Last updated on April 23rd, 2024 at 11:00 pm
Are you in search of valuable deep learning courses to learn new skills? If so, this article lists the 14 best deep learning courses online for both novice and experienced learners.
Deep learning is a buzzing technology these days, encouraging students and professionals to learn trending skills to work with data. If you want to build a career in this field, then joining a valuable deep learning course online will help you.
In short, Deep Learning is a sub-field of artificial intelligence and machine learning that deals with artificial neural networks that learn complex data patterns from big data sets.
So, by learning the fundamentals of deep learning, you will get opportunities to enhance your skills in machine learning and artificial intelligence.
In this article, I have listed 14 of the best deep learning courses online, which I have selected from many options available. Whether you are a beginner learner or a professional looking to enhance your skills, you will find a suitable course from this list.
Interested in learning Machine Learning techniques? Check out our article on best machine learning courses available online.
List of the Best Deep Learning Courses Online
1. Deep Learning Specialization – Offered by DeepLearning.AI
The first entry on this list of the best deep learning courses online is the Deep Learning Specialization Course. It covers the fundamentals of deep learning, enabling you to understand what deep learning is and its applications and challenges.
It discusses how to build and train neural network architectures (CNN, RNN, Transformers, LSTMs) using top-notch strategies such as Dropout, BatchNorm, etc. Also, you will learn the concepts of Python and TensorFlow and how to use them in the field of deep learning.
By joining this course, you will take your first step toward the field of AI and learn the necessary skills to enhance your career.
Who is this course for?
Anyone who is willing to step into the world of AI and understand the concepts of deep learning should join this course. Students should have a basic understanding of Python, linear algebra, and machine learning to be able to understand the topics covered in this course.
If you are someone who is in a technical role but doesn’t understand neural networks and deep learning, this course will help you advance your career. Apart from that, anyone with programming experience and interests in artificial intelligence and neural networks can consider joining this course.
Course Highlights
Institution – DeepLearning.AI
Platform – Coursera
Level – Intermediate Level
Instructor – Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh
Duration – 3 Months
Rating – 4.9
Language Options – 22 Languages Available
Schedule Type – Flexible Scheduling
What you will learn
The curriculum of this deep learning specialization course comprises four courses on the topic of deep learning. This course begins with the fundamentals of neural networks and deep learning. You will learn the ability, importance, and challenges of deep learning and how to use it to build advanced AI technologies.
Further, the course explains the techniques of deep neural networks, such as hyperparameter tuning, regularization, and optimization. Then, you will learn how to build deep learning projects using your experience in machine learning and the guidance of top instructor Andrew Ng.
The concepts of convolutional neural networks and sequence models will also be covered in this course.
2. Introduction To Deep Learning and Neural Networks with Keras – Offered By IBM
Those who are looking to build a career in the field of deep learning and participate in building excellent technologies with AI can join this course. You will get to know about the fundamentals of deep learning and neural networks in this course.
In this course, you will learn how to build deep learning models using the Keras library. Throughout this online course, you will learn the basics of deep learning models and neural networks and the differences between them. Also, it discusses the fundamentals of supervised and unsupervised deep learning.
Who is this course for?
Any person who is interested in the field of AI and deep learning can consider joining this course. Since this course is of the intermediate level, students should have some experience in programming and related fields to understand these topics better.
If you want to do a job in this field or learn the deep learning concepts to advance your career, then you can join this course.
Course Highlights
Institution – IBM
Platform – Coursera
Level – Intermediate Level
Instructor – Alex Aklson
Duration – 8 Hours Approx
Rating – 4.7
Language Options – 22 Languages Available
Schedule Type – Flexible Scheduling
What you will learn
The first chapter of this course explains what deep learning and neural networks are and why deep learning is becoming so popular. You will resonate deep learning algorithms with the functions of the human brain in this course.
Then, the course discusses artificial neural networks, including the gradient descent algorithm, backpropagation, and activation functions.
In the next chapter, you will be introduced to several deep learning libraries such as Keras, TensorFlow, and PyTorch and learn to use the Keras library to build classification and regression models.
Finally, you will build deep learning models using the Keras library and do a similar learning project in the end. By completing this course, you will have a better understanding of deep learning and neural networks and learn how to use the Keras library.
3. Introduction To TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning – Offered by DeepLearning.AI
This introductory course on TensorFlow is a great choice for anyone who wants to learn this open-source framework for machine learning, deep learning, and artificial intelligence. It teaches how to use TensorFlow to build deep learning and machine learning models to solve real-world problems.
This course doesn’t describe deep learning or machine learning in detail. If you want to learn these concepts, you can join the Deep Learning Specialization Course or the Machine Learning Specialization Course.
Who is this course for?
Anyone who is interested in deep learning and wants to learn how to use TensorFlow for the creation of deep learning models can join this course.
Having knowledge of Python programming, deep learning, and machine learning fundamentals will help you in this course.
If you are looking for a specific course to learn what TensorFlow is and how to use it with deep learning, then this course is for you.
Course Highlights
Institution – DeepLearning.AI
Platform – Coursera
Level – Intermediate Level
Instructor – Laurence Moroney
Duration – 17 Hours Approx
Rating – 4.8
Language Options – 22 Languages Available
Schedule Type – Flexible Scheduling
What you will learn
The first chapter of this course introduces machine learning and deep learning, describing what they are and their capabilities. With basic programming skills, you can understand these concepts better.
Then, the course discusses what computer vision is and solving problems with your code. The later session of this course discusses convolutional neural networks and how to use them to enhance computer vision. Finally, you will work with real-world images and learn how to improve them using deep neural networks.
4. Introduction To Deep Learning – Offered by Udacity
Introduction to Deep Learning is an introductory course that discusses the fundamentals of deep learning and its applications. You will learn both the theory and practical aspects of deep learning in this course.
The course explains deep learning algorithms, architectures, and goals, along with their fundamentals. Also, it introduces the students to the PyTorch framework and how to use it to deploy deep learning models.
Overall, this course is a perfect choice to learn the basics of deep learning and use the PyTorch library for deep learning models.
Who is this course for?
Anyone who wants to learn deep learning and its uses can consider joining this course. This course describes how to determine the right scenario to use the deep learning tool.
This course requires students to have skills in linear algebra. Also, having skills in the related field will be helpful in this course. If you have an interest in the latest technologies, such as neural networks, deep learning, and artificial intelligence, and want to explore these fields, this course will fulfill your needs.
Course Highlights
Platform – Udacity
Level – Intermediate Level
Instructor – Erick Galinkin
Duration – 4 weeks
What you will learn
In the first lesson, you will meet the instructor, Erick Galinkin, and get introduced to the topics covered in this course. Then, you will learn about deep learning and when to use it for your work with some examples.
The next chapter will teach you different concepts of neural networks, including Backpropagation, Gradient Descent, and Error Functions. Also, you will come to know how neural network algorithms function like the human brain and solve certain problems.
Then, the course describes how to train neural networks using several techniques such as regularization, early stopping, dropout, local minima, and random restart. After that, you will do a project with PyTorch that classifies using MNIST Dataset.
5. Intro To Deep Learning with PyTorch – Offered by Udacity
Intro to Deep Learning is a free course on the fundamentals of deep learning that will teach students how to create deep neural networks with PyTorch. PyTorch is a powerful and open-source machine learning framework that helps deploy deep learning models.
It has a wide range of applications, including computer vision and natural language processing. You will get to know about this machine learning library and how to use it in this free deep learning course on Udacity.
Who is this course for?
Anyone who is interested in learning the concepts of deep learning and neural networks can consider joining this course. It doesn’t require any previous experience of students in similar fields, but having such skills will be good.
If you are looking for an introductory course on deep learning to understand the basics, you can join this course.
Course Highlights
Platform – Udacity
Level – Beginner Level
Instructor – Luis Serrano, Alexis Cook, Soumith Chintala, Cezanne Camacho, Mat Leonard
What you will learn
In the beginning, you will learn the functioning of neural networks and how to train them using data. Then, the course discusses what PyTorch is and explains how to use it to create and train deep neural networks.
The course also covers convolutional neural networks, recurrent neural networks, style transfer, and deployment of PyTorch models.
By the end of this course, you will be able to understand the basics of deep learning and neural networks and use PyTorch Library to build and train deep neural networks.
6. Deep Learning for Business – Offered by Yonsei University
Deep learning, machine learning, and artificial intelligence are emerging technologies that are being used in every field nowadays. If you don’t use this in your product or service, you will be far from the trend.
Therefore, it is crucial to learn these techniques and apply them to your business for better results. With this motto, this deep learning course has been designed by Yonsei University.
In this course, you will learn what deep learning is and how to use it with TensorFlow for the advantage of your business.
Who is this course for?
Deep learning for business is a novice-friendly course, so no previous experience in this field is required to join this course. If you are interested in learning about the latest technologies to use them in your business, then this course might meet your needs.
This course is also perfect for those who want to learn the concepts of deep learning and apply them to prepare strategies for certain problems.
Course Highlights
Institution – Yonsei University
Platform – Coursera
Level – Beginner Level
Instructor – Jong-Mong Chung
Duration – 8 Hours Approx
Rating – 4.4
Language Options – 22 Languages Available
Schedule Type – Flexible Scheduling
What you will learn
The deep learning for the business course will teach you how to use deep learning and machine learning techniques in your products and services for the betterment of your business.
Out of three parts of this course, the first part discusses how to design future business strategies using deep learning and machine learning technologies. This session also covers building new products and services using deep learning techniques.
In the second part, you will go a step deeper into these concepts and understand what neural networks are and different types of neural networks, such as convolutional neural networks and recurrent neural networks.
The last part of this course explains the TensorFlow framework. First, you will learn how to use this library and design neural networks using it. This will be helpful for you in building deep learning models for complex business problems.
7. Applied AI with Deep Learning – Offered By IBM
The Applied AI with Deep Learning is an invaluable course by IBM that explains deep learning models, computer vision, natural language processing, time series analysis, etc. This course is a part of the IBM Advanced Data Science Certificate program.
In this course, you will learn the basics of linear algebra and neural networks and get introduced to deep learning open-source libraries such as TensorFlow, Keras, PyTorch, Apache System ML, and DeepLearning4J.
A major part of this course discusses the TensorFlow and Keras libraries, educating you about their features and how to use them to design deep learning models.
Who is this course for?
If you are in the field of AI or machine learning and want to enhance your skills, you can consider joining this course. Programming in Python is necessary, but having coding skills in any language will also work.
In addition to that, students should also understand linear algebra, which will be helpful for understanding the topics better.
If you are in a role that requires an understanding of deep learning concepts and deep learning libraries such as TensorFlow and Keras, then this course will be suitable for you.
Course Highlights
Institution – IBM
Platform – Coursera
Level – Advanced Level
Instructor – Romeo Kienzler, Niketan Pansare, Tom Hanlon, Max Pumperla, Ilja Rasin
Duration – 24 Hours Approx
Rating – 4.4
Language Options – 22 Languages Available
Schedule Type – Flexible Scheduling
What you will learn
First, you will understand what deep learning is and its importance in the field of artificial intelligence. You will encounter various deep learning libraries such as TensorFlow, PyTorch, Keras, DeepLearning4J, and Apache SystemML.
You will learn more details about TensorFlow and Keras libraries and learn how to create deep learning models using them. This course also explains different functions such as image recognition, anomaly detection, time series forecasting, and neural language processing using the Keras library.
The last session of this course discusses how to use Kubernetes, Apache Spark, and GPUs. To learn the concepts of machine learning and data science, you can join the Advanced Data Science with IBM specialized course.
8. Deep Learning – Offered By IIT Ropar
If you are looking for a free-to-join course to learn the fundamental building blocks and algorithms of deep learning, then this course is perfect for you. It is an AICTE-approved course offered by IIT Ropar.
In this course, you will learn what deep learning and neural networks are and how to train them using different algorithms such as Gradient Descent, Nesterov Accelerated Gradient Descent, Adam, RMSProp, and AdaGrad.
Also, this deep learning course covers the basics of deep architectures, which are used for solving various problems in computer vision and natural language processing.
Who is this course for?
Anyone who is interested in deep learning and neural networks and wants to learn more about them can consider joining this course. Even if you just want to explore the field of deep learning for fun, you can enroll in this course.
The prerequisites of this course are prior knowledge of linear algebra and probability theory. Students should also have experience in machine learning to take complete benefits of this course.
Course Highlights
Institution – IIT Ropar
Platform – NPTEL
Level – intermediate/advanced Level
Instructor – Prof. Sudarshan Iyengar, Prof. Padmavati
Duration – 12 weeks (about 3 months) approx.
What you will learn
The course starts with the fundamentals of deep learning and goes deeper into different concepts of deep learning. It discusses neural networks, autoencoders, and different algorithms of deep learning.
In this way, you will learn various aspects of deep learning concepts and understand the subject better. The final part of this course discusses how to use deep architectures to solve various problems in natural language processing and computer vision.
9. Deep Learning with TensorFlow 2.0, Keras and Python – Offered by Codebasics YouTube Channel
This is a beginner-friendly deep learning course available for free for everyone on YouTube platform. It covers the concepts of deep learning and why this is a buzzing field. You will understand the advantages of deep learning and how to use it for your work.
In addition to the basics, this course also covers deep learning libraries such as TensorFlow, Keras, and PyTorch. You will also learn the difference between these libraries in this course.
This course also provides several exercises for each concept so that you can understand them better. You can practice them on your existing system as it doesn’t require any specific system.
Who is this course for?
Any person who is interested in learning new technologies and wants to explore the field of deep learning can join this course. By the way, it is available on YouTube, so anyone can access it at any time.
It teaches deep learning from scratch, but knowing a little about Python, pandas, and machine learning will be helpful for you.
What you will learn
In the beginning, you will learn what deep learning is and get a complete overview of it. Then, you will explore open-source frameworks such as TensorFlow, Keras, and PyTorch and understand the differences among these libraries.
After that, the instructor discusses the concepts of artificial neurons, neural networks, and training of neural networks. In this context, you will learn changing rules, gradient descent, recurrent neural networks, convolutional neural networks, Word2Vec, BERT, etc.
Apart from these, there are several other topics such as Droupout regularization, computer vision, image classification, handling imbalanced datasets, YOLO algorithm, etc.
10. Deep Learning Full Course – Offered by Edureka On YouTube
This is another excellent course on deep learning available on YouTube. It is a 6-hour long video that covers everything related to deep learning and TensorFlow.
You just have to spare 6 hours a day and watch this video to understand the concepts of deep learning and its algorithms. From the basics of deep learning & AI to deep learning algorithms, applications, and interview questions are covered in this course.
Who is this course for?
If you are in search of a complete course on deep learning to understand it and use it in your projects for free, then this course will be perfect for you. It is also suitable for students who are looking to learn deep learning for free.
If you are not in the technical field but want to learn deep learning just for fun, then this course will be suitable for you. Whether you are a novice or a professional, this course will help you master deep learning.
What you will learn
This deep learning course introduces deep learning, machine learning, and artificial intelligence to students. It also explains the applications of deep learning and machine learning.
Then, the course discusses the concept of artificial neurons and neural networks. After that, the course covers 8 deep learning frameworks – TensorFlow, PyTorch, Keras, DeepLearning4J, Chainer, CNTK, Caffe, and MXNet.
The concepts of autoencoders and their types will also be covered. By the end of this course, you will know what deep learning is, how it works, its algorithms, and its applications.
11. Deep Neural Networks with PyTorch – Offered By IBM
Deep Neural Networks with PyTorch is an intermediate-level course on deep learning concepts that discusses how to build deep learning models using the PyTorch framework.
In this course, you will learn about deep learning algorithms, machine learning methods, neural networks, and how to build deep learning applications using PyTorch.
In 10 modules of this course, you will encounter different topics related to deep learning.
Who is this course for?
If you are in the field of AI and machine learning and want to learn about deep learning algorithms and the PyTorch library, then this course will be suitable for you. Having some experience in Python and related fields will be helpful in this course.
If you are looking for a specific course that covers the concepts of deep learning and the PyTorch library, this course will suit your needs.
Course Highlights
Institution – IBM
Platform – Coursera
Level – Intermediate Level
Instructor – Joseph Santarcangelo
Duration – 30 Hours Approx
Rating – 4.4
Language Options – 22 Languages Available
Schedule Type – Flexible Scheduling
What you will learn
The beginning of this course describes the datasets and tensors in PyTorch. Then it explains different topics such as linear regression, logistic regression, softmax regression, neural networks, and different types of neural networks.
Also, this course explains several other deep learning methods. The Deep Neural Networks with PyTorch is a part of the IBM AI Engineering Professional Certificate. If you are interested in learning deeper concepts of AI, you can consider joining this course.
12. Deep Learning Nano Degree Program – Offered By Udacity
Deep Learning Nano Degree Course is a perfect course to learn the fundamentals of deep learning. The course covers both theory and practice related to deep learning and explains how to decide when to use deep learning.
You will learn about algorithms, architecture, goals, and neural networks in deep learning throughout this course. After completing this course, students can create deep learning models and neural networks using PyTorch.
Who is this course for?
Any person who is interested in AI technology and wants to explore the field of deep learning will find this fundamental course on deep learning highly valuable.
To join this course, you just need to know Python, machine learning basics, matrix manipulation, pandas, and linear algebra.
If your job requires you to learn deep learning to apply these techniques to your projects, this course will suit your needs.
Course Highlights
Platform – Udacity
Level – Intermediate Level
Instructor – Giacomo Vianello, Nathan Klarer, Erick Galinkin, Thomas Hossler
Duration – 4 Months Approx
Schedule Type – Flexible Scheduling
What you will learn
The course will start with an introduction to the instructors and an introductory lesson. It explains the basics of deep learning, fundamental algorithms, and neural networks.
In the second part, you will learn about different types of neural networks, such as convolutional neural networks, recurrent neural networks, convolutional generative adversarial neural networks, and their functions.
This course also discusses PyTorch and how to use this platform to create and train deep learning models. The final part of this course will help you shape your career in the field of artificial intelligence by providing appropriate guidance and helping you enhance your LinkedIn Profile.
13. TensorFlow 2 For Deep Learning Specialization – Offered by Imperial College London
TensorFlow 2 for deep learning specialization is a course by Imperial College London to help students create, train, and test deep learning models. Also, it discusses how to predict certain situations using deep learning models.
You will encounter the TensorFlow framework to build deep learning models for several applications. In this context, you will learn how to use TensorFlow APIs and create complex and flexible deep learning models.
Who is this course for?
This specialized deep learning course is suitable for anyone who is in the field of artificial intelligence and machine learning and wants to practice the skills of deep learning with the TensorFlow library.
This course is not for beginners who don’t have experience in any related field. Specifically, students should know Python 3, basic machine learning concepts, basic deep learning concepts, and the concepts of statistics and probability to understand the topics better.
Course Highlights
Institution – Imperial College London
Platform – Coursera
Level – Intermediate Level
Instructor – Dr Kevin Webster
Duration – 3 Months Approx
Rating – 4.8
Language Options – 22 Languages Available
Schedule Type – Flexible Scheduling
What you will learn
The TensorFlow 2 deep learning specialization course has three courses, and the first course discusses concepts of using TensorFlow for deep learning. This includes how to build, train, test, and predict using deep learning models in TensorFlow.
With the tutorials provided in this course, you will get practical experience in using TensorFlow. The next course focuses on modifying deep learning models using the TensorFlow framework.
In the last course, you will learn the concepts of probabilistic approach in deep learning using TensorFlow. This will help you create probabilistic models using the probability library of TensorFlow.
14. Multi-Backened Deep Learning with Keras – Offered by Udacity
This is another valuable course on deep learning for developers or learners who want to use machine learning in their Python projects. While browsing through the course content, I found that this course gives a thorough explanation of the fundamentals of deep learning, which will be really helpful for anyone who wants to start with the basics.
Then, it describes how to use Keras, TensorFlow, JAX, and PyTorch frameworks to build and train machine learning frameworks. In addition to that, it also covers the concepts of neural networks.
Who is this course for?
This is a 3-hour intermediate-level course suitable for any programmer or developer who wants to learn the use of different deep learning frameworks and create multi-backend deep learning abstraction using Keras.
I would recommend this course to students who are interested in the fields of AI and ML and want to explore the field of deep learning with Keras. To join this course, knowing intermediate Python and NumPy is necessary.
Course Highlights
Platform – Udacity
Level – Intermediate Level
Instructor – Jesse Chan
Duration – 3 Hours Approx
Schedule Type – Flexible Scheduling
What you will learn
First, this course will explain the fundamentals of deep learning and multi-backend Keras briefly. Then, it explains how to build and train deep learning models using multi-backend Keras. Also, the course explains how to build image classification models using Keras.
In addition to this, you will also encounter other deep learning frameworks such as TensorFlow, PyTorch, and Jax. Then, the course covers the fundamentals of neural networks and explains how they function. By completing this course, you will learn how to use multi-backend Keras and other deep learning frameworks.
FAQ
Why is it called deep learning?
The name deep learning comes from the way this technology functions. It contains neural networks that have artificial neurons to process and learn from data. So, the number of layers in neural networks is termed as the word deep, and the whole process is called deep learning.
What are CNN and TensorFlow in deep learning?
CNN stands for convolutional neural networks that do pattern recognition and image classification.
On the other hand, TensorFlow is an open-source framework developed by Google that helps create and train neural networks, including convolutional neural networks.
What are the three types of deep learning?
The three types of deep learning are as follows –
Multilayer Perceptrons
Convolutional neural networks
Recurrent neural networks
What is the difference between deep learning and machine learning?
Machine learning is a part of artificial intelligence that creates algorithms and statistical models. It learns from input data and makes decisions without needing specific coding.
On the other hand, deep learning is a subfield of machine learning that uses neural networks to analyze complex patterns and relationships in data. It functions like the human brain.
Final Thoughts
This is it! You now have a list of the best deep learning courses to start your learning. I have included both free and paid courses in this list so that you can easily review them and choose the right one for you.
Also, consider sharing this post with your friends and colleagues to let them explore the field of deep learning.
