8 Best Advanced Deep Learning Courses Online You Must Know in 2024

Are you looking for the Best Advanced Deep Learning Courses?… If yes, this article is for you. In this article, you will find the 8 Best Advanced Deep Learning Courses. So give your few minutes and find out the Best Advanced Deep Learning Courses for you.

Now without any further ado, let’s get started-

Best Advanced Deep Learning Courses

1. Deep Learning Specialization– Coursera

Rating- 4.8/5

Time to Complete- 4 months ( If you spend 5 hours per week)

Provider- deeplearning.ai

This course is taught by Andrew Ng, the co-founder of Coursera and an Adjunct Professor of Computer Science at Stanford University. This is a Specialization Program that contains 5 courses.

This is a Specialization Program that contains 5 courses. Python and TensorFlow are used in this specialization program for Neural networks. This is the best follow-up to Andrew Ng’s Machine Learning Course.

This Deep Learning Specialization is an advanced course series for those who want to learn Deep Learning and Neural networks. More than 250,000 learners from all over the globe have already enrolled in this Specialization Program.

Courses Include-

1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models

Extra Benefits-

• You will get a Shareable Certificate. 
• You will get a chance to work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.
• Along with that, you will get a chance to hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.

Who Should Enroll?

NOTE- This Specialization Program is not for Beginners. This program is suitable for-

• Those who have some basic understanding of Python.
• And those who has a basic knowledge of Linear Algebra and Machine Learning.

Interested to Enroll?

If yes, then check here-  Deep Learning Specialization.

2. Deep Learning– Udacity

Rating- 4.7/5

Time to Complete- 4 months (If you spend 12 hours per week)

This Nano-Degree program from Udacity will give you a complete understanding of Deep Learning. In this program, you will build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation.

You will also learn how to implement gradient descent and backpropagation using NumPy matrix multiplication, how to prevent overfitting of training data and minimize the error of a network, how to define and train neural networks for sentiment analysis, etc.

This Nanodegree program will also teach you how to use Amazon’s GPUs to train neural networks faster. The instructor Sebastian Thrun will explain about detecting skin cancer with CNN. There are 5 courses in this Nanodegree program. Now let’s see the details of the courses-

Courses Include-

1. Neural Networks
2. Convolutional Neural Networks
3. Recurrent Neural Networks
4. Generative Adversarial Networks
5. Updating a Model

Extra Benefits-

• You will get a chance to work on Real-world projects.
• You will get Technical mentor support.
• Along with that, you will get Resume services, Github review, LinkedIn profile review.

Who Should Enroll?

• Those who have intermediate-level Python programming knowledge and experience with NumPy and pandas.
• And those who have math knowledge, including- algebra and some calculus.

Interested to Enroll?

If yes, then check it out here– Deep Learning (Udacity)

3. Generative Adversarial Networks (GANs) Specialization– Coursera

Rating- 4.7/5

Time to Complete- 3 months ( If you spend 8 hours per week)

Provider- deeplearning.ai

A generative Adversarial Network (GAN) is a powerful algorithm of Deep Learning. Generative Adversarial Network is used in Image Generation, Video Generation, and Audio Generation. In short, GAN is a Robot Artist, who can create any kind of art perfectly.

And in this Generative Adversarial Networks (GANs) Specialization, you will learn how to build basic GANs using PyTorch and advanced DCGANs using convolutional layers.

You will use GANs for data augmentation and privacy preservation, survey GANs applications, and examine and build Pix2Pix and CycleGAN for image translation.

There are 3 courses in this Specialization program where you will gain hands-on experience in GANs. Now, let’s see all the 3 courses of this Specialization Program-

Courses Include-

1. Build Basic Generative Adversarial Networks (GANs)
2. Build Better Generative Adversarial Networks (GANs)
3. Apply Generative Adversarial Networks (GANs)

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments.

Who Should Enroll?

• Those who have a working knowledge of AI, deep learning, and convolutional neural networks. And have intermediate Python skills plus familiarity with any deep learning framework (TensorFlow, Keras, or PyTorch).
• You should also proficient in basic calculus, linear algebra, and statistics.

Interested to Enroll?

If yes, then check it out here–Generative Adversarial Networks (GANs) Specialization

4. Become a Deep Reinforcement Learning Expert– Udacity

Rating- 4.6/5

Time to Complete- 4 months (If you spend 10 hours per week)

This is an advanced Nanodegree Program. In this program, you will learn the fundamentals of reinforcement learning by writing your own implementations of many classical solution methods.

You will apply deep learning architectures to reinforcement learning tasks. And train your own agent that navigates a virtual world from sensory data.

Then you will learn the theory behind evolutionary algorithms and policy-gradient methods. And design your own algorithm to train a simulated robotic arm to reach target locations.

At the end of this Nanodegree program, you will learn how to apply reinforcement learning methods to applications that involve multiple, interacting agents.

There are 4 courses in this program.

Courses Include-

1. Foundations of Reinforcement Learning
2. Value-Based Methods
3. Policy-Based Methods
4. Multi-Agent Reinforcement Learning

Extra Benefits-

• You will get a chance to work on Real-world projects.
• You will get Technical mentor support.
• Along with that, you will get Resume services, Github review, LinkedIn profile review.

Who Should Enroll?

• Those who have Intermediate to advanced Python experience and intermediate statistics and machine learning knowledge.

Interested to Enroll?

If yes, then check it out here– Become a Deep Reinforcement Learning Expert

5. TensorFlow 2 for Deep Learning Specialization– Coursera

Rating- 4.9/5

Time to Complete- 4 Months( If you spend 7 hours per week)

In this specialization program, there are 3 courses where you will gain fundamental concepts to build, train, evaluate, and make predictions from deep learning models.

Along with this, you will learn TensorFlow to develop fully-customized deep learning models and workflows for any application. You will also learn TensorFlow APIs to include sequence models.

In the last course, you will learn how to build probabilistic models with TensorFlow and how to use the TensorFlow Probability library. Now, let’s see all the 3 courses of this Specialization Program-

Courses Include-

1. Getting started with TensorFlow 2
2. Customizing your models with TensorFlow 2
3. Probabilistic Deep Learning with TensorFlow 2

Extra Benefits-

• You will get a Shareable Certificate and Course Certificates upon completion.
• Along with that, you will get Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments.

Who Should Enroll?

• Those who are familiar with Python 3, machine learning concepts, Probability and statistics, and basics of deep learning.

Interested to Enroll?

If yes, then check it out here– TensorFlow 2 for Deep Learning Specialization

6. Deep Learning: Convolutional Neural Networks in Python– Udemy

Rating- 4.7/5

Time to Complete- 12 hours

This course is focused on Convolutional Neural networks (CNN), a powerful algorithm of Deep Learning. In this course, you will learn the fundamentals of CNN and how to build a CNN using Tensorflow 2. 

This course will also teach how to do image classification in Tensorflow 2 and how to use Embeddings in Tensorflow 2 for NLP.

Throughout this course, you will work in Numpy, Matplotlib, and Tensorflow libraries. This course is based on a practical approach.

Extra Benefits-

• You will get a Certificate of Completion.
• Along with that, you will get lifetime access to the course material.

Who Should Enroll?

• Those who have understanding of basic math (taking derivatives, matrix arithmetic, probability).

Interested to Enroll?

If yes, then check it out here– Deep Learning: Convolutional Neural Networks in Python

7. Deep Learning in Python– Datacamp

Time to Complete- 20 hours

Type- Skill Track

This is a skill track offered by Datacamp. In this skill track, there are 5 courses. In these 5 courses, you will learn the fundamentals of neural networks, how to use deep learning with Keras 2.0, TensorFlow 2.4, and PyTorch.

You will also learn about convolutional neural networks and how to use them to build much more powerful models which give more accurate results. Throughout these courses, you will learn how to accurately predict housing prices, credit card borrower defaults, and images of sign language gestures.

In the last course, you will learn some advanced topics such as category embeddings and multiple-output networks. 

Who Should Enroll?

• Those who have previous knowledge in Machine Learning and Python Programming.

Interested to Enroll?

If yes, then check it out here– Deep Learning in Python

8. Reinforcement Learning– Udacity

Time to Complete- 4 Months

This is an advanced-level free deep learning course on Udacity. This course is good for you if you have intermediate-level machine learning knowledge and you want to engage with the theoretical perspective of machine learning.

In this course, you will get a chance to learn from two of the foremost experts in this field of research, Profs. Charles Isbell and Michael Littman. But before taking this course, you should know Java programming and you are familiar with machine learning algorithms.

Interested to Enroll?

If yes, then check it out here– Reinforcement Learning

And here the list ends. I hope these Best Advanced Deep Learning Courses will definitely help you. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.

Conclusion

In this article, I tried to cover the 8 Best Advanced Deep Learning Courses. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

Thank YOU!

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Though of the Day…

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– Henry Ford