensorflow 2 and how to use Embeddings in Tensorflow 2 for NLP.

Throughout this course, you will work in Numpy, Matplotlib, and Tensorflow libraries. This course is based on a practical approach.

Extra Benefits-

• You will get a Certificate of Completion.
• Along with that, you will get lifetime access to the course material.

Who Should Enroll?

• Those who have understanding of basic math (taking derivatives, matrix arithmetic, probability).

Interested to Enroll?

If yes, then check it out here– Deep Learning: Convolutional Neural Networks in Python

11. Neural Networks and Deep Learning– Coursera

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, Graded Programming Assignments.

Who Should Enroll?

• Those who have basic understanding of machine learning.

Interested to Enroll?

If yes, then check it out here– Neural Networks and Deep Learning

12. Intro to TensorFlow for Deep Learning– Udacity

Time to Complete-2 Months

This is an intermediate-level free deep learning course on Udacity. This course will teach you how to build deep learning applications with TensorFlow. In this course, you will get a chance to work on projects and you will build your own state-of-the-art image classifiers and other deep learning models.

You will also learn advanced techniques and algorithms of deep learning. But You should have previous knowledge of linear algebra and Python programming.

Interested to Enroll?

If yes, then check it out here– Intro to TensorFlow for Deep Learning

And here the list end. I hope these 12 Best Courses to Learn Deep Learning will definitely help you. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.

Conclusion

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

All the Best!

Enjoy Learning!

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Though of the Day…

‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

– Henry Ford