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Pro & University Programs

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McCombs School of Business at The University of Texas at Austin

7 months  • Online

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McCombs School of Business at The University of Texas at Austin

10 weeks  • Online

For Grade 8-12 students

Free Deep Learning Courses

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Neural Network in R
star   4.6 6.7K+ learners
1.5 hrs
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Introduction to Neural Networks and Deep Learning
star   4.57 67.8K+ learners
2.5 hrs
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Introduction to Tensorflow and Keras
star   4.54 22.4K+ learners
3.5 hrs
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Multilayer Perceptron
star   4.65 3.4K+ learners
1.5 hrs
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Batch Normalization
star   4.63 1.3K+ learners
1.5 hrs
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Neural Network in R
star   4.6 6.7K+ learners 1.5 hrs
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Introduction to Neural Networks and Deep Learning
star   4.57 67.8K+ learners 2.5 hrs
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Introduction to Tensorflow and Keras
star   4.54 22.4K+ learners 3.5 hrs
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Multilayer Perceptron
star   4.65 3.4K+ learners 1.5 hrs
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Batch Normalization
star   4.63 1.3K+ learners 1.5 hrs

Learner reviews of the Free Deep Learning Courses

Our learners share their experiences of our courses

4.56
70%
23%
5%
1%
1%
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5.0

“This course was so useful for me, and I have learned some features about neural networks.”
The knowledge I gained from this course has been incredibly valuable, and I have learned some features about neural networks that I find fascinating. I now understand the fundamental concepts, such as how neurons function as processing units and how they are interconnected to form complex architectures. The course covered various types of neural networks, including feedforward networks and convolutional neural networks, and their applications in tasks like image recognition and natural language processing.

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5.0

“Neural Network in R is an awesome class”
Neural Network in R is an awesome class. I liked it very much.

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5.0

“Artificial Neural Network & Deep Learning”
Overall, it was a good experience. The Artificial Neural Networks (ANN) and Deep Learning course provided a comprehensive introduction to neural networks and deep learning techniques. The course effectively covered both theoretical foundations and practical applications, making complex concepts like backpropagation, activation functions, and optimization techniques easier to grasp. Hands-on assignments with popular frameworks like TensorFlow and PyTorch were particularly useful for applying knowledge to real-world problems.

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4.0

“Highlight of Your Learning Experience in Introduction to Neural Networks and Deep Learning”
I enjoyed the comprehensive overview of neural networks and their applications in various fields. The hands-on exercises helped solidify my understanding of concepts like activation functions and backpropagation. The practical examples showcased how deep learning can be applied to solve real-world problems, such as image and speech recognition. Additionally, the discussions on different neural network architectures provided valuable insights into their strengths and weaknesses, making the learning experience both engaging and informative.

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5.0

“Introduction to Neural Networks and Deep Learning”
The course "Introduction to Neural Networks and Deep Learning" offers several appealing aspects: 1. Clear Conceptual Foundation: It starts with fundamental concepts, making it easier for beginners to understand how neural networks work, including perceptrons, layers, and how learning happens through backpropagation. 2. Hands-on Learning: The course often includes practical examples and coding exercises using Python and libraries like TensorFlow or Keras, allowing learners to implement networks and immediately see results.

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5.0

“Learning Outcomes from This Course Were Very Helpful”
I really enjoyed this course as it really helped me with new learning of TensorFlow and Keras to kickstart my journey in artificial intelligence.

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5.0

“I Found the Course Content to Be Well-Structured and Easy to Follow”
I particularly appreciated the way the course content was broken down into manageable modules. Each module built upon the previous one, making it easy to follow the progression of the material.

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5.0

“Videos Are Easy to Understand and Very Useful to Cover Extra Topics”
The 'Introduction to TensorFlow and Keras' course provided a clear and practical foundation to get started with deep learning. The explanations are easy to follow, and the hands-on examples made complex concepts like neural networks, tensors, and model building much more accessible.

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5.0

“Very Informative and Interesting Topic, Well Explained and Presented”
It was easy to follow the course. I really enjoyed the in-depth practical demos and felt it was easy to follow along and try it out for myself!

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4.0

“It Was a Good Learning Experience for Me”
I liked the way they kept it simple to teach us, and the way they visualized the concepts was good.

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Learn Deep Learning Online & Gain Certificates

What is Deep Learning?

Deep learning is a type of Artificial Intelligence (AI) that employs algorithms and techniques inspired by the arrangement and function of the human brain. It is based on a type of Machine Learning, where computers learn from data without relying on predetermined rules. It can be used to recognize objects, classify images, and understand natural language. Deep Learning (DL) revolutionizes how businesses, organizations, and industries work. It is becoming increasingly important to the current industry because of its potential to improve efficiency, reduce costs, and increase customer experience. Get familiar with this in-demand industry skill through Great Learning’s free Deep Learning courses that cover basic to advanced concepts.

 

Why Learn Deep Learning?

In the past decade, there has been a tremendous increase in the use of Deep Learning as Artificial Intelligence (AI) technology. This technology has been applied widely in various industrial fields, including medical diagnostics, autonomous driving, media & entertainment, and natural language processing. 

 

Deep Learning is a subset of Machine Learning, which is the capability of a computer system to learn without being explicitly programmed. It involves the use of artificial neural networks which emulate the workings of the human brain. In Deep Learning, the neural network is used to identify patterns in data and then learn to make decisions based on these patterns. 

 

In recent years, there has been an upsurge in demand for Deep Learning expertise in various industries. The technology is being used in industries ranging from medical diagnostics and autonomous driving to media & entertainment. 


One of the most common applications of Deep Learning is in medical diagnostics, which is used to identify different diseases by analyzing images or CT Scans. Deep Learning systems are also used in robotic surgery to help the robot determine the best route to take for a procedure. Similarly, Deep Learning is used in autonomous driving to detect objects such as pedestrians and vehicles. 

 

In the media & entertainment field, Deep Learning is used for video object segmentation and object recognition, as well as facial recognition and voice recognition. For example, Deep Learning can detect objects in a video, such as a person's face or clothes, and recognize movements or gestures, such as the action of raising a hand.


Deep Learning is applied to analyze and understand written or spoken language in Natural Language Processing(NLP). This technology can be used to detect sentiment in text, identify topics of conversations, and translate between languages. 


Learning Deep Learning has become essential for professionals in the various industries that are making use of it. With the current demand for Deep Learning expertise, gaining knowledge in this field has become essential for professionals who wish to stay ahead of the competition. It can be an invaluable asset for those who want to work in the cutting-edge field of AI technology.


Benefits of Learning Deep Learning
Deep learning has evolved into one of the most talked about topics in technology as more and more people are realizing the potential of this powerful machine learning technology. It has revolutionized the way computers can learn by enabling them to learn from large amounts of data. This has made it a valuable tool for businesses, researchers, and scientists. Here are some of the key benefits of learning Deep Learning.


1. Greater Prediction Accuracy
Deep learning algorithms are able to learn complex patterns and make predictions that are much more accurate than traditional Machine Learning algorithms. This improved accuracy is beneficial in many areas, such as medicine, finance, and robotics, where decision-making needs to be as precise as possible.

 

2. Increased Efficiency
Deep learning algorithms use advanced techniques such as natural language processing, convolutional neural networks, and recurrent neural networks to quickly process large sets of data. This results in faster and better predictions, saving businesses time and money.

 

3. Increased Automation
Machine Learning algorithms have been used for some time to automate tasks that would otherwise have to be done manually. Deep learning increases this automation by taking the capability a step further, allowing machines to understand complex data patterns and make decisions with minimal human intervention.

 

4. Faster Development
Deep learning algorithms are able to quickly develop models based on data that is provided. This makes the development of complex models much faster and more efficient than with traditional Machine Learning algorithms.

 

5. Improved Understanding 

Deep learning algorithms are able to understand data in greater depth than traditional Machine Learning algorithms. This improved understanding helps businesses and scientists to understand the data better and make better decisions.

 

These are only a few of the benefits of learning Deep Learning. As businesses and scientists continue to realize its potential, its use will only increase. By learning Deep Learning, you will be well-placed to take advantage of this rapidly advancing technology. Advance in your career by enrolling in Great Learning's Artificial Intelligence and Machine Learning Program by the University of Texas at Austin’s McCombs School of Business and gain in-demand industry skills along with the certificate of course completion.

Meet your faculty

Meet industry experts who will teach you relevant skills in artificial intelligence

instructor img

Sunil Kumar Vuppala

Director-Data Science
  • IIT Roorkee, IIM Ahmedabad alumnus with 20+ years of experience
  • Director at Ericsson specializing in AI, ML, and analytics

Frequently Asked Questions

How can I learn the Deep Learning Course for free?

Great Learning offers free Deep Learning courses, which address basic to advanced concepts. Enroll in courses that suit your career goals through the pool of courses and earn Deep Learning course completion certificates.
 

Can I learn Deep Learning Courses on my own?

With the support of online learning platforms, it is now possible to learn concepts on your own. Great Learning Academy is a platform that provides free Deep Learning courses where learners can learn at their own pace.  
 

How long does it take to complete these Deep Learning courses?

These free Deep Learning courses offered by Great Learning Academy contain self-paced videos allowing learners to learn crucial Deep Learning skills at their convenience.
 

Will I have lifetime access to these Deep Learning courses with certificates?

Yes. You will have lifelong access to these free Deep Learning courses Great Learning Academy offers.

What are my next learning options after these Deep Learning courses?

You can enroll in Great Learning's Artificial Intelligence and Machine Learning Program by the University of Texas at Austin’s McCombs School of Business, which will help you gain advanced AIML skills in demand in industries. Complete the course to earn a certificate of course completion.

 

Is it worth learning Deep Learning?

Yes, Deep Learning can be very powerful. It is a rapidly growing field, and there are many potential applications for it. It can solve complex problems, from image recognition and speech recognition to language understanding and natural language processing. It can also be used in self-driving cars, robotics, and other autonomous applications.
 

Why is Deep Learning so popular?

Deep Learning has become increasingly popular in recent years due to its potential to solve complex problems, such as computer vision and natural language processing, more accurately and effectively than traditional Machine Learning methods. 
It is also popular because of its ability to quickly and accurately process large amounts of data, identify patterns, and extract valuable insights that can be used for decision-making. Furthermore, developing new algorithms and hardware tailored explicitly for Deep Learning has enabled the technology to become even more powerful and easy to use.
 

Will I get certificates after completing these free Deep Learning courses?

You will be awarded free Deep Learning certificates after the completion of your enrolled Deep Learning free courses.
 

What knowledge and skills will I gain upon completing these free Deep Learning courses?

You will learn about Tensorflow, Kera, neural networks, backpropagation, multilayer perceptron, and more through these free Deep Learning courses.
 

How much do these Deep Learning courses cost?

These Deep Learning courses are provided by Great Learning Academy for free, allowing any learner to learn Deep Learning and gain crucial skills.
 

Who are eligible to take these free Deep Learning courses?

Learners, from freshers to working professionals who wish to learn the latest skills in Deep Learning can enroll in these free Deep Learning courses and earn certificates of course completion.
 

What are the steps to enroll in these free Deep Learning courses?

Choose the free Deep Learning courses you are looking for and click on the "Enroll Now" button to start learning Deep Learning.
 

Why take Deep Learning courses from Great Learning Academy?

Great Learning Academy is the proactive initiative by Great Learning, the leading e-Learning platform, to offer free industry-relevant courses. Free Deep Learning courses contain courses ranging from beginner-level to advanced-level to help learners choose the best fit for them.

 

What jobs demand you learn Deep Learning?

There are several jobs that require you to learn Deep Learning, including:

  • AI Research Scientist
  • Machine Learning Engineer
  • Data Scientist
  • Computer Vision Engineer
  • Image Recognition Specialist
  • Autonomous Vehicle Engineer
  • Robotics Engineer
  • Natural Language Processing (NLP) Engineer
  • Speech Recognition Scientist