Whether you’re a novice student of computer science, budding coder, or software developer looking to acquire the knowledge and skills required to pursue a career in deep learning, online learning offers you the opportunity to do so from the comfort of your home. In this article, we review the best deep learning courses available on the web, so stick around. Moreover, we give you these best Programming certifications review to take a look at, too.
3 Top Deep Learning Courses
[Best Deep Learning Course Overall]
With more than 500,000 reviews, Coursera’s Deep Learning Specialization is amongst the most popular deep learning courses on the web.
[Best for the Money]
Udemy’s Deep Learning A-Z is perfect for two categories of students. The first category is those who are looking to learn how to code their own deep learning algorithms.
[Best for the Beginners]
3. Introduction to Deep Learning with OpenCV (LinkedIn Learning)
While the courses we’ve reviewed so far aren’t necessarily for complete beginners, Introduction to Deep Learning with OpenCV is.
What Is the Ideal Deep Learning Course for You?
Pinpointing the ideal deep learning program for your needs can be difficult considering the broad variety of options available on the web. To simplify your selection process, we’ve compiled a list of the best deep learning courses online. And to give you a fair share of other options, you can also inform about VBA Certification, and consider a Business Intelligence Course or a Full Stack Developer Course.
Best Deep Learning Course Reviews
With more than 500,000 reviews, Coursera’s Deep Learning Specialization is amongst the most popular deep learning courses on the web. Although this is a beginner-friendly program, it does come with a couple of prerequisites: basic Python programming skills and a solid background in linear algebra. Being familiar with the concepts of deep learning is considered a plus.
This deep learning specialization program is composed of five comprehensive courses, namely Neural Networks and Deep Learning, Improving Deep Neural Networks, Structuring Machine Learning Projects, Convolutional Neural Networks, and Sequence Models.
From understanding the major technologies that are helping drive deep learning and grasping the different key parameters that are essential to neural
network architecture to be able to apply sequence models to text synthesis and other natural language problems, you’ll come out of this course ready to tackle the field of deep learning with utter confidence.
The names of the online courses and the topics within each course might seem intimidating, but don’t worry, as the course is developed and tutored by Stanford professor, Andrew Ng, who also happens to be the founder of DeepLearning and co-founder of Coursera. Andrew Ng has an uncanny knack for simplifying complex topics into easily digestible bits.
One of the strongest selling points of this course, apart from being remarkably comprehensive, is that it covers natural language processing in detail. Most deep learning courses fail to cover this topic the way this specialization program does, which is very odd considering it’s a key topic in the field of deep learning.
Another strong selling point is that this course comes with an excellent applied learning project that will have you assessing and working on real-life case studies in various domains, including autonomous driving, healthcare, natural language processing, music generation, and more.
What’s more, this program will have you working on multiple deep learning models that will add tons of value to your portfolio. One of the models is a machine learning flight simulator based on real-world case studies.
Deep Learning Specialization requires approximately 4 months to be completed, punching in 5 hours per week. It’s available with several subtitles, including English, French, Italian, Chinese, Arabic, and Ukrainian, to name a few. You also get to earn a professional certificate upon compilation.
Udemy’s Deep Learning A-Z is perfect for two categories of students. The first category is those who are looking to learn how to code their own deep learning algorithms. The second category is those who want to learn deep learning on a fundamental basis with the aid of TensorFlow and PyTorch. The basic nature of the material offered in this course makes it perfect for beginners.
The prerequisites for this program are also basic. You only need high-school mathematics and basic Python programming knowledge. This Udemy course contains 22.5 hours' worth of on-demand video lectures, 5 downloadable resources, 37 articles, and a certification of completion.
There are plenty of reasons why this Udemy course is ranked high on our list; the first being its robust structure. The tutorials within the course are grouped into two volumes; each volume representing the main branches of deep learning:
supervised and unsupervised learning. Note that each volume covers only three fundamental deep learning algorithms.
The second reason why we love this Udemy course is its intuitive tutorials. Rather than bombarding the student with tons of theory without explaining the practicality of what the student is learning, this course does an excellent job of helping the student develop a strong intuition for the concepts that are being taught.
Another prominent reason why this Udemy course is great is its extensive real-world datasets. Students get to work on real-world business problems such as image recognition via convolutional neural networks, stock price prediction with the aid of recurrent neural networks, fraud investigation via self-organizing maps, and building a recommender system via Boltzmann machines.
The course is backed by a support team of professional data scientists that are always prompt to respond to any question. No matter how complicated your query might be, you’re guaranteed a detailed response within a maximum of 48 hours from submitting your question.
This Udemy course will help familiarize you with a wide range of tools; most prominently PyTorch and TensorFlow. Note that big-name companies use TensorFlow to bring their work to life, including eBay, Intel, Airbus, and Uber.
Theano is another tool that you’ll get to learn. It’s an open-source library that’s quite similar to TensorFlow when it comes to functionality. Other tools that you’ll get to use include Keras, scikit learn, Numpy, Matplotlib, and Pandas.
While the courses we’ve reviewed so far aren’t necessarily for complete beginners, Introduction to Deep Learning with OpenCV is. This course, as the name suggests, introduces you to the fundamental concepts of deep learning and trains you to use OpenCV.
OpenCV is a vast library of programming functions aimed primarily at real-time computer vision. The library was developed by Intel, but it later received support from Willow Garage and then Isteez. The library is completely free for use under Apache 2 License.
Introduction to Deep Learning with OpenCV is developed and tutored by Jonathan Fernandes, machine learning and artificial intelligence leader and consultant. Jonathan is a specialist when it comes to data science, big data, and AI, so there’s no doubt this course is full of vital and practical insights.
This deep learning course is composed of 4 volumes, excluding the introduction and conclusion. The introduction will teach you how to generate insights from images and videos with the aid of OpenCV.
It’ll also show you how to install Python and Anaconda. Further, you get to learn how to create a virtual environment and how to use a text editor.
The first volume, aptly titled Deep Learning with OpenCV, sheds light on what deep learning is and what OpenCV is. Then, it shows you how to utilize OpenCV in deep learning. The second volume is all about images and videos in OpenCV.
The third volume will teach you how to work with the deep neural networks module (dnn). Lastly, the fourth volume will teach you how to work with the numerous deep learning models, from the classification of images/videos to the latest version of the object detection algorithm, YOLOv3.
Considering this is a course for absolute beginners, Jonathan Fernandes ends the course with a conclusive volume that indicates the next steps that a beginner deep learning engineer or data scientist should take after finishing the course.
Please bear in mind that this isn’t a comprehensive deep learning course by any means. It’s not packed with tutorials, assignments, tests, real-life projects, and so forth. It’s just an introductory course that aims to help enrollees get their foot through the door.
Machine Learning Engineering Career Track from Springboard is a heavy-duty program that’s mainly designed for experts. The program comes with tough admission criteria that require the applicant to pass a programming challenge and demonstrate their knowledge in software.
As you can tell from this course’s name, it’s not just about deep learning, it covers everything that has to do with AI, including tested machine learning models in production and at scale, computer vision, image processing, machine learning engineering stack, deploying machine learning systems, and working with data.
Machine Learning Engineering Career Track is one of the most challenging AI courses on the web. Not only is the admission process tough, but the graduating process is also packed with a ton of hardships.
The good news, however, is that Springboard guarantees a job proposal for all the students who graduate from this course. In addition to the job offer, students who complete this course will be equipped with the knowledge and skills required to dabble in any AI project.
Many of Springboard’s graduates were sought after and hired by big-name companies such as Google, Microsoft, Home Depot, Verizon, Zoom, Maxar, Zume, and Wayfair.
The course is backed by a 1-on-1 mentorship program that enables the student to have weekly guided calls from industry experts as their personal mentors. Students get to enjoy an unlimited number of mentor calls at no extra cost.
This advanced program requires a bunch of prerequisites. Enrollees need to have at least one year of professional work experience in software development and engineering or in data science with the aid of general-purpose languages like C++, Python, and Java.
The course also welcomes enrollees that have a Ph.D. or Master’s degree in mathematics, EE, physics, informatics, economics, data science, computer science, financial engineering, operations research, applied stats, and all other degrees that revolve around programming,
The admission process is as follows: you fill out and submit your application, get an interview with an admissions director, pass the skills survey, then join the program. The course requires around 6 months to be completed, with students devoting 15-20 hours per week.
Despite being a notably short course, Deep Learning with Keras from Pluralsight will get you up to speed with the different theories and best practices of deep learning neural networks with the aid of Keras.
This online course is divided into three phases. Throughout the first phase, you’ll learn how to use Keras to establish multiple layers of neurons in a short period of time. Each layer will be defined with a specific functionality vital to the overall solution you’re trying to come up with in a practical scenario.
The second phase is all about using Keras to interconnect the several layers you’ve created in the previous stage in order to build your deep neural networks. The final stage will teach you how to employ Keras in implementing full-fledged deep neural networks such as recurrent and convolutional neural networks.
Why Keras? Well, building neural networks is a challenging process that can take a lot of time. Keras, which serves as an API, is all about quickness. Instead of wasting hours or even days writing code in Python, Keras enables you to create full-fledged networks in no time.
This online course will also teach you how to leverage the power of DL frameworks such as Theano, CNTK, and TensorFlow with the aid of Keras. Further, you’ll get to work on a ton of real-world projects, which will help expand your learning experience and add to your resume.
Deep Learning with Keras is formulated and tutored by Jerry Kurata, one of the most prominent solutions architects at InStep Technologies. Kurata has several online courses available on Pluralsight that are worth checking out, including Understanding Machine Learning with R and Python, TensorFlow: Getting Started, and Getting Started with Azure Machine Learning.
Like we already mentioned, this is a pretty short online course, spanning only 2.5 hours, which isn’t enough to cover the entire field of deep learning. However, Kurata managed to include a ton of practical material into the course with lots of useful tips and real-world examples. It’s one of the most practical and straightforward deep learning courses on the web.
The content of this deep learning course can be accessed for free throughout the first ten days after signing up, and since this is a 2.5-hour course, you can very easily finish it during the free trial period.
Udacity’s Deep Learning Nanodegree program is one of the best courses for students that have a basic understanding of Python programming and linear algebra. Does that mean it’s not fit for absolute beginners? Not at all! The course starts out with very simple lessons that aspiring deep learning engineers can easily grasp with no prior deep learning knowledge.
This nanodegree program covers a host of topics that have to do with AI, with deep learning being center stage. You start with an overview of neural networks, then, as the course progresses, you start going deeper into recurrent networks and convolutional networks.
The course will teach you how to build convolutional neural networks and utilize them to classify different images based on the objects and patterns that appear within them. The networks you built will also be used for image denoising and data compression.
You’ll also learn how to build recurrent networks with PyTorch. Further, you’ll learn how to create short-term memory networks and perform sentiment analysis.
After learning about recurrent and convolutional neural networks, you’ll learn about generative adversarial networks (GANs) and how you can implement them in generating images. You’ll get to enjoy insight from Ian Goodfellow, the creator of generative adversarial networks, as well as Jun-Yan Zhu, founder of CycleGANS.
That’s not all this nanodegree has to teach you, though; you’ll be learning the basics of setting systems for AI-assisted tasks, and you’ll learn how to use Amazon Sagemaker and PyTorch to execute your projects.
One of the best things about this course is that, upon its completion, the student is automatically enrolled into other more advanced AI courses from Udacity; at a cost, of course. Such online courses include Self-Driving Car Engineer, Flying Car and Autonomous Flight Engineer, and more.
The DL course requires around 4 months to be completed, dedicating only 12 hours per week. It doesn't have any demanding prerequisites. You just need some basic knowledge of Python. If you don’t know anything about Python, we highly recommend checking out Udacity’s Learn AI Programming with Python before signing up for Deep Learning Nanodegree.
The last course we’re going to review is a fundamentals course from edX. Being a fundamentals course, you don’t need to be an expert in the field of deep learning to excel in it. You don’t even need prior deep learning knowledge.
Deep Learning Fundamentals with Keras will take you on a journey through the applications of deep learning, fundamentals of neural networks, various deep learning techniques and models, and how you can go about building your first DL model with the aid of Keras, which we’ve already established as one of the easiest yet most powerful libraries to use.
This course will ingrain a solid data science foundation into your brain before even attempting to cover practical, real-life projects, which makes it one of the best courses for beginners on the web. It starts by explaining what deep learning is, how neural networks learn, what deep learning libraries are, and what the difference between supervised and unsupervised learning is.
With the theory out of the way, you’ll start learning about the different applications of deep learning, weights and biases of neural networks, vanishing gradient, building a regression model, building a classification model, and much, much more.
The course is divided into 4 modules: Introduction to Deep Learning, Artificial Neural Networks, Deep Learning Libraries, and Deep Learning models. Each module covers a wide variety of topics that are essential to becoming a sought-after deep learning engineer and data scientist.
Despite its vast and comprehensive curriculum, the estimated time required to finish this course is around 5 weeks, dedicating only 2-4 hours per week. This deep learning course is self-paced, so you can learn at your own leisure.
The course can be audited for free, but if you wish to receive a verified certificate, you’ll have to pay $99. IBM is the institution behind this course, with Ph.D. and data scientist Alex Aklson as the instructor.
How to Choose the Right Deep Learning Course?
When trying to choose from the variety of deep learning courses online, there are three factors that you need to take into consideration: utilized tools, course prerequisites, and your end goal.
Most deep learning courses rely on Tensorflow and PyTorch as their open-source algorithms libraries. That being said, each of these two libraries requires programming knowledge, be it in Python, Keras, or others. Depending on which tools you’re familiar with or which tools you want to use, your course selection will vary.
You also want to take into consideration the prerequisites that come with each course. Beginner courses don’t really have demanding prerequisites. Most of them need high-school maths and basic Python knowledge. Intermediate and advanced courses, on the other hand, tend to have more demanding prerequisites.
Lastly, you need to take your end goal into consideration. Are you looking to start a career in autonomous domains like self-driving vehicles, or are you looking to keep things digital? Your choice will help you determine which of the above-reviewed courses is best suited for you.
Regardless of your background and skills in the field of deep learning, you’re guaranteed to find a course that suits your needs amongst the ones reviewed in this article.
If you’re a complete beginner, we highly recommend opting for LinkedIn Learning’s Introduction to Deep Learning with OpenCV or Udemy’s Deep Learning Nanodegree.
If you’re an intermediate, all of the above-reviewed deep learning courses are perfect for you, except for Springboard's Machine Learning Engineering Career Track, which is tailored for experts.
Please remember that deep learning is a complex field that takes time and effort to comprehend completely. Even the shortest course on networks and deep learning will require you to go through its content more than once, so make sure you have enough time and resources to spare before signing up for a DL course.