Online LearningData Science8 Best Deep Learning Courses to Learn Online in 2024

8 Best Deep Learning Courses to Learn Online in 2024

Deep learning is a powerful machine learning technique based on neural networks with feature learning. It now has infinite practical applications in every industry, including speech/image recognition, mobile advertising, medical image analysis, and many more.

As of 2024, deep learning has become the basis of modern AI development. More and more companies need experts in this field to help build powerful AIs of their own.

Because the skill is hugely in demand, deep learning is inevitably a lucrative skill. According to Indeed, deep learning engineers earn a base salary of $131,229 per year in the United States. This number might increase even further within a few years,

Undoubtedly, if you want a high-earning career in tech with excellent prospects, deep learning is 100% worth learning.

Learning the fundamentals of deep learning online is plausible. However, not every course is high-quality and worth the investment.

Hence, I decided to conduct independent research and provide you with a list of the eight best deep learning courses in this post. This article will help you find the right deep learning course, saving hours of your research time.

Disclaimer: This post from Victory Tale contains affiliate links. We will receive a small commission from learning platforms if you purchase a deep learning course from them.

Still, we always value integrity and prioritize our audience’s interests. You can rest assured that we will present all the courses truthfully.

Things You Should Know


Deep learning is not for beginners. Beginners cannot learn deep learning without background knowledge.

However, you do not need a master’s degree in computer science to learn deep learning. Still, you will need a good understanding of these subjects as follows.

Before taking any course on deep learning, your Python programming skills need to be intermediate or higher. In other words, you should have taken at least one Python for Data Science course before.

Besides Python, other programming languages (C++/JavaScript/Java) are NOT required for learning deep learning. However, former hands-on experience in machine learning techniques would be beneficial.

**If you are a beginner, you could follow the links above to select the best online courses that would help fulfill the prerequisites**

1. Udacity’s Deep Learning Nanodegree

Udacity’s Deep Learning Nanodegree program is more like an online Bootcamp. After you enroll in the training program, you will gain access to the mentor, who is an industry expert. They will provide feedback on your projects and assist you if you have any problems.

Udacity's Deep Learning Nanodegree Program
Udacity’s Deep Learning Nanodegree Program

Course Content and Student Support

Udacity’s Nanodegree is always much more time-consuming than other online courses. The workload each week is considerably higher. You should expect to spend 12 hours a week completing this Nanodegree.

Below is what you will learn from the course.

  • Introduction to Deep Learning
  • Neural Networks: You will build them by using various tools such as Python, NumPy, and PyTorch and use them to predict bike-sharing patterns
  • Convolutional Neural Networks: You will build CNNs and use them to classify medical images based on patterns and objects.
  • Recurrent Neural Networks: This section will teach you to build RNNs and LSTM networks with PyTorch and use them to generate TV scripts
  • Generative Adversarial Networks: You will learn about GANs in this part and use them to generate realistic images
  • Sentiment Analysis Model: You will train and deploy a new sentiment analysis model by utilizing PyTorch.

In all sections of the course, you will complete several real-world projects that would enhance your deep learning skill gradually.

Once you have completed the program, you can access Udacity’s career services. Experts will help improve your resume, Github portfolio, and LinkedIn profile to ensure that they are up to professional standards. This is crucial for landing interview invitations from tech giants.


This program costs $249 per month. However, you can choose to buy a 4-month bundle and get a 15% discount.

However, Udacity frequently offers discounts and financial support. These can reduce program fees by another 40%. You will need to create an account to access them, as I did below.

Thus, with these discounts, you can access all Udacity’s course catalogue at $149 per month or even lower.

Pros and Cons



      2. Deep Learning Specialization by Andrew Ng

      This Coursera specialization is from Andrew Ng and his education technology company, As a Stanford University professor, Andrew is one of the leading experts in deep learning and AI. Learning from the best cannot make things go wrong.

      Besides Andrew, you will learn with Kian Katanforoosh and Younes Bensouda Mourri, who are artificial intelligence researchers with years of experience in the field.

      Deep Learning Specialization by Andrew Ng and Deep Learning AI, one of the best deep learning courses online
      Deep Learning Specialization by Andrew Ng and Deep Learning AI

      Course Content

      As a typical Coursera specialization, it comprises five minor courses as follows:

      1. Neural Networks and Deep Learning – This course will help you get started on neural networks and deep learning. You will learn to build different neural networks from the beginning, including shallow and deep neural networks.

      2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization – This course will continue where the first course left off. The instructors will explain the optimization process and best practices/techniques that help improve the results.

      You will learn standard neural network techniques in this course including but not limited to hyperparameter tuning, L2 and dropout regularization, batch normalization, and many others.

      You will also learn how to apply optimization algorithms such as mini-batch gradient descent, Momentum, and RMSProp.

      Part of the deep learning course

      3. Structuring Machine Learning Projects – Unlike other minor courses in the specialization, this one is a stand-alone project that aims to provide the industry experience of a machine learning project leader to learners.

      This course is beneficial for those who have taken a machine learning course. Essentially, you will learn how to reduce errors from your machine learning models, handle complicated settings, compare results, and apply end-to-end learning, transfer learning, and multi-task learning.

      4. Convolutional Neural Networks – The fourth course in the sequence will cover the fundamentals of CNNs or convolutional neural networks and their variations.

      You will also perceive how to apply this knowledge to use, especially in computer vision (autonomous cars, image recognition, and the like.)

      5. Sequence Models – The last course in the series will discuss NLP (Natural Language Processing) models and their applications, such as music generation, speech recognition, language translation, sign language reading, etc.

      You will learn to build RNNs (Recurrent Neural Networks) and apply them to language modeling. suggests spending 5 hours a week for four months to complete an entire specialization.

      You can also audit it for free, but the full course, costing $49 per month, is much better, as you will get graded assignments, feedback on your work, and a certificate.

      Pros and Cons



          3. IBM’s Deep Learning

          This 6-course deep learning program on the edX platform is from IBM, a tech giant which is a leading developer of AI technologies. Many people may be familiar with its flagship AI product, IBM Watson.

          Throughout the entire series, you will learn with an IBM expert in data science and AI. Thus, you can be confident the knowledge is practical and accurate.

          IBM Deep Learning Course
          IBM Deep Learning – one of the best deep learning training programs online

          Course Content

          Unlike Andrew Ng’s course, this series from IBM is more in-depth. You will use different frameworks, such as Keras, Pytorch, and Tensorflow, to build various types of neural networks.

          However, you don’t need to master these frameworks beforehand. A good understanding of Python and machine learning will suffice.

          The series comprises six courses as follows.

          1. Deep Learning Fundamentals with Keras – This first course will introduce fundamental deep learning concepts and applications. You will also have a chance to build deep learning models utilizing the Keras library.

          2. PyTorch Basics for Machine Learning – This course will bring you back to machine learning basics. You will implement classic machine learning algorithms using Pytorch and build a solid foundation before proceeding to deep learning.

          deep learning course from IBM

          3. Deep Learning with Python and PyTorch – You will use the basic PyTorch knowledge from the second course to build advanced deep neural networks. Furthermore, you will train them by applying methods such as initialization, batch normalization, and various optimizers.

          4. Deep Learning with Tensorflow – You will learn Tensorflow fundamentals in this fourth course and its beneficial applications. Furthermore, all types of deep architectures will be explained in-depth.

          5. Using GPUs to Scale and Speed Up Deep Learning – The fifth course will focus on optimization. You will realize how to use GPUs for your deep learning networks and train them for object recognition.

          6. Applied Deep Learning Capstone Project – You will build, train, and test a deep learning model on your own based on real-world case studies. There are no restrictions on deep learning frameworks or libraries.

          IBM recommends spending 2-4 hours on the course content, and you will complete the entire series in 8 months.

          You audit the entire series for free. However, if you want a deep learning certification and feedback on your projects, you will need to pay the tuition fees ($525.60).

          Pros and Cons



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

              This deep learning course is one of the most popular deep learning courses on Udemy. You will learn with Kirill Eremenko and Hadelin de Ponteves, industry experts in the data science and artificial intelligence fields.

              Kirill and Hadelin's Deep Learning Course
              Kirill and Hadelin’s Deep Learning Course

              Course Content

              As a beginner course, students will learn deep learning from the beginning. Below is the summary of what you will learn in the course.

              • Artificial Neural Networks (ANNs)
              • Convolutional Neural Networks (CNNs)
              • Recurrent Neural Networks (RNNs)
              • Self-Organizing Maps
              • Boltzmann Machines
              • Autoencoders
              • Review machine learning concepts (Regression & Classification, Data Preprocessing, Logistic Regression implementation)

              I like the structure of this course. Instructors will switch back and forth between lecturing theories and guiding you on building neural networks in real life.

              Furthermore, the instructors structured the lessons well, so learners could quickly realize the borderline between the theoretical and project parts. Coming back to the course after a long pause would be effortless.

              This course receives 4.5/5.0 in ratings and has more than 300,000 students.

              Pros and Cons



                  5. A Beginner’s Guide to Deep Learning

                  If you are looking for a robust deep learning tutorial to kickstart your journey swiftly, this one from Educative may interest you.

                  Unlike other courses in this list, you will learn all deep learning concepts by reading texts.

                  Though it may sound like reading a book, Educative’s course is much more entertaining, as you will complete many interactive exercises and assignments throughout the program.

                  Educative's Deep Learning Course
                  Educative’s A Beginner’s Guide to Deep Learning

                  Course Content

                  Below is the summary of the content you will learn in the course.

                  • Machine Learning reviews
                  • Deep Learning Fundamentals
                  • Building simple perceptron models in NumPy
                  • Deep neural networks in NumPy
                  • Building deep learning models using Keras
                  • Fine-tune Keras model

                  Apart from the main content, you will complete several challenges and projects. For example, you will build a letter classification model based on a 3-layered neural network. Most students complete the course in approximately 20 hours.

                  This course alone costs $34 per year. However, you can subscribe to the annual unlimited plan to access all 240 courses on the Educative platform. The price is $16.66 per month.

                  Pros and Cons



                      6. Introduction to Deep Learning

                      This deep learning training program by Purdue University will introduce learners to the world of deep learning. You will learn the fundamentals and perceive how deep learning algorithms solve many engineering problems.

                      Introduction to Deep Learning by Purdue University
                      Introduction to Deep Learning by Purdue University

                      Course Content

                      Despite its name, this course is lengthy and comprehensive. You will start with very basic content and progress gradually to advanced concepts such as LSTM (Long Short-term memory) and NTM (Neural Turing Machine.)

                      The summary of the course content is as follows.

                      • Introduction to Deep Feedforward Networks
                      • Regularization for Deep Learning
                      • Optimization for training deep models
                      • Convolutional Neural Networks
                      • Recurrent Neural Networks

                      In addition to the lectures, you will work on several hands-on projects utilizing Tensorflow and Keras.

                      After you finish the course, you will possess the ability to make suitable design choices for deep learning algorithms, implement and optimize state-of-the-art deep architectures, or even tackle open problems in deep learning.

                      Students will need to spend approximately 16 weeks on the course, while the workload for each week is 6-9 hours.

                      You can audit the course for free. However, if you want feedback on your models and receive a certificate, you will need to pay a hefty $2,250 to access the full course.

                      Pros and Cons



                          7. Lazy Programmer’s Deep Learning Courses

                          Lazy Programmer, a highly knowledgeable AI and machine learning engineer, has created a dozen deep learning courses on Udemy. I have taken some of these courses and realized that they are in-depth and practical. Thus, I decided to recommend him to you.

                          Lazy Programmer’s Course

                          Course Content

                          As indicated above, Lazy Programmer has more than a dozen courses in deep learning. Below are some that I believe most learners may be interested in.

                          Data Science and Neural Networks in Python – This course will teach you the fundamentals of deep learning, including how deep learning works, how the model is built, and different types of neural networks.

                          You will then learn to build a neural network from scratch using Python and Tensorflow.

                          Length: 11.5 hours, Ratings: 4.6/5.0, 45,800+ students

                          Modern Deep Learning in Python – This training course will help you understand several frameworks and libraries used for building neural networks. These include Tensorflow, Theano, Keras, PyTorch, MXNet, and many more.

                          You will also learn to implement beneficial techniques, including dropout regularization, backpropagation, batch normalization, and many more.

                          Length: 11.5 hours , Ratings: 4.7/5.0, 27,600+ students

                          Unsupervised Deep Learning in Python – There are many courses on supervised learning and unsupervised learning. However, this one will teach a much more advanced “unsupervised deep learning.”

                          The instructor will go through several unsupervised neural networks, such as the autoencoder and Restricted Boltzmann Machines (RBMs). You will understand how they work and put them to proper use.

                          Length: 10.5 hours , Ratings 4.7/5.0, 17,300+ students

                          Deep Learning: Convolutional Neural Networks in Python – This course is a deep dive into CNNs or convolutional neural networks.

                          After you complete the entire course material, you will be able to explain their architecture by heart, create them on Tensorflow and apply them to NLP for text classification.

                          Length: 12 hours, Ratings 4.6/5.0, 25,900+ students

                          Deep Learning: Recurrent Neural Networks in Python – This course will focus on RNNs (Recurrent Neural Networks.) You will learn to build them on Tensorflow and apply them to image classification, stock price prediction, and spam detection (NLP.)

                          Length: 12 hours. Ratings 4.6/5.0, 24,000+ students

                          Deep Learning: Advanced NLP and RNNs – This advanced course will deal with sophisticated RNNs and Sequence-to-Sequence (Seq2seq models). You will build a machine translation system, a text classification system, and a memory network.

                          Length: 8.5 hours, Ratings 4.6/5.0, 19,500 students

                          Advanced AI: Deep Reinforcement Learning in Python – This advanced course will teach you to use reinforcement learning and deep learning together. You will build various deep learning agents such as A3C and use CNNs with Q-Learning.

                          Length: 10.5 hours, Ratings 4.6/5.0, 32,800 students.

                          You need to purchase these courses separately. As these are Udemy courses, I recommend buying them at a discount when they are on sale. Thus, each will cost you less than $15.

                          Absence of Course Order

                          Many students, including me, find Lazy Programmer’s courses inaccessible as the entire series lacks a proper curriculum, so we don’t know which courses to take in succession. Lazy Programmer’s ambition to create more courses could make the series even fuzzier.

                          In fact, to solve the problem, Lazy Programmer has provided a lengthy guide on it. Still, I don’t think this guide is the solution, as each learner has distinct backgrounds.

                          Hence, my advice is if you are new to deep learning, you may want to take the first course elsewhere. Andrew Ng’s, IBM’s, and Kirill’s are solid options.

                          Subsequently, if you want to enhance your knowledge on specific deep learning topics, you can purchase one or more of Lazy Programmer’s courses. Thus, an absence of the course order will not bother you at all.

                          Lazy Programmer’s courses have different prerequisites. Some may even require knowledge of Tensorflow or Theano. Therefore, it is vital to read the requirements section on the Udemy sales page very carefully.

                          Pros and Cons



                              8. Deep Learning and Neural Networks for Financial Engineering

                              Not available in 2024

                              Those in quantitative finance will find this training program by New York University on edX highly beneficial.

                              This program will teach you deep learning and neural networks as other courses. However, all examples and case studies are from finance. Thus, it is effortless for finance professionals to apply the knowledge to use.

                              NYU Deep Learning Course

                              Course Content

                              Below is a summary of all the course content.

                              • Overview of classical machine learning
                              • Introduction to Deep Learning and Neural Networks
                              • Convolutional Neural Networks
                              • Recurrent Neural Networks
                              • Techniques to train neural networks (backpropagation, initialization, etc.)
                              • Interpretation and Transfer Learning
                              • Advanced Recurrent Architectures (RNNs, LSTM, Neural Programming)
                              • NLPs

                              As a typical university course, there will be quizzes, assignments, and projects to test your skills. You will build neural networks and use multiple deep learning techniques with ease when you reach the end of the course.

                              This course is self-paced. Hence, you can create your own study schedule. According to the university, you will spend 4-6 hours per week on the course, and you will finish it in 7 weeks.

                              Unlike most edX courses, this one is NOT free to audit. If you are interested in it, you need to pay $799 to access the full course.

                              Pros and Cons



                                  Pun Anansakunwat
                                  Pun Anansakunwat
                                  Founder of Victory Tale, a multipotentialite who has a particular interest in technology. He loves to spend time testing new products and learning interesting topics to broaden his insights. After graduating from Columbia University in 2014, he makes a living by being a stock market investor, a private tutor, a writer of three published books, and finally a website owner.

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