9 Deep Learning Online Courses and Specializations

Deep Learning Online Courses and Specializations

Artificial intelligence has brought about a revolutionary change in a plethora of industries. With Deep Learning Specialization, you get a streamlined pathway to take a definitive step in the field of AI. The proper deep learning course will equip you with all the necessary skills and knowledge to level your career.


If you pick a good deep learning course, you will also be entitled to the requisite advice from a leading industry expert. Now, the thing with the internet is everything is in abundance. So, if you type deep learning online courses, you will get a gigantic list of options.


To simplify the search process for you, we researched and measured the courses on five parameters to prepare this list of the top deep learning online courses.


The five parameters used by us are:


  1. Rating, reviews, and duration of the course
  2. Educational qualifications, experience, and the knowledge of the instructor
  3. Course’s learning outcomes
  4. The price that you pay


Now, let us get started and take a quick look at these courses one by one to help you find the best deep learning certification course.


Table of Contents


Deep Learning Online Courses and Classes for This Year

1. Deep Learning Specialization – Offered by DeepLearning.AI – [Coursera]

deep-learning-specialization-offered-by deeplearning

The Coursera deep learning specialization is a foundational program, which will equip you to gather knowledge of the challenges, capabilities, and consequences of deep learning. With this course, you will be confident to participate in this top-notch AI technology.


It is an English language course, but you can find subtitles in other languages in addition to English such as Spanish, Chinese, Arabic, Japanese, French, Portuguese, Italian, Vietnamese, Korean, German, Russian, and Turkish.


Rating 4.9 based on 116800+ reviews
Certification Yes
Course Material Available
Instructors Andrew NG, Kian Katanforoosh, Younes Bensouda Mourri
Refund Policy 7-day free trial, followed by a 14-days free refund policy
Recorded or live Recorded
Financial Aid Available
Duration Five months (flexible schedule)
Free/Paid Paid
Difficulty level Intermediate
Scope for improvement (Cons) Assignments could surely be more challenging, the duration could have been shorter, users who run the video with subtitles had difficulty reading through the whiteboard


Learning Outcome

There are a couple of things that you will learn via this Coursera deep learning specialization. A few of them have been listed below:


  1. Understanding of the deep neural networks, such as Convolutional, Recurrent, LSTMs, Transformers and their application
  2. Implementation of the vectorized neural networks
  3. Master theoretical concepts and their application in the real world with TensorFlow and Python
  4. Identifying key architecture parameters
  5. Employment of the optimization algorithms and standard techniques
  6. Hands-on experience with real world examples. For instance, machine translation, speech recognition, chatbots, and so much more.
  7. Building the neural networks in TensorFlow
  8. Train test sets
  9. Analysis of the variance for DL applications
  10. Building a CNN
  11. Application, Detection, and Recognition tasks
  12. Employing neural style transfer to generate art
  13. Application of algorithms to video data and images
  14. Building and training RNNs
  15. Working with Word Embeddings and NLP
  16. Employing HuggingFace tokenizers
  17. Transformer models to perform NER and Question Answering


Why is it the right deep learning course for you?

This course tops the list because of its remarkable learner career outcomes.

Eleven percent of students who took this course started a new career post-completion. In addition, it is a 100% online course. So, there is absolute flexibility on how and when you take this course. You can start and learn at your schedule and set and maintain the deadlines as desired.



This is an intermediate-level course. So, you must possess intermediate Python skills – understanding of loops, basic programming, data structures, and if/else statements.


Further, students must also have a basic knowledge of ML and linear algebra before signing up for this deep learning course. It is a five-month-long course, and a recommended effort of seven hours every week will suffice.

Reviews by AM:

I really enjoyed working through the modules of this course. The material was interesting and enlightening. The self-paced format worked well for me and I will look for similar courses going forward.



2. Neural Networks and Deep Learning – Offered by DeepLearning.AI – [Coursera]

Neural Networks and Deep Learning

It is the first course of a five-set deep learning specialization program. So, in this certification course, you will be equipped with all the foundational concepts of deep learning and neural networks.


Rating 4.9
Certification Yes
Course Material Available
Instructors Andrew NG, Kian Katanforoosh, Younes Bensouda Mourri
Refund Policy 7-day free trial, followed by a 14-days free refund policy
Recorded or live Recorded
Financial Aid Available
Duration 23-hours
Free/Paid Paid
Difficulty level Intermediate
Scope for Improvement (Cons) Learned users may find the math & calculus very basic, constant complaints regarding assignment quality, no feedback mechanism for students


Learning Outcome

This is hands down one of the best deep learning certification courses with an array of things for you to learn. Overall, after completing this course, you will know the challenges, capabilities, and consequences of deep learning.


A few of the things learned in this course have been listed below:


  1. Familiarity with the key technological trends responsive for the growth of deep learning trend
  2. Training, building and applying fully connected deep neural networks
  3. Identifying the significant parameters in a neural network’s architecture
  4. Implementation of efficient (vectorized) neural networks
  5. Application of deep learning to your personalized applications
  6. Participating in the development of leading-edge AI technology
  7. Skills necessary to employ Deep Learning in your projects
  8. Leveling your technical career


This is simply one of the best deep learning specialization courses because of its experienced instructors. In addition, almost 10% of the total course takers received a tangible benefit, so why can’t you!



This is an intermediate-level deep learning training program. So, you need intermediate Python skills, such as understanding loops, data structures, if/else statements, and basic programming.


Further, a grasp of the ML and linear algebra is also needed for this deep learning course to work well for you. To progress further in this field, you can take the remaining four five-part Deep Learning Specialization courses. It is a short-term course, but time and willingness to learn are primary.


Reviews by: MZ

This course is really great. The lectures are really easy to understand and grasp. The assignment instructions are really helpful and one does not need to know python before hand to complete the course.



3. Hands-on artificial intelligence – Offered by IBM – [edX]

Hands-on artificial intelligence

The next deep learning training is offered to you by trained experts from IBM. It is a free course, but you do not get certification in the free version, and the study material is also limited.


Rating N/A
Certification Optional
Course Material Available
Instructors Experts from IBM
Refund Policy No
Recorded or live Recorded
Financial Aid No
Duration 7 months (self-paced)
Free/Paid Both (Limited access to course material, no certificate, no graded assignments – in the free option)
Difficulty level Intermediate to advanced
Scope for Improvement (Cons) Costlier than its alternatives


Learning Outcome

A few things that this deep learning training will teach you are:


  1. Fundamental Deep Learning concepts, such as Neural networks
  2. Unsupervised and supervised learning
  3. Building, deploying, and training different kinds of deep architecture. A few of them are autoencoders, recurrent networks, and convolutional networks.
  4. Applying the learnings to the real-world scenarios
  5. Becoming thorough with concepts such as Natural Language Processing, text analytics, image and video processing, object recognition, recommender systems, computer vision, and an array of classifiers
  6. Becoming thorough with Deep Learning at scale with accelerated hardware and GPUs
  7. Employing prevalent Deep Learning libraries, such as Tensorflow, PyTorch, and Keras, to industry issues


Why is this the best deep learning training?

We think of this deep learning specialization as the best for more than a few reasons. First, it is a package of six skill-building courses. Second, the course is detailed and offered by IBM, i.e., one of the most renowned names.


This deep learning certification course won’t overburden you with weekly tasks as it is designed carefully. Also, you can take the course at your pace and set or reset the deadlines as per your schedule.



This is a long deep learning training program and will require seven months for completion. So, you need to dedicate two to four hours every week to complete the course.



4. Complete Guide to TensorFlow for Deep Learning with Python – [Udemy]

Complete Guide to TensorFlow for Deep Learning with Python

At number fourth on our list is a Udemy course. It is one of the top-selling best deep learning certification courses from the platform.


There are abundant exercises that can help you test your skills developed via the course. It is a 100% online course that you can watch on your laptop, tablet, TV, or mobile. The deadlines are flexible, so complete this course without any rush.


The reason why this is one of the best deep learning certification courses on Udemy is because it has been designed to balance practical and theoretical concepts with some notebook guides of code and easy-to-reference slides and notes.


Rating 4.5 based on 16000+ reviews
Certification Available
Course Material Available with lifetime access
Instructors Jose Marcial Portilla
Refund Policy Yes – 30-days
Recorded or live Recorded
Financial Aid No
Duration 14 hours
Free/Paid >Paid
Difficulty level Beginners to Intermediate
Scope for Improvement (Cons) Upgrade to TensorFlow 2 needed


Topics covered under this deep learning training program

Some topics included in this deep learning certificationcourse are:

  1. Neural Network Basics
  2. TensorFlow Basics
  3. Artificial Neural Networks
  4. Densely Connected Networks
  5. Convolutional Neural Networks
  6. Recurrent Neural Networks
  7. AutoEncoders
  8. Reinforcement Learning
  9. OpenAI Gym and a lot more!


Learning Outcome

Some things you will learn in this deep learning certification program are:

  1. Understanding the working of the neural network
  2. Building your tailored Neural Network from Scratch using Python
  3. Employing TensorFlow for Classification, Time Series Analysis, Image Classification, solving Unsupervised Learning Problems, and Regression Tasks
  4. Utilizing TensorFlow with AutoEncoders, Convolutional Neural Networks, and Recurrent Neural Networks
  5. Conducting Reinforcement Learning via OpenAI Gym
  6. Creating Generative Adversarial Networks
  7. Becoming an expert at Deep Learning
  8. Knowledge of the complexities and the use of Google’s TensorFlow framework



To take this best deep learning course,you must have some programming knowledge, mainly Python, and some basic math knowledge – math, such as mean, standard deviation, and more.

You can also choose and enroll for some of the best TensorFlow courses online here.


Reviews by: Hans Palacios

Overall, great overview and examples to work with! The lessons are explained at a good pace and in enough detail to successfully complete each of the projects. There were some variations needed along the way to adapt the use of TensorFlow, but with some quick searches in Stack Overflow, they were all resolved without issue for me. I’m looking forward to continuing learning from Jose through his other courses, particularly the one delving into TensorFlow 2. Keep up the great work!



5. Deep Learning A-Z™: Hands-On Artificial Neural Networks – [Udemy]

Deep Learning A-Z™

Ranked at number fifth on our top deep learning certification courses is this Udemy course. It is a best-selling course and has been a student delight ever since its launch.



Rating 4.6 based on 37900+ reviews
Certification Available
Course Material Available with lifetime access
Instructors Kirill Eremenko, Hadelin de Ponteves, and Ligency Team
Refund Policy Yes – 30-days
Recorded or live Recorded
Financial Aid No
Duration 22.5 hours
Free/Paid Paid
Difficulty level Beginners
Scope for Improvement (Cons) Some repetitive content from the machine learning course, this course could have been more detailed, a few topics are rushed, getting used to instructor’s accent also takes some time


Learning Outcome

In this Udemy’s one of the top-rated deep learning online courses,there are many things for you to learn. A few of them include:


  1. Getting equipped with the knowledge of Artificial Neural Networks and their application
  2. Getting familiar with the understanding of Convolutional Neural Networks and their application
  3. Gaining comprehensive knowledge of Recurrent Neural Networks and their application
  4. Acquiring adequate knowledge of Self-Organizing Maps and their application
  5. Becoming familiar with Boltzmann Machines and their application
  6. Learning AutoEncoders and their application


Is it the right deep learning course for you?

This Udemy deep learning certification is suitable for:


  1. Anyone who aspires to learn Deep Learning
  2. Intermediate-level students who understand Deep learning or machine learning basics, such as classical algorithms, logistic regression or linear regression, and advanced topics, such as Artificial Neural Networks, wish to learn more.
  3. Anyone who is not comfortable with coding but wishes to learn Deep learning, and its application on datasets
  4. An entrepreneur who wishes to build disruption in an industry using the most cutting edge Deep Learning algorithms
  5. Students wanting to start a career in data science
  6. Anyone who wishes to level up in Deep Learning
  7. People who wish to become a data scientist
  8. People who wish to add more value to their business by employing Deep Learning tools
  9. Business owners who wish to understand leveraging the Exponential technology of Deep Learning in their business



To take this one of the top-selling deep learning online courses, you require basic mathematics knowledge and an understanding of basic Python.


Reviews by: Anshul Vankar

I had no doubt about the quality of this course as I had already done their Machine Learning course. For those looking for a beginner to intermediate level of knowledge in Deep Learning, I would definitely recommend this course, as the concepts are explained very clearly and in simple language. This also helps in advance level knowledge as the basic concepts become clear.



6. Natural Language Processing with Deep Learning in Python – [Udemy]

Natural Language Processing with Deep Learning in Python

The sixth best deep learning course on our list is provided to you by Udemy. It is a detailed course, which has been taken and appreciated by approximately 40000 students. Apart from getting a deep learning certification, you get a lifetime of access to the study material here.


Rating 4.6 based on 6500+ reviews
Certification Available
Course Material Available with lifetime access
Instructors Lazy Programmer Team and Lazy Programmer Inc.
Refund Policy Yes – 30-days
Recorded or live Recorded
Financial Aid No
Duration 12 hours
Free/Paid Paid
Difficulty level Advanced
Scope for Improvement (Cons) The instructor keeps fixating on if you don’t meet the prerequisites – this course isn’t for you, the tone of the instructor may sound boring and rude to some, the course isn’t very budget-friendly


Learning Outcome

Some things that you will learn with this deep learning course are:


  1. Gaining knowledge of word2vec, CBOW method, and skip-gram method, and their implementation.
  2. Employing recursive neural networks for sentiment analysis
  3. Getting equipped with negative sampling optimization.
  4. Understanding and implementing GloVe via alternating least squares and gradient descent.
  5. Using recurrent neural networks for parts-of-speech tagging and named entity recognition
  6. Utilizing recursive neural tensor networks for sentiment analysis
  7. Using Gensim to acquire pre-trained word vectors, calculate analogies and similarities



To take this deep learning training,there are a few prerequisites that you need to bear in mind.


  1. Knowledge of tree algorithms.
  2. Coding a feedforward neural network in Theano and a recurrent neural network from basic primitives in Theano
  3. Ability to derive and code the equations on your own
  4. Coding scan function
  5. Install Matplotlib, Numpy, TensorFlow, Theano, and Sci-Kit Learn.
  6. Knowledge of basic math, including concepts such as probability (conditional and joint distributions), calculus, multiplication, and matrix addition
  7. Understanding of if/else, loops, lists, dicts, sets


Who should take this course?

This deep learning specialization is ideal for:

  1. Students and professionals who aspire to build vector representations for different NLP tasks.
  2. Students and professionals who aspire to be thorough with recursive neural networks.


Reviews by: Arafat Saiyed

Good to start Python from Scratch, very brief explanation for each and every function. I enrolled earlier for different python courses from other sites, but unable to complete. This time cover 25%-30% in a week and looking to complete the course in a month. Thanks Ryan keep it up



7. Modern Deep Learning in Python – Udemy

Modern Deep Learning in Python

At number seventh on our list for the top deep learning online courses is again a Udemy course.


Rating 4.6 based on 2500+ reviews
Certification Deep learning certification provided
Course Material Available with lifetime access
Instructors Lazy Programmer Inc.
Refund Policy Yes – 30-days
Recorded or live Recorded
Financial Aid No
Duration 11.5 hours
Free/Paid Paid
Difficulty level Intermediate to Advanced
Scope for Improvement (Cons) Without fulfilling the prerequisites, the course is going to be difficult for you; 12 hours is too less of a time to explain the complexities of the topic


Learning Outcome

Some things that this deep learning course will teach you are:


  1. Writing a neural network with Keras, MXNet, CNTK, and PyTorch.
  2. Building a neural network in Theano and TensorFlow.
  3. Creating a neural network, which works brilliantly in the MNIST dataset
  4. Developing an understanding of the basic building blocks of TensorFlow and Theano.
  5. Application of the adaptive learning rate procedures, such as RMSprop, Adam, and AdaGrad.
  6. Application of momentum to backpropagation to train neural networks
  7. Learning the difference between stochastic gradient descent, batch gradient descent, and full gradient descent.
  8. Knowledge and implementation of the dropout regularization and batch normalization in TensorFlow and Theano.



There are a few prerequisites necessary for this deep learning training.These include:


  1. Knowledge of gradient descent.
  2. Thorough knowledge of statistics and probability
  3. Understanding of Python coding – sets, dicts, lists, loops, and if/else.
  4. Knowledge of drafting a neural network.
  5. Understanding of Numpy coding – loading a CSV file, vector, and matrix operations
  6. Requisite knowledge of Matplotlib.


Who should take this course?

  1. This is the best deep learning course for anyone who wants a deep learning certification to add to their resume and learners who wish to enhance their skills.
  2. Students and professionals who aspire to build on their understanding of machine learning
  3. Data scientists who wish to equip themselves with a greater understanding of deep learning
  4. Data scientists who are equipped with the knowledge of gradient descent and backpropagation, aspiring to better it with adaptive learning rate procedures, momentum, and stochastic batch training


Reviews by: Yves Augusto Lima Romero

Excellent course! The instructor brings information from papers and incorporates this knowledge to explain the reasons why some practices are usually adopted



8.  Data Science: Deep Learning and Neural Networks in Python – [Udemy]

Deep Learning and Neural Networks in Python


Next, we have another hugely popular deep learning course by Udemy. This course has been availed by over 40000 students, who have thoroughly enjoyed and benefited from the course.


Rating 4.6 based on 7600+ reviews
Certification Available
Course Material Available with lifetime access
Instructors Lazy Programmer Inc.
Refund Policy Yes – 30-days
Recorded or live Recorded
Financial Aid No
Duration 11 hours
Free/Paid Paid
Difficulty level Intermediate
Scope for improvement (Cons) Feedback about the course isn’t taken very well by the instructor

Learning Outcome

Some of the things that you will learn with this deep learning course are listed below:


  1. Working on deep learning
  2. Building a neural network from basic building blocks
  3. Coding a neural network from scratch in Google’s TensorFlow, Numpy, and Python.
  4. Installation of TensorFlow
  5. Defining the different neural networks terms.
  6. Describing various neural networks and solving some problems on each of these types.
  7. Building a neural network with an output has K > 2 classes using softmax
  8. Deriving the backpropagation rule with first principles



To take this deep learning course,there are a few prerequisites listed below.


  1. Install Python and Numpy.
  2. Knowledge of basic math concepts, such as calculus, probability, derivatives, and matrix arithmetic.
  3. Understanding of the basic linear models, such as logistic regression and linear regression.
  4. Knowledge of Python coding, such as dicts, lists, if/else, sets, and loops.


If you are not familiar with logistic regression, you can take the course from the instructor.


Who can enroll in this deep learning training?

This deep learning course is ideal for:

  1. Students who aspire to learn about machine learning.
  2. Professionals who wish to employ neural networks in data science and machine learning.


Here are some of the best data science courses you might also be interested in.


Reviews by: Louis Chiu

Very good course for deep learning. Not just teaching the intuition and teach you how to use the api. But spend most of the time teaching the concept and derivation of the algorithm. Now I can really understand how I construct a neural network and without the api. However, as the tensorflow used in this course is really old, it may be better to take the tensorflow 2.0 course first.



9. Deep Learning – [Udacity]

Deep Learning

The last deep learning course is presented to you by Udacity. This course is offered to you in collaboration with AWS and Facebook Artificial Intelligence. Also, please know that it is not a standalone course. Instead, it is a nanodegree program.


Rating N/A
Certification No
Course Material Available
Instructors Multiple instructors
Refund Policy Yes – Can cancel anytime but different payment packages have different refund periods.
Recorded or live Recorded
>Financial Aid No
Duration 4 months
Free/Paid Paid
Difficulty level Beginner friendly, working knowledge of Python helps
Scope for Improvement (Cons) Very expensive


Learning Outcome

Some of the things that are covered in this course are:

  1. Basics of neural networks
  2. Building your neural network with Python and Numpy
  3. Building multi-layer networks
  4. Analyzing real-world data
  5. Building convolutional neural networks
  6. Classifying the images based on patterns
  7. Learning data compression and image denoising
  8. Building recurrent neural networks and using them for TV scripts or new text
  9. Generative adversarial networks
  10. Deploying a Sentiment Analysis Model



This program includes beginner-friendly deep learning online courses. However, to take this course, you must have a working knowledge of Python.


In addition, you are also expected to have basic math knowledge. This is a long deep learning course of four months, and you need to put in an effort of 12 hours every week for the course.


Who should take this course?

This deep learning training is designed for students interested in machine learning, deep learning, or AI. You can also take up a specialised machine learning course from our selection of top courses.


Is it the right deep learning course for you?

In this course, you will get to work on real-world projects. Further, you will have continuous feedback from industry experts. You will also avail yourself of access to career services and a personal career coach.


Reviews by: abhishek s.

The course is going very steadily, explaining each a every step as I am going through the lessons. The mathematics used behind building the neural networks is explained deeply and with the practical learning experience I am very much satisfied by the course and the way of teaching. with coding exercises in every lesson and all the mini projects coming up in between to give much more practical experience is really a cool step. So far till now I am enjoying building the neural networks (honestly this is the most important stuff on the planet right now). Big Like for the Course




So, these are the nine best deep learning courses. We have tried to include all the information about these deep learning specialization courses on our list.


However, if you aspire to know more about them, you can click on the link annexed and read more about the courses, such as the author’s bio, reviews on the course, enrollment details, and the course price.


Be assured, regardless of the selection you make, you will not regret your decision. So, go ahead, take your pick, and learn deep learning from an expert.


Best Deep Learning Courses Reviewed by 10 Deep Learning Experts 4.7