9 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

• 9 Best Deep Learning Courses
  1. Deep Learning Specialization – Offered by DeepLearning.AI – [Coursera]
  2. Neural Networks and Deep Learning – Offered by DeepLearning.AI – [Coursera]
  3. Hands-on artificial intelligence – Offered by IBM – [edX]
  4. Complete Guide to TensorFlow for Deep Learning with Python – [Udemy]
  5. Deep Learning A-Z™: Hands-On Artificial Neural Networks – [Udemy]
  6. Natural Language Processing with Deep Learning in Python – [Udemy]
  7. Modern Deep Learning in Python – Udemy
  8. Data Science: Deep Learning and Neural Networks in Python – [Udemy]
  9. Deep Learning – [Udacity]
• Conclusion

Deep Learning Online Courses and Classes for This Year

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

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.

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.

Prerequisites

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.
 

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

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.

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!

Prerequisites

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.

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

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.

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.

Prerequisites

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.

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

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.

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

Prerequisites

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.

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

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.

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

Prerequisites

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

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

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.

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

Prerequisites

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.

 
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7. Modern Deep Learning in Python – Udemy

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

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.

Prerequisites

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

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

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.

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

Prerequisites

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.

 
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9. Deep Learning – [Udacity]

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.

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

Prerequisites

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.

 
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Conclusion

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