Top Online Deep Learning
Courses in 2021
Today, most advanced technologies encompass one form of artificial intelligence or another. Machine learning is one of these forms. If you are fascinated by machines and their ability to learn human intelligence, then you might want to learn deep learning.
Continue reading to find our picks of the best online deep learning courses in 2021. We will provide in-depth information regarding deep learning online courses so that you can choose the right one.
What Is Deep Learning?
Deep learning is an artificial intelligence subset that focuses on making a machine understand data algorithms. Machines use deep learning to take in complex neural networks with both supervised and unsupervised human input.
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Today, deep learning is present in various advanced technologies, including facial recognition, natural language processing, and speech recognition systems. It will continue to grow in popularity as technology advances.
Why Study Deep Learning?
If you enjoy artificial intelligence and computer science, then you likely will be interested in deep learning. Studying deep learning opens up opportunities for various lucrative AI careers.
You can become a machine learning engineer, software engineer, robotics engineer, data scientist, or computer vision engineer. This artificial intelligence field is perfect for those in search of a promising tech career and a competitive salary.
According to the Bureau of Labor Statistics, computer and information technology jobs are projected to increase by 11 percent between 2019 and 2029. This is well above the general job outlook estimations. Furthermore, according to ZipRecruiter, the national average salary for deep learning is $137,941.
What Do Deep Learning Courses Cover?
Deep learning courses cover artificial intelligence, machine learning, programming, and advanced AI applications. However, the course content varies depending on your background. For example, if you are new to AI and deep learning, your courses should cover basics such as Python programming, robotics, and computer vision.
If you already have an intermediate understanding of AI, then your courses should cover AI neural networks, Keras, TensorFlow developer, machine learning technologies, and natural language processing systems.
Overview of the Best Online Deep Learning Courses in 2021
Although deep learning and artificial intelligence are advanced tech sectors, they don’t necessarily require an advanced degree. You can enter the world of deep learning via online courses. Below is a list of our picks for the top online deep learning courses.
| Provider and Course | Price | Length | Certificate |
|---|---|---|---|
| AWS on Udacity Intro to Tensorflow for Deep Learning |
Free | 2 months | No |
| DeepLearning.AI on Coursera Deep Learning Specialization |
7-day free trial, then between $39 and $79 per month |
5 months | Yes |
| DeepLearning.AI on Coursera Neural Networks and Deep Learning |
7-day free trial, then between $39 and $79 per month |
23 hours | Yes |
| IBM on edX Deep Learning Fundamentals with Keras |
Free | 5 weeks | Yes, $99 for verified track fee |
| IBM on edX Hands-on Artificial Intelligence: Professional Certificate in Deep Learning |
$526 | 8 months | Yes |
| MITx on edX Machine Learning with Python: From Linear Models to Deep Learning |
Free | 15 weeks | Yes, $300 for verified track fee |
| Udacity Deep Learning Nanodegree |
$339 for 4 months |
4 months | Yes |
| Udemy Complete Guide to Tensorflow for Deep Learning with Python |
$11.99 | 14 hours | Yes |
| Udemy Data Science: Deep Learning and Neural Networks in Python |
$11.99 | 11 hours | Yes |
| Udemy Deep Learning A-Z: Hands-on Artificial Neural Networks |
$11.99 | 22.5 hours | Yes |
Top Deep Learning Courses of 2021
Now that you have a list of the best online deep learning courses, choose the course that suits your AI background and career goals most. Take a look below to learn more about course content, career options, and more.
Intro to Tensorflow for Deep Learning by AWS on Udacity
AWS is offering this course for software developers and machine learning engineers interested in pursuing deep learning. You will need to know Python syntax and basic algebra to succeed in the course.
The course teaches how to build deep learning applications using TensorFlow and provides plenty of hands-on project opportunities. By the end of the course, you will be an expert in using algorithms and advanced techniques with big datasets and AI applications.
Deep Learning Specialization by DeepLearning.AI on Coursera
This course on deep learning is suitable for those who already have an intermediate understanding of Python programming, machine learning, and algebra. Specialization topics include artificial neural networks, convolutional neural networks, recurrent neural networks, deep learning, and transformers.
You will also learn TensorFlow, backpropagation, mathematical optimization, and hyperparameter tuning. The course also consists of a hands-on project where you will build and train deep neural networks and work with other deep learning applications.
Neural Networks and Deep Learning by DeepLearning.AI on Coursera
This course is the first of five courses of the deep learning specialization offered by DeepLearning.AI. It requires you to have an intermediate knowledge of Python, machine learning, and algebra.
The course covers the foundations of deep learning topics such as artificial neural networks, backpropagation, Python, and neural network architecture. By the end of the course, you will be able to build and train your own neural networks.
Deep Learning Fundamentals with Keras by IBM on edX
Having an excellent understanding of the Keras platform when studying deep learning is crucial. This course by IBM teaches you just that, and it is suitable for deep learning newbies with AI understanding.
The course has prerequisites, including Python knowledge and a machine learning course on edX. You will learn the basics of deep learning, which includes definitions, neural networks, libraries, models, Keras, and essential job skills.
Hands-On Artificial Intelligence: Professional Certificate in Deep Learning by IBM on edX
This certification by IBM is best suited for those already working an AI job looking to further their careers. The course is designed for students with basic artificial intelligence, Python, and machine learning skills.
You will learn the foundations of deep learning, neural networks, supervised and unsupervised learning, and deep architectures. The course also covers natural language processing, GPUs, Keras, Tensorflow, and PyTorch. It consists of a capstone project where you will use deep learning technologies to develop and train deep learning models.
Machine Learning with Python: from Linear Models to Deep Learning by MIT on edX
This course by the Massachusetts Institute of Technology is part of the university’s micro master’s program in data science and statistics. It requires you to complete two courses on Python and probability.
This course is perfect for those looking to enhance their deep learning portfolios with verified certificates from reputable institutions. You will learn about advanced machine learning problems, kernel machines, neural networks, deep learning, and backpropagation.
Deep Learning Nanodegree by Udacity
If you have a basic understanding of Python and are looking to educate yourself in deep learning, this nanodegree program is for you. The course covers everything from how to learn artificial intelligence to advanced deep learning topics.
You will learn how to use tools such as Anaconda and Jupyter notebooks, along with learning the different neural networks. Additionally, you will also build your own network with Python and NumPy. This is a great program for those wanting ample real-world projects for their resumes and great job services to launch their deep learning careers.
Complete Guide to Tensorflow for Deep Learning with Python by Udemy
Udemy is a massive online open course platform that is known for its self-paced learning approach. You will learn to use neural networks, TensorFlow for regression and classification, and time series analysis.
This course also teaches you how to build your own neural network with Python and create generative adversarial networks. To succeed in this course, it is recommended that you have prior basic knowledge of Python and intermediate knowledge of mathematics.
Data Science: Deep Learning and Neural Networks in Python by Udemy
This is yet another Udemy course on deep learning and neural networks, designed for those wanting to learn Python for machine learning. This course requires you to have an understanding of math and logistic regression. In addition, you must install NumPy and Python before starting the course.
The course will teach you how deep learning works, how to code a neural network in Python and NumPy, and how to use Tensorflow. By the end, you will have a solid grasp of neural networks, backpropagation, deep learning, and feedforward.
Deep Learning A-Z: Hands-on Artificial Neural Networks by Udemy
In this well-rounded course, you will learn many types of neural networks in-depth. You will learn self-organizing maps, Boltzmann machines, and AutoEncoders. Just like most other deep learning courses, this course requires you to have basic Python and math skills.
The course also includes several projects covering real-world deep learning technologies. They include image recognition, churn modeling problems, fraud detection, and stock price prediction.
Choosing the Right Course
Online platforms have a huge selection of courses on deep learning and many other subjects, so choosing the perfect course can be difficult. Below are tips to help you find the best deep learning course for you.
Content and Provider Reputation
Because the subject of deep learning is an advanced topic in AI, most courses will expect you to have an understanding of artificial intelligence. Therefore, before picking the course, look for the prerequisites and the course content. You must also consider the provider’s reputation, as it plays a big role in enhancing your portfolio.
Duration
Most online courses are short and end within a matter of days or weeks. However, some courses can take from a couple of months to a year until completion. If you are looking for in-depth training on deep learning, then pick the longer course. If you want a foundational understanding of deep learning, then the shorter courses are more suitable.
Cost
This is one of the most important aspects of picking a course. If you are looking to learn deep learning without a certification of completion, choose one of the free courses. However, if you want to build a career in deep learning, then you can pick a paid course that is within your budget.
Schedule
The course schedules and delivery methods also matter. If you are a visual learner and don’t need one-on-one instruction, then Udemy’s self-paced videos are best for you. However, if you want a traditional learning experience, then enroll in online courses that are taught by industry experts.
Next Steps After Your Course
After completing the deep learning courses, there are several steps you can take to jumpstart your career. Read below to find possible next steps.
Practice Your Skills
The most important part of learning a new skill is practice. This means building more neural networks and training them in deep learning until you feel proficient. You can practice your skills with deep learning courses, machine learning courses, and other online resources.
Build Your Portfolio
The next step is to make your skills and background desirable to a hiring manager. If you don’t have an artificial intelligence background, start by doing AI projects. Then, move onto deep learning projects.
Add your course projects to your portfolio. You can also apply for freelance jobs to gain further industry experience. Make sure that you do projects that involve different data domains and tasks such as image, text, classification, generation, and multimodal.
Apply for a Job
This step will vary from person to person depending on their industry experience. If you are currently working in machine learning or artificial intelligence, then you can move up and apply for a deep learning position. However, if you are entirely new to the field, then you will have to start at entry-level AI positions and gain experience.
Should You Study Deep Learning?
If you are interested in working in advanced AI and machine learning fields, then you should study deep learning. Deep learning comes along with opportunities to work with high technologies such as self-driving cars, computer visions, and image processing.
So, if you are looking for a career that continuously works with new tech, engages in machine advancement, and comes with a competitive salary, then studying deep learning is a good choice for you.
