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.

- 1. Deep Learning Specialization- Coursera
- 2. Deep Learning- Udacity
- 3. Deep Learning in Python- Datacamp
- 4. Deep Learning A-Z™: Hands-On Artificial Neural Networks- Udemy
- 5. TensorFlow 2 for Deep Learning Specialization- Coursera
- 6. Professional Certificate in Deep Learning- edX
- 7. Introduction to Deep Learning in Python- Datacamp
- 8. Generative Adversarial Networks (GANs) Specialization- Coursera
- 9. Complete Guide to TensorFlow for Deep Learning with Python- Udemy
- 10. Deep Learning: Convolutional Neural Networks in Python- Udemy
- 11. Neural Networks and Deep Learning- Coursera
- 12. Intro to TensorFlow for Deep Learning- Udacity

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-**

- Rating of these Courses.
- Coverage of Topics.
- Engaging trainer and Interesting lectures.
- Number of Students Benefitted.
- 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

**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-**

**Neural Networks and Deep Learning****Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization****Structuring Machine Learning Projects****Convolutional Neural Networks****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-**

- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Generative Adversarial Networks
- 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

**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-**

**Getting started with TensorFlow 2****Customizing your models with TensorFlow 2****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-**

**Deep Learning Fundamentals with Keras****PyTorch Basics for Machine Learning****Deep Learning with Python and PyTorch****Deep Learning with Tensorflow****Using GPUs to Scale and Speed-up Deep Learning****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

**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-**

**Build Basic Generative Adversarial Networks (GANs)****Build Better Generative Adversarial Networks (GANs)****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 b
**asic 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!

**FAQ**

**You May Also Interested In**

**How Good is Udacity Deep Learning Nanodegree in 2024?****9 Best+ Free Online Courses for PyTorch for Deep Learning in 2024**** ****10 Best Books on Neural Networks and Deep Learning, You Should Read****Best Deep Learning Courses on Coursera You Need to Know in 2024****Deep Learning vs Neural Network, The Main Differences!What is Generative Adversarial Network? All You Need to Know**

**Top 5 Deep Learning Algorithms List, You Need to Know**

**What is Convolutional Neural Network? Super Easy Explanation!**

**Top 6 Skills Required for Deep Learning That Will Make You Expert!**

**Stochastic Gradient Descent- A Super Easy Complete Guide!**

**Gradient Descent Neural Network- Quick and Super Easy Explanation!**

**How does Neural Network Work? A step by step Guide.**

**Activation Function and Its Types-Which one is Better?**

**Artificial Neural Network: What is Neuron? Ultimate Guide.**

**What is Deep Learning and Why it is Popular?**

Thank YOU!

**Learn Deep Learning Basics** **here.**

Though of the Day…

– Henry Ford

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

### Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.