10 Best Deep Learning Courses on Udemy

Deep learning is a type of Artificial Intelligence (AI) and Machine Learning process that teaches computers to process data in a manner inspired by the human brain. Deep learning algorithms can identify complex patterns in images, text, sounds, and other data, resulting in accurate insights and predictions. Deep learning algorithms can be used to automate tasks that would normally need human intellect, such as visual description or audio transcription. 

You can learn about Deep Learning by watching online videos and tutorials. An online deep learning course allows you to: Learn the principles of deep learning, including the various types of neural networks for both supervised and unsupervised learning, and create several forms of deep architectures, such as convolutional networks, recurrent networks, and autoencoders.

In this article, I am reviewing the 10 best Deep Learning Courses on Udemy. I have picked these courses based on my personal experience and for different types of learners, delving into what makes each course exceptional and providing you with insights to make an informed decision. 

Why Pursue a Deep Learning Course on Udemy?

Udemy stands out as a leading online learning platform, providing affordable and accessible education. With a vast library of courses, Udemy offers a range of Deep Learning courses catering to different skill levels. The platform’s intuitive interface and the ability to learn at your own pace make it an ideal choice for anyone eager to master Deep Learning techniques.

1. A Deep Understanding of Deep Learning (with Python intro)

A deep understanding of deep learning (with Python intro) course provides a deep dive into deep learning. You will obtain flexible, foundational, and long-term skills in deep learning. You will also have a thorough understanding of the core concepts behind deep learning, which eventually allows you to study new topics and trends as they develop in the future. The course also provides clear and understandable explanations of deep learning principles such as transfer learning, generative modelling, convolutional neural networks, feedforward networks, generative adversarial networks (GAN), and more.

Who would benefit most from taking this course?

For deep learning courses and Machine Learning enthusiasts, this is a must-pursue course. The course will also suit anyone interested in mechanisms of AI (artificial intelligence), Data scientists who want to expand their library of skills, and aspiring data scientists.

What do I like in the course?

• The course is the most comprehensive and understandable Deep Learning course on Udemy.
• This is a comprehensive, well-explained and illustrative course
• The course also provides you with an Active Q&A forum where you can ask questions, get feedback, and contribute to the community.

What could have been better?

• The presentation of the course content can be improved.
• The course would have been better to have some quizzes, assignments, and tests that could help in the self-assessment of the students.
• The course seems to cover a variety of topics, but it only skims the surface. Some explanations are shallow and vague.

2. Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024] course has been designed by a Data Scientist and Machine Learning specialist. So candidates can either complete the entire course from beginning to end or can directly dive right into any individual component and learn what they need for their career right now. The course content also includes numerous practical exercises based on real-life case studies. So, not only will you study the theory, but you’ll also learn plenty of hands-on experience developing your models. Last but not least, this course provides Python and R code templates that you may download and use in your projects.

Who would benefit most from taking this course?

The course is for students who have at least high school knowledge in mathematics and for those who want to start learning Machine Learning, Any intermediate level people who know the basics of machine learning,  who want to learn more about it and explore all the different fields of Machine Learning Any students in college who want to start a career in Data Science. Any data analysts who want to level up in Machine Learning and Anyone interested in Machine Learning.

What do I like in the course?

• The course content had great conceptual coverage of Machine Learning topics and tools.
• The course content also provides a lot of hands-on practice.
• The course content is informative and knowledgeable and helps you to improve your knowledge in the field of Deep Learning.

What could have been better?

• The Python part is too repetitive.
• The course lacks a lot of depth on the technical/mathematical side.
• The course doesn’t have enough mathematical explanation. Very basic course with good code examples.

3. The Data Science Course: Complete Data Science Bootcamp 2024

‘The Data Science Course: Complete Data Science Bootcamp 2024 course’ equips you with all of the tools you’ll need to become a data scientist. In the course, you will learn how to pre-process data as well as the mathematics behind Machine Learning. After completing the course, you will be able to fill out your résumé with the most sought-after data science skills: Statistical analysis, and Python programming using NumPy, pandas, matplotlib, and Seaborn. Advanced statistical analysis with Tableau, machine learning with stats models and sci-kit-learn, deep learning with TensorFlow, and so on.

Who would benefit most from taking this course?

This course is highly recommended for students who want to become a Data Scientist or if you want to learn about the field of Data science and Deep learning. The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills from the beginning. This course also suits beginners who are looking to expert their career in the field of Data Science.

What do I like in the course?

• The course content includes excellent presentations and explanations.
• The course provides a diverse array of lessons directly relevant to the field of Data Science.

What could have been better?

• Few topics of the course content could dedicate a little more time.
• The course lacks some of the crucial concepts of Deep learning.

4. Deep Learning: Recurrent Neural Networks in Python

Deep Learning: Recurrent Neural Networks in Python Course, will help students master the fundamentals of machine learning, neurons, and neural networks for classification and regression. Students will also learn how to model sequence and time series data. This course focuses on “how to build and understand” rather than “how to use”. Anyone can learn to use an API in 15 minutes by reading the documentation. It is not about “remembering facts”, but about “seeing for yourself” through exploration. It will teach you how to visualize the internal workings of the model. If you want more than a casual glance at machine learning models.

Who would benefit most from taking this course?

The course will benefit students, professionals, and anyone else interested in Deep Learning, Time Series Forecasting, Sequence Data, or NLP, and also, Software Engineers and Data Scientists who want to level up their careers.

What do I like in the course?

• This course contains a lot of information that is quite useful for sequence modelling, time series forecasting, and NLP. 
• The coding practices help to understand how RNN works in the back.
• The typing of the code out and talking it through in the video helps quite a bit.

What could have been better?

• The theoretical part of the course (GRU and LSTM) is very poor.
• There is a lot said in the videos but not included in the slides.

5. [2023] Machine Learning and Deep Learning Bootcamp in Python

[2023] Machine Learning and Deep Learning Bootcamp in Python course covers the core ideas of machine learning, including deep learning, reinforcement learning, and machine learning. These themes are becoming increasingly popular since these learning algorithms may be applied in a variety of disciplines, including software engineering and investment banking. In each part, students will learn about the theoretical foundations of all of these algorithms before collaborating to solve these challenges. Students will learn how to use Python with SkLearn, Keras, and TensorFlow during this course.

Who would benefit most from taking this course?

This course is specially meant for newbies who are not familiar with machine learning, deep learning, computer vision, and reinforcement learning and want to get a deep dive into the field of Deep Learning. or students looking for a quick revision of topics.

What do I like in the course?

• The course lectures were clear and easy to understand.
• The course contains very excellent and clear explanations of the topics.
• The course gives a good overview of OCR and Face detection algorithms.

What could have been better?

• The video quality can be improved.
• Some more real-time examples can be added to the course content.

6. Complete Tensorflow 2 and Keras Deep Learning Bootcamp

Complete Tensorflow 2 and Keras Deep Learning Bootcamp course will teach students how to use Google’s latest TensorFlow 2 framework to build artificial neural networks for deep learning. The course aims to provide you with an easy-to-understand introduction to the complexity of Google’s TensorFlow 2 framework. The course balances theory and practice, with complete jupyter notebook coding guides and easily accessible slides and notes. The course also provides lots of exercises to put your newly acquired skills to the test.

Who would benefit most from taking this course?

The course is specially designed for Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence. The course also suits the Employees of major companies all over the world, including Airbnb, eBay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google.

What do I like in the course?

• The course covers from the basics to the advanced details about the contents required for Deep Learning.
• This course provides a good overview of how to program in TF2 + Keras using Python.

What could have been better?

• A few of the course theory papers are difficult and demanding.
• It would be good to have more explanation for some complicated concepts related to CNN, NLP, GAN as well as the code.

7. NLP – Natural Language Processing with Python

NLP – Natural Language Processing with Python course will teach the students everything they need to know to become a world-class NLP practitioner using Python. The course begins with the fundamentals, including how to open and deal with text and PDF files in Python, as well as how to utilize regular expressions to search for specific patterns inside text files. Students will also understand vocabulary matching with Spacy.

Who would benefit most from taking this course?

The course is ideal for Python developers interested in learning how to use Natural Language Processing. The course will also suit candidates having prior knowledge of Python and who are willing to learn about NLP.

What do I like in the course?

• The course content contains in-depth and great material.
• It is an overall ideal course covering the key tools in NLP.
• The course content explanations are so clear that it is easily understandable.

What could have been better?

• Real-life and real-time example citations can make the course feasible and lucid for students.
• There are sections in the course that need to be updated as per recent developments.

8. Artificial Intelligence with Machine Learning, Deep Learning

“Artificial Intelligence with Machine Learning, Deep Learning” is an easy course on Python programming and machine learning. In the course, students will receive down-to-earth explanations and projects. In this course, you will study machine learning step by step. They made it simple and easy with exercises, challenges, and numerous real-life examples. Throughout the course, students will learn about artificial intelligence and the principles of machine learning, as well as using advanced machine learning Python algorithms.

Who would benefit most from taking this course?

The course is for anyone who wants to start learning about “Machine Learning”, and anyone who needs a complete guide on how to start and continue their career with machine learning. The course also helps people who want to learn artificial intelligence with machine learning, deep learning, transfer learning, supervised learning, unsupervised machine learning methods, and Artificial Intelligence.

What do I like in the course?

• It is an ideal course to learn Python Machine Learning and Python Deep Learning. The explanation of the course content is simple and clear.
• The course curriculum consists of detailed pedagogy which is best suited for beginners and intermediates
• The narration about the course content is very fluent and understandable.

What could have been better?

• The course content could have more illustrative examples that are necessary for better understanding.
• The course contains a lot of terminologies thrown around but very few explanations and examples to make learning possible.

9. Machine Learning & Deep Learning: Python Practical Hands-on

‘Machine Learning & Deep Learning: Python Practical Hands-on Course’ will help students learn about the fundamentals of machine learning and deep learning through live practice interviews with experts. They will also become familiar with concepts such as anomaly detection algorithms, efficient feature engineering, and data pre-processing. Students will also work with multiple data sets and build algorithms in Kaggle Cloud.

Who would benefit most from taking this course?

The course will help the students, whether it is Data Science Beginners, Researchers & PhD Scholars, and also it will help the professionals to level up their skills.

What do I like in the course?

• As a beginner in Data Science, I found the course content easy to understand & adapt.
• The content of the course is very well organised and presented simply.

What could have been better?

• The PPT of course content is not at all updated and I feel it is outdated.
• The audio in several course pages froze and became inaudible.

10. The Complete Neural Networks Bootcamp: Theory, Applications

‘The Complete Neural Networks Bootcamp: Theory, Applications’ course is divided into a total of 27 sections. Each section of the course provides a detailed introduction to Deep Learning and Neural Networks. The theories are discussed thoroughly and in a nice manner. Following that, we’ll have a hands-on session where we’ll learn how to write Neural Networks in PyTorch, a very complex and powerful deep-learning framework.

Who would benefit most from taking this course?

The course is ideal for students interested in learning Neural Networks and Deep Learning in detail. However, they should have some basic Python knowledge and know up to high school-level Math.

What do I like in the course?

• The course provided a good coverage of different applications of Artificial Intelligence. 
• The instructor covers every concept in depth.
• The teaching style is engaging.

What could have been better?

• The sound quality was inconsistent.
• Some theoretical parts are difficult to understand because of the sound quality.