14 Best Deep Learning courses online for beginners & intermediates
As a subfield of Machine Learning, Deep Learning professionals are one of the most sought-after in the industry today. This rise has compounded with a rise in online courses and MOOCs to make Deep Learning much more accessible as a subject of study.
Below, we give you ar tour of some of the most popular and best deep learning courses online suitable for beginners, intermediates, and advanced level learners.
Deep Learning Courses – Overview
Deep Learning is an artificial intelligence method of approaching data that is modeled on the functioning of the human brain. Just as how a human brain processes new information based on a schema and creates patterns to deal with recurring problems more swiftly, DL can process data without external intervention by creating neural networks (patterns).
Although evolving, deep learning has already begun to create massive waves in AI applications, demystifying computer memory and building easily accessible data sets.
Both machine learning (ML) and deep learning (DL) are applications of AI that allow machines varied levels of autonomous decision-making. While ML comes in handy with smaller data, deep learning is applied to sift information across large data sets.
Recent years have seen a great rise in the application of Deep Learning across various sectors. Some examples include image recognition in commercial apps, online recommendations, and research tools used in the health sector.
DL applications across multiple industries have sparked interest among professionals, students, and tech enthusiasts to learn more. Below, we now discuss some of the best deep learning courses online to help you start a career in the industry.
Best Deep Learning Courses 2021 – Top 4
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*Disclaimer: This post contains affiliate links. Read the full disclosure at the end of this post.
The following list of best deep learning courses and career tracks provides brief overviews of popular programs available for beginner, intermediate, and advanced level learners.
1. Deep Learning – Udacity
Udacity’s Deep Learning (visit website) is a beginner-friendly nanodegree best suited to students with a basic knowledge of Python programming. In this course, students will learn how to build and apply a self-created neural network to key challenges in the field of AI.
Deep Learning by Udacity spans over six modules that can be easily completed in four months at 12 hrs/week. Upon enrolling in the course, learners will begin with encountering major development tools in DL including Anaconda and Jupyter notebooks.
From here, students will kick start the application by building their first network in the program using Python and NumPy. Next, you’ll learn to create convolutional networks that allow the classification of images based on patterns and image objects.
With the fundamentals down, learners can begin building recurrent networks, generate realistic images by applying Deep Convolutional GAN (a generative adversarial network), and deploying models to assess user input.
Coming out of the course, you will have developed a firm understanding of how to resolve common challenges in Machine Learning and Deep learning, such as image classification, time-series, and model deployment. One of the best deep learning courses for beginners.
Instructors: Cezanne Camacho, Mat Leonard, Luis Serrano | Certificate: Yes
Deep Learning Courses: 5 | Prerequisites: Basic Python
Price: $339/month. $1356/4 months. Try with a discount. View Course.
2. Deep Learning A-Z: Artificial Neural Networks – Udemy
Udemy’s Deep Learning course (visit website) spans around 173 lectures that will cost students some 23 hours of dedicated learning. It is aimed at individuals interested in AI, ML, and DL and who are looking for ways to expand their knowledge. As you come out of the course, you will have developed skills in creating DL algorithms in Python.
Deep Learning A-Z will walk students through the intricacies of intuitive AI, building convolutional and recurrent neural networks and applying what they learn as the course will progress. Along the way, students will learn related DL techniques and tools such as self-organizing maps, Boltzmann machines, and AutoEncoders.
In this course, students will also familiarize themselves with Tensorflow and Pytorch, two of the most widely used open-source libraries for DL. Since both libraries are fairly new, users will have to analyze themselves when and where to apply either for the best results. Starting from $11.99, this is one of the best deep learning courses for smaller budgets.
3. Deep Learning: Convolutional Neural Networks in Python – Udemy
Udemy’s second course (visit website) on the list spans over 78 lectures and 12 hours for video. It is the second in the series of deep learning courses offered by the site to help professionals advance their DL and AI careers. Designed for learners with intermediate–level skillsets, the course requires foundational skills in Python, Numpy, and Maptplotlib.
As students enroll, they will begin their DL journey with an understanding of the intuitive function of convolutional neural networks in achieving results in computer vision. Some of these include object detection, image segmentation, and high-end image generation.
Once learners understand why convolutional neural networks (CNN) are crucial in DL, they will move on to understanding a CNN architecture. Next comes implementing CNNs in TensorFlow 2 and applying these to image-recognition tasks and NLP (Natural Language Processing) for text classification.
Successful candidates can earn a certificate upon course completion. This is one of the best deep learning courses for intermediates who shy away from high tuition fees.
4. Introduction to Deep Learning – Coursera
Coursera’s Introduction to Deep Learning (visit website) is part of its Advanced Machine Learning Specialization. Individuals interested in DL can exclusively join the course, or enroll in the complete specialization alternatively. It is an introductory program, spanning 6 units that will cost learners some 34 hours of learning.
Enrolling in the course, students will begin with a beginner’s introduction to linear models that are the building blocks for deep architectures. Learners will also encounter stochastic optimization methods that will help them master all subsequent models. In the second unit, you will learn more about linear models and create your very first deep neural network.
The following modules focus on convolutional neural network (CNN) architectures, recurrent neural networks, and unsupervised parts of deep learning. Towards the end, you can apply your skills to different tasks involving sequential input/output.
As part of the program’s capstone project, students will generate descriptions for real-world images using skills previously developed in neural networks for images and texts.
5. Deep Learning Specialization – Coursera
This DL specialization (visit website) is designed for learners possessing an intermediate-level skillset in DL who’re looking to achieve next-level expertise at their own pace.
Some of the key concepts covered in this specialization include building and implementing deep neural networks and convolutional neural networks (CNN). It also covers image generation, creating networks using Tensorflow, and creating recurrent neural networks.
Coming out of the specialization, learners will have developed the ability to identify architecture parameters, application of neural networks for detection and image-recognition tasks. You will also learn how to apply DL algorithms for image generation, optimization algorithms for training sets, and testing variance, etc.
At the same time, the program focuses on related ML concepts such as error reduction in ML systems, understanding complex ML settings, and multi-task learning. Due to its interdisciplinary, this is certainly one of the best deep learning courses on Coursera.
Tp: To meet some of the prerequisites of Deep Learning courses, browse our lists best Python courses or data science courses. If you follow a different focus on AI, browse our guides of artificial intelligence courses or Machine Learning courses.
6. Building Deep Learning Solutions with PyTorch – Pluralsight
As the name indicates, Pluralsight’s deep learning course (visit website) helps learners use PyTorch. PyTorch is a popular, open-source ML and DL library, to perform many common and challenging domain tasks. These include image classification, style transfer, predictive analytics, and natural language processing.
Individuals who meet the prerequisites of ML literacy and Python programming are encouraged to apply. As with typical Pluralsight learning paths, the program is divided into beginner, intermediate and advanced units. Learners can run a skill IQ test before opting for any particular tier. It’s one of the best deep learning courses from novice to expert.
At the beginner level, individuals will encounter their first PyTorch project, learning foundational concepts and applying them in the cloud. At the intermediate level, you will learn the application of PyTorch to DL problem domains. Finally, at the advanced level, you will use PyTorch to create predictive analytics models, deploy and monitor them.
7. Deep Learning and Python for AI with Microsoft Azure – FutureLearn
FutureLearn’s Deep Learning program is an expert track for AI and computer science professionals with an intermediate-level skill set looking to make a switch to the next level. The course that takes roughly 21 weeks for completion at 5-6 hours per week. It’s one of the best deep learning courses online for those having a specific job and career objective.
In this deep learning course, learners will meet the opportunity to pass the Microsoft Azure AI Engineer Associate Exam by building skills using the platform’s advanced lab environments and software tools powered by CloudSwyft and Azure.
This program is designed around key concepts relating to DL that facilitate AI system operations. These concepts and tools include deep semantic similarity models (DSSM) and neural models to perform machine translations. Apart from these, learners will also work on building basic signal processing and common algorithms for language modeling.
Deep Learning Courses: 5 | Duration: 21 weeks | Certificate: Yes.
Level: Intermediate | Price: $39/month. Access to all courses
8. Artificial Intelligence Engineer – Simplilearn
Artificial Intelligence Engineer is Simplilearn’s Master’s program curated in collaboration with IBM. Towards the end, the program will award successful candidates with AI certification. In this course, individuals looking to make a career switch to Data Science will learn fundamental AI, ML, and DL concepts.
Simplilearn’s program in AI is a career path comprising of 5 mandatory courses that come with individual certificates and 3 electives. You will begin your AI journey with introductory concepts in AI and Data Science. The track then expands your skillsets to ML and Deep Learning with TensorFlow and Keras. These tools will help learners master integral concepts and models in DL and implement them in AI and computer vision domains.
The capstone project will introduce students to a realistic industry-aligned challenge. In the elective program, you can opt for natural language processing (NLP), AI applications, and using Python in Data Science depending on your prior knowledge.
This is one of the best deep learning courses for those seeking guidance and certification. Before applying, students have developed foundational skills in Python and Statistics.
Provider: Simplilearn, IBM | Average salary: $92-140K | Capstone: Yes
Certification: Master’s Program | Price: $1,499. Pay in installments: $136
9. Professional Certificate in Deep Learning, IBM, edX
edX directs its 8-month course on AI towards individuals with a solid understanding of major DL concepts, including Python programming, and Statistics. Learners looking to make a career switch can earn a professional certificate authorized by IBM.
Spanning over a total of 6 online deep learning courses, this certification program will walk you through DL fundamentals using Keras, DL applications using PyTorch, Python, and Tensorflow, and scaling DL with GPUs.
Coming out of the program, you will have developed an understanding of fundamental concepts in DL including neural networks, types of DL architectures, convolutional networks, recurrent networks, and Autoencoders.
With the foundations laid, you will begin implementing your expanded DL skillset to address real-world problems of object recognition, NLP, computer vision, etc.
Duration: 8 months. 2-4 hours/week | Certificate: Yes.
Provider: IBM, edX | Price: Free. With certificate $525. Discount available.
10. Machine Learning Engineer – Springboard
Springboard has introduced a Career Track Program for individuals hoping to build their own portfolio and break into the AI domain. In this six-month course, with live 1:1 mentorship, you will master some foundational ML and Deep Learning models and concepts through hands-on learning.
At the outset, the track is designed to help students gain unique engineering skills in ML and DL to give them a competitive edge in the industry. By the end of it, you will have designed an ML/DL prototype and deployed a running and accessible application.
Skills you will gain during this course include linear and logical regression, anomaly detection, and cleaning data. An entire unit in this program is dedicated to deep learning. It focuses on principles of DL neural networks and other foundational techniques, RNNs, CNNs, generative DL, GANs, and related core concepts.
Duration: 6 months | Certificate: Yes. | Info: Hands-on experience. Career support.
1:1 Mentoring: Yes | Job Guarantee: Yes | Price: $10,000. $1,680/month.
11. Natural Language Processing – Coursera
Coursera’s next deep learning course specialization (visit website) on the list aims to help learners break into the NLP space by mastering cutting-edge DL and NLP techniques.
In this four-course specialization, learners will encounter the tools needed to perform integral NLP functions such as sentiment analysis, complete analogies, translations, and auto-corrections. You will learn to do this by using logistic regression, naïve Bayes, word vectors, and dynamic programming.
In the later phase of this specialization, learners will move on to recurrent neural networks, GRUs, LSTMs, and Siamese networks in TensorFlow. You learn to perform advanced functions, such as named-entity recognition, identifying a duplicate question, etc. Coursera awards successful participants with a sharable certificate towards the end of the program.
12. Deep Learning and Neural Networks in Python – Udemy
This Deep Learning course (visit website) is designed to help intermediate-level learners go deep with their understanding of the neural network theory and build their own systems using Python and Tensorflow. Deep Learning and Neural Networks in Python comprises 14 sections, spanning over 84 lecturers with more than 12 hours of video content.
As learners enroll in the program, they will begin with detailed exposure to the back-end of real-life DL operations. From here, your next milestone would be to understand different kinds of neural networks and create your own from scratch using Python and Numpy.
Next, participants will code neural networks with TensorFlow, derive backpropagation rules, and understand DL-related keywords such as feedforward, activation, etc.
Individuals seeking enrolment must possess Numpy and Python, have basic skills in Calculus, Arithmetic, and Statistics, and be familiar with foundational concepts and keywords used in logistic regression.
13. Practical Deep Learning for Coders – Fast.ai
Fast.ai has curated this course to make DL accessible to individuals who do not possess the advantage of a highly technical or mathematical background. At the same time, learners are expected to possess around a year’s experience in coding and basic math.
The first three chapters in the course exclusively deal with those DL phenomena that would benefit product managers and executives without needing to code.
Coming out of the course, you will have developed enough understanding of how to create, train and deploy DL models and some of the most recent DL techniques in practice. It’s one of the best deep learning courses online you can take for free.
Duration: Self-paced. | Price: Free | Prerequisites: Coding knowledge
14. Spinning Up In Deep RL – Open.ai
As an educational platform, OpenAI bridges the gap between powerful AI and deep Reinforcement Learning (RL) by using key concepts in Deep Learning as the stepping stones for RL. The latter is a DL approach that uses trial and error for reinforcement.
With the mission of safe AGI development at its core, OpenAI uses algorithms in Spinning Up to help students with more diverse skillsets contribute towards safely implementing AI. Once on the platform, users will encounter a curated list of important papers on DL, a code repo of the implementations of key algorithms, and exercises to get them started.
Duration: Self-paced | Price: Free | Prerequisites: Coding knowledge
How to Choose the Best Deep Learning Courses Online
Before researching the best deep learning courses, we recommend analyzing your prior knowledge and experience with deep learning, the time you can leverage, and your financial resources.
Keep in mind that Deep Learning is still relatively new and many see it as an off-shoot or sub-field of machine learning (ML). For this reason, it might be helpful to start with basics in Machine Learning and then move towards broader DL phenomena.
Here are a few questions you can ask yourself before opting for a deep learning course.
1. If it’s an introductory course, does it address any tech-based deficiency common amongst novice learners or attempt to bridge the technical gap for individuals from non-AI backgrounds?
This is important because while learners enroll in a program with some expectations, they are also expected to meet certain prerequisites. Failing to carefully evaluate whether you meet these can result in wastage of time, energy, and resources.
2. Do the integral topics and concepts align with the goals I have set for myself or my career?
Deep Learning can benefit professionals from diverse fields and may not always have or require expertise in coding or algorithms. Individuals hoping to keep abreast of newer developments that may or may not impact their domains would look at a Deep Learning course differently from those who are seriously considering a career switch to AI.
3. Do I have the time, willpower, and resources to invest in a Deep Learning course, specialization, or degree program?
Technically speaking, this should be the first question on the list. Your choice of a course that is not commensurate with your current circumstances, financial or otherwise, is destined to be a mistake. To avoid such an error, analyze both your current situation as well as important course metrics, such as duration, topics covered, price, etc.
Best Deep Learning Courses Online – Verdict
Summarizing our tour of the best deep learning courses online. Ever since the self-paced study of advanced topics, such as Deep Learning, became more accessible, the sheer weight of opting for the right course shifted onto the shoulders of the learners.
To make the most of your time and financial investment in a course or a similar curated program, begin with a complete analysis of your current skill set, career goals, and course outcomes. If these align, you are not only more likely to complete the course but also genuinely expand your skillset.
Best Deep Learning Courses Online 2021
- Deep Learning – Udacity
- Deep Learning A-Z: Artificial Neural Networks – Udemy
- Deep Learning: Convolutional Neural Networks in Python – Udemy
- Introduction to Deep Learning – Coursera
- Deep Learning Specialization – Coursera
- Building Deep Learning Solutions with PyTorch – Pluralsight
- Deep Learning and Python for AI with Microsoft Azure – FutureLearn
- Artificial Intelligence Engineer – Simplilearn
- Professional Certificate in Deep Learning, IBM, edX
- Machine Learning Engineer – Springboard
- Natural Language Processing – Coursera
- Deep Learning and Neural Networks in Python – Udemy
- Practical Deep Learning for Coders – Fast.ai
- Spinning Up In Deep RL – Open.ai
What are the best deep learning courses online? Have you enrolled in any of the DL courses mentioned above? What is your overall learning experience? Let us know in the comments below or contact us for questions.
What is Deep Learning?
As a subset of Machine Learning, Deep Learning can build on the information extracted or patterns generated by machine learning algorithms to generate bigger and more intuitive patterns. This is achieved through a hierarchical organization of neural networks formed from interlinked neuron codes.
Such a hierarchical setup allows the system to process data in a non-linear way, unlike traditional machines. This unique breakthrough has become responsible for the ability of deep learning, now understood as an AI function, to conduct unsupervised, non-intervened learning of large unstructured data.
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