We’ve listed the 15 best online course for Deep Learning that would help you to change or upgrade your career. This list includes a Deep Learning online course for every level of learners.

If you’re on a mission to succeed in the AI career, then understanding Deep Learning to the core is what you need. Deep Learning is one of the most highly sought technical skills, and this list will help you.

Now let’s look at the list of deep learning courses.

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## 15 Best Online Course for Deep Learning

### 1. Deep Learning Essentials – edX

Learn all the deep learning essential by signing up for this course in the edX site. It is developed by IVADO, Mila and Université de Montréal and offers various tools to help you grasp the Deep Learning subject.

You gain a good understanding of Deep Learning fundamentals of what kind of problem it solves. Deep Learning is a part of Machine Learning where machines learn without any intervention by human beings.

This course starts from the ground level and teaches you all the essentials of Deep Learning. The prerequisite of this course is that you should have the least knowledge of Python, basics of linear algebra, and statistics. Hence, this deep learning course is intended for beginners.

**Key takeaways of this course:**

- Understand all the terminologies used in Deep Learning.
- Learn the different types of Machine Learning tools.
- Identify which neural network you can use to solve a particular problem.
- Hands-on labs will expose you to learn different Deep Learning Libraries.
- Learn about the Convolutional Neural Networks (CNN) architectures.

The length of this course is about five weeks. By dedicating four to six hours a week, you can complete this course in 5 weeks. And finally, earn a Deep Learning Essentials certificate from edX.

### 2. An Introduction to Practical Deep Learning – Coursera

Introduction to Practical Deep Learning is a course by Intel. This course is a basic course aimed at students who have no idea what deep learning is.

If you’re someone who has zero knowledge about Deep Learning, then An Introduction to Practical Deep Learning by Coursera is the best online course.

You’ll learn all the essentials of Deep Learning, which is a part of AI. Also, you will get insights on some of the core concepts in Deep Learning and many topics that come under it.

**Key takeaways of this course:**

- An introductory class to Deep Learning basics.
- Learn what CNN is in DL.
- Get training tips on Deep Learning models.
- Learn Intel’s roadmap in Deep Learning.

You can complete this course in a week by dedicated two hours a day. Since this is an introductory course, it is practically aimed for beginners.

### 3. IBM Deep Learning Fundamentals with Keras – edX

This course is developed by IBM and offered by edX. Deep Learning Fundamentals with Keras course not only provide basic introductory to Deep Learning but offers something unique. That is, you will be able to build the first Deep Learning model using Keras library.

Keras is a Python library, which means you should have prerequisite knowledge of Python language. Along with Python, you should have some basic understanding of Machine learning with Python, and Partial Derivatives. If you’re not familiar with Partial Derivatives, you can learn for free in Khan Academy.

This course also includes the basics of neural networks and teaches the different Deep Learning models.

**Key takeaways of this course:**

- Understand what neural networks are from the basics.
- Learn to leverage Deep Learning skills.
- Learn what a vanishing gradient problem is.
- You will be able to build a regression and classification model using the Keras library.
- Also, you will learn the different types of supervised deep learning models, which are recurrent neural networks and convolutional neural networks.
- Using Keras Python library, you will be able to build the convolutional neural network.
- Also, you learn about unsupervised learning in Deep Learning, such as autoencoders.

By dedicated two to four hours of your time in a week, you can complete this course in about six weeks. Not a long time to shoot for the stars.

### 4. Deep Learning with Python and PyTorch – edX

This course is developed by IBM and available to enroll in the edX platform. With this course, you’ll learn to use Python and its popular libraries, such as Pandas, NumPy, and PyTorch.

IBM Deep Learning with Python and PyTorch course will teach you all the essentials of Deep Learnings and build models using Pytorch. Given that you have some basic knowledge of Machine Learning and Deep Learning, the only prerequisite is that you know Python Programming Language.

It is clear from the course title itself that you should know Python to proceed with this course. This shouldn’t be an issue as most of the popular courses such as ML, AI, and DL need Python knowledge.

The course will kickoff by teaching PyTorch’s tensors and its Automatic Differentiation package. Then with each section, you learn more about different Deep Learning models. Finally, you end the course by learning pre-trained models and Convolutional Neural Networks.

**Key takeaways of this course:**

- Learn and apply Deep Neural Networks knowledge and ML models.
- Able to apply different Python libraries such as Pandas, NumPy, and PyTorch for Deep Learning applications.
- Learn linear regression, logistic regression, and softmax regression.
- Understand the optimization methods in a deep network.
- Using PyTorch, you’ll be able to build Deep Neural Networks.

You can complete this course in six weeks by dedicating 3-5 hours per week.

### 5. Deep Learning with Tensorflow – edX

Most of the data in the world are in a state of unstructured data. By taking this course, you can solve real-world problems by applying Deep Learning with TensorFlow. This course is designed by IBM.

The best library that can be used to implement Deep Learning is TensorFlow. It was created by Google and is widely used to provide solutions for Deep Learning. The prerequisite for this course is Python, Jupyter notebook, ML, and Deep Learning concepts.

This course will teach you the essential concepts of TensorFlow, its operations, main functions, and execution. You’ll start with a simple example and see how the TensorFlow is used in most of the Deep Learning concepts.

**Key takeaways of this course:**

- Learn the basics of TensorFlow concepts.
- Understand the main functions, operations, and execution pipelines.
- You’ll learn how TensorFlow is used in regression, classification, curve fitting, and minimization of error functions.
- Learn the Architectures of different Deep Learning models.
- Understand the methods to apply TensorFlow for backpropagation while training neural networks.

You can complete this course in about five weeks, given that you are putting a daily effort to learn Deep Learning. By learning for about 3-4 hours in a week, you will be able to complete this course in the above timeline.

### 6. IBM Deep Learning Professional Certificate – edX

IBM Deep Learning professional course is a big course and consists of 5 courses. The 5th one is the final capstone project. This course is an extensive Deep Learning course by IBM that provides a professional certificate after completing the final capstone project. Also, check out the best Deep Learning projects for students and beginners.

You’ll start by learning the basic concepts of Deep Learning, including supervised and unsupervised learning. Going further into the course, you’ll learn to build Deep Learning models using popular libraries such as Pytorch, Keras, and TensorFlow.

As you learn to build Deep Learning models using different libraries, you become an expert in no time. In this entire course, you will practice many times with their extensive video training, demos, assignment, and hands-on lands.

If you’re serious about a Deep Learning career, then this is the best online course for Deep Learning.

**Key takeaways of this course:**

- Start learning from the basics of Deep Learning.
- Learn different neural networks for supervised and unsupervised learning.
- Use all three popular Deep Learning libraries – PyTorch, Keras, and TensorFlow.
- You will be able to build, train, and deploy different types of Deep Learning models.
- Apply Deep Learning to solve real-world problems.
- Master Deep Learning by using GPU and accelerated hardware.
- Build the final Capstone project using any of the three libraries (PyTorch, Keras, and TensorFlow) that you learn in this course.

Since this is a great course in Deep Learning, it would take about 7 – 9 months to complete this IBM Deep Learning course. However, it is recommended that you dedicate at least 2-4 hours per week in learning it. Otherwise, there is no point.

### 7. Deep Learning Specialization – Coursera

Deep Learning Specialization course is developed by deeplearning.ai and offered by Coursera. It consists of five courses that will take you from fundamentals to building Deep Learning models.

To get the most out of this course, it is recommended that the learners should have the necessary Python programming experience, math topic on basic linear algebra, and basic knowledge of machine learning. These are the suggested prerequisite to succeed in Deep Learning.

By enrolling in this course, you not only learn theory but also get a hands-on lab. This hands-on lab will get to the speed by learning it practically, which makes sense, considering it an advanced course. In this course, you will be using both Python and TensorFlow in Deep Learning.

**Key takeaways of this course:**

- You will be able to build, train, and apply a fully connected, deep neural network.
- Learn how to apply the vectorized neural network.
- Learn the best practice of developing Deep Learning models.
- Be able to understand complicated ML settings.
- Understand how to build CNN and apply it to image data.
- Learn to apply algorithms to various data such as video, image, 2D, or 3D data.
- For natural language problems, you’ll be able to apply sequence models.

You can complete this course in about two months by dedicating at least 2 hours per day.

### 8. Introduction to Deep Learning & Neural Networks with Keras – Coursera

What is Deep Learning? How does it compare with artificial neural networks? What is Keras in Deep Learning? You will find all the answers to these questions in this course.

Introduction to Deep Learning & Neural Networks with Keras course is one of the best courses to learn Deep Learning using Keras library. The instructor is this course is Alex Aklson, who has a Ph.D. in Data Science. This course is a 5-week course that comes with a project at the end.

This course is developed by IBM and is available to enroll in Coursera. It covers most of the basic topics in Deep Learning using Keras. Hence, if you’re interested in learning the Deep Learning model using Keras, then this is the right course.

**Key takeaways of this course:**

- Relate what a neural network is to understand the different models of Deep Learning.
- Understand the critical difference between different models.
- Show your understanding of unsupervised learning models – autoencoders.
- Show your understanding of supervised learning in DL models – CNN and RNN.
- Finally, you should be able to build the DL model using Keras library.

The total duration of this course is about 9 hours, and hence by dedicated 1-2 hours a day, you should be able to complete this course in a week.

### 9. Deep Learning in Computer Vision – Coursera

Deep Learning in Computer Vision is one of a kind course. With the advancement of Deep Learning, it inspired the newly developing field in computer vision.

The purpose of this course is to bring students to computer vision technology. This course kicks off from basics and then progresses to the modern Deep Learning model.

This course covers various topics in the field of computer vision. The instructor of this course is Anton Konushin and Alexey Artemov. Both are faculty of computer science. Please note that this is an advanced course in Deep Learning developed by the National Research University Higher School of Economics.

**Key takeaways of this course:**

- Learn the purpose of computer vision.
- Using a deep regression CNN, you will build your key-points detector.
- You’ll practice on training a face detection model.
- Understand how to design a computer vision architectures.

As we already mentioned that this is an advanced course, it would take about 24 hours to complete this course. By dedicating 2-5 hours a week, you can complete this course in 5-6 weeks.

### 10. TensorFlow for Deep Learning with Python – Udemy

Complete Guide to TensorFlow for Deep Learning with Python course is developed by Jose Portilla, available in Udemy. All you need is some knowledge on Python and basics of math topic on standard deviation.

This course uses Google’s TensorFlow framework and creates artificial neural networks. The instructor provides a clear guide so that you’ll be able to understand the topics easily. It shows all the latest techniques of TensorFlow available for Deep Learning.

This course offers a perfect balance of theory and practical course with Jupyter notebook guides of code. It also provides many problems to be solved by you as you progress the course.

**Key takeaways of this course:**

- Understand the Neural Network works.
- Using Python, you’ll be able to build your own Neural Network from scratch.
- Use TensorFlow for Regression Tasks, Time Series Analysis, and Classification.
- Solve problems of unsupervised learning with Autoencoders.
- Become a Deep Learning Guru with this course.
- Perform Image Classification by using TensorFlow.
- You can create Generative Adversarial Networks with TensorFlow.

This course consists of 14 hours of on-demand video. You can complete this course in your own time by dedicating the available hours in your day.

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

With the growing demand for Artificial Intelligence, the demand for Deep Learning is also growing. Kirill Eremenko has developed this course with the prerequisite of knowing Python and high school math.

By taking this course is offer you can solve real-world problems by learning to work on real-world datasets. It offers a total of six real-world challenges. The instructor provides a clear explanation of saying what he is doing and why he does this.

A step by step videos will teach you to change your code by applying your dataset. This will make you confident in the learnings of Deep Learning.

**Key takeaways of this course:**

- Understand behind the scenes of Artificial Neural Networks.
- Understand the intuition behind CNN, RNN, self-organizing maps, and autoencoders.
- Practice how to apply Artificial Neural Networks.
- You will learn how to apply Self-Organizing Maps by practicing it.
- Learn to apply CNN and RNN by live practice with this course.

This course consists of 22 hours of on-demand video, which follows self-paced learning. You can complete this course in 30-45 days.

### 12. Natural Language Processing with Deep Learning in Python -Udemy

In this course, you will look at the Natural Language Processing with Deep Learning. If you are on the career path to becoming a data scientist or machine learning, then this course is the best online course for Deep Learning.

This course will take you on the journey of how word2vec works. Both from theory and practical point of view. And you will learn about the GloVe method, which is used to find the word vectors using a technique known as matrix factorization.

This course has some severe requirements for prerequisites. It also mentions that you should no sign up for this course if you’re not aware of the prerequisite. Check out the requirements on the course page.

**Key takeaways of this course:**

- You will learn to implement word2vec.
- Understand what the skip-gram method does in word2vec.
- Learn to implement recursive neural tensor networks for sentiment analysis.
- Understand the negative sampling and CBOW method in word2vec.
- Learning to implement GloVe using gradient descent

The course can be completed in 13 hours. Since this is a short duration course, you will be able to complete it in 3 weeks.

### 13. Data Science: Deep Learning in Python – Udemy

You can enroll in this course if you want to become a master in Deep Learning. You can also take this course if you’re interested in machine learning and data science.

Using Deep Learning, this course will let build your very first artificial neural network. This course includes various projects where you will be able to predict visitors’ actions on the website, number of viewed products, the time they stayed on your site, etc.

In the last project, you’ll learn to implement facial expression recognition using Deep Learning.

**Key takeaways of this course:**

- Learn to code neural network from scratch using NumPy, Python library.
- Understand solving different types of problems using different types of neural networks.
- Build a neural network using TensorFlow.
- Get the knowledge on the backpropagation rule.

This course consists of around 10 hours of video training, which can be completed in 2-4 weeks. Once you complete this course, you should sign up for their next course of Modern Deep Learning in Python.

### 14. Modern Deep Learning in Python – Udemy

This course is created by Lazy Programmer Inc and consists of over 10 hours of video training. It is a continuation of Data Science: Deep Learning in Python.

This will take off where the earlier course as left off. This course teaches all the TensorFlow variables and expressions to build a neural network. Modern Deep Learning in Python teaches you to write a neural network using CNTK, Keras, PyTorch, and MXNet.

The prerequisite of this course is that you should be comfortable with Python language and some of its libraries – Matplotlib and NumPy. You should take the earlier Deep Learning course if you’re not familiar with backprop, gradient descent, and softmax.

**Key takeaways of this course:**

- Learn the basic building blocks of Theano.
- Understand the basic building blocks of TensorFlow.
- Write a neural network using CNTK, Keras, PyTorch, and MXNet.
- Build a neural network in Theano and TensorFlow.
- Learn the key difference between different types of gradient descent.

You can complete this course in 2-3 weeks as it has about 10 hours of video training.

### 15. Deep Learning: Recurrent Neural Networks in Python – Udemy

In this Deep Learning online course, you’ll be introduced to the Simple Recurrent Unit, also known as the Elman unit. You should take this course if you want to apply time series or sequence data and want to learn Deep Learning.

Some to the basic requirement of taking this course is basic math, Python, a basic understanding of backpropagation.

**Key takeaways of this course:**

- Learn the recurrent unit in Deep Learning.
- You’ll be able to solve XOR and parity problems using a recurrent neural network.
- Understand gated recurrent unit (GRU).
- Learn to mitigate the vanishing gradient problem.
- Learn and understand the long short-term memory unit.

This course consists of around 8 hours of training videos, which can be completed in one week.

## Summary

These are our 15 best online course for Deep Learning in 2020. Since there is so much of choice, we’ve broken down each of the course overviews you choose the best one.

If you’re interested in cloud computing check out our 11+ best online course for Google Cloud Certification.

If you’ve any questions regarding the course or unable to make the decision, please leave a comment, and we’ll help you choose the right one.

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