Free Online Deep Learning Courses and
Certifications 2025
Deep Learning is a branch of Artificial Intelligence that uses algorithms to model high-level abstractions in data. It is used in various fields such as computer vision, natural language processing, and robotics. People with knowledge in mathematics, computer science, and statistics are suitable for courses related to Deep Learning.
Popular Courses
Explore the power of deep learning and transform your teaching with this innovative course. Join now and become an education leader in the 21st century.
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This comprehensive online tutorial is perfect for anyone looking to learn Deep Learning and Convolutional Neural Network using Python and Keras. From beginners to professionals, this course offers a comprehensive guide to understanding the fundamentals of Deep Learning and Neural Networks. Sign up now and start your journey to becoming an expert in this field!
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This course is perfect for anyone looking to learn the fundamentals of deep learning and machine learning with Keras and Python. It covers the basics of the Keras library and how to use it to create powerful deep learning models. With practical examples and hands-on activities, this course will help you gain the skills and knowledge you need to start building your own deep learning models. Sign up now and start your journey into the world of deep learning and machine learning with Keras and Python.
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Explore the essentials of Deep Learning for algorithmic trading using Python
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This course is perfect for beginners who want to learn CATIA part design from scratch. It covers all the fundamentals of CATIA from A to Z. You will learn how to create 3D models, use the tools and features of CATIA, and apply the best practices for part design. By the end of the course, you will be able to design 3D models with confidence.
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This Deep Learning program is the perfect opportunity to join the next generation of AI-powered talent. You will learn cutting-edge topics such as neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. This program will help you define a highly beneficial future for the world. Don't miss out on this chance to become a leader in the field of deep learning.
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This course is perfect for those who want to take their deep learning and NLP skills to the next level. It covers advanced deep learning concepts and provides hands-on experience in building advanced deep learning and Natural Language Processing (NLP) projects. You will be able to use deep learning algorithms which are widely used in industry and be introduced to the most commonly used machine learning tools such as NumPy, Matplotlib, scikit-learn, Tensorflow and others. Click now to start building advanced deep learning and NLP projects!
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This online course provides an introduction to deep learning, teaching students how to build and apply their own deep neural networks to challenges such as image classification, prediction, and model deployment.
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This course provides an in-depth exploration of unsupervised deep learning techniques. It covers the theory behind principal components analysis (PCA), t-SNE, autoencoders, restricted Boltzmann machines (RBMs) and deep belief networks (DBNs). Through hands-on coding exercises, you will learn how to write the code for PCA, t-SNE, autoencoders, and RBMs in Theano and Tensorflow. You will also gain an understanding of the limitations of PCA and t-SNE, and how stacked autoencoders are used in deep learning.
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This course is perfect for anyone looking to learn the fundamentals of deep learning and how to implement them in PyTorch. With this course, you will gain a practical understanding of deep learning concepts and be able to effectively wield PyTorch, a Python-first framework, to build your deep learning projects. Don't miss out on this opportunity to become a deep learning expert!
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This specialization in Deep Learning for Healthcare is designed for those interested in applying machine learning to medical applications. It covers topics such as health data analysis, different types of neural networks, and training and application of neural networks on real-world medical scenarios. Whether you are a machine learning expert or a medical professional, this specialization will provide you with the knowledge and skills to make a difference in healthcare.
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This tutorial provides an introduction to modern deep convolutional neural networks and their implementation with PyTorch. It covers advanced deep learning and representation learning techniques for image recognition.
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This course provides an introduction to deep learning, a powerful set of algorithms used in machine learning. It covers the fundamentals of deep learning, such as gradient descent and backpropagation, as well as design constructs of neural networks and how to optimize them for accuracy and robustness. The course combines theory and practice, with PyTorch code to reinforce both. It is suitable for anyone interested in learning the fundamentals of deep learning.
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This course provides an introduction to the exciting world of deep learning and neural networks, giving learners the tools to become proficient in AI. With hands-on exercises and real-world examples, learners can gain the skills necessary to become an AI expert.
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MIT's Introduction to Deep Learning 6.S191 is a comprehensive course for those interested in learning the foundations of deep learning. Led by Alexander Amini, the course covers topics such as the perceptron, neural networks, loss functions, training and gradient descent, backpropagation, setting the learning rate, batched gradient descent, and regularization. With slides and lab materials available at http://introtodeeplearning.com/, this course is perfect for those looking to gain a better understanding of deep learning. Subscribe to stay up to date with new lectures or follow @MITDeepLearning on Twitter and Instagram to stay connected!
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Discover the fundamentals of Blockchain and Deep Learning: Future of AI
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Deep Learning Courses
Career Trends
Career Prospects
| Average Salary | Position Overview
|
Deep Learning Engineer | $187,585 per year
| Deep learning engineers develop and maintain machine learning models, working in conjunction with a team of data scientists, software engineers, and other experts. They are tasked with building new AI-powered systems capable of performing advanced functions, such as image recognition and natural language processing. |
Computer Vision Engineer | $162, 028 per year | As a Computer Vision Engineer, your responsibilities include developing, testing, debugging, deploying, and maintaining computer vision algorithms and hardware for various environments. You will also be tasked with developing automated vision algorithms, particularly for use with robots and autonomous hardware systems. |
Software Engineer | $166,416 per year | Software engineering is a field within computer science that focuses on the process of designing, developing, testing, and maintaining software applications. This involves using principles of engineering and expertise in programming languages to create effective and efficient software solutions for end-users. |
Data Scientist | $186,585 per year | Data scientists are professionals who identify the important questions to be answered and locate the relevant data sources. They possess both business knowledge and analytical expertise, along with the ability to extract, clean, and present data effectively. Companies rely on data scientists to handle and analyze large amounts of unstructured data. |
Research Engineer | $138,695 per year | As a Research Engineer, your role involves building prototypes, products, and systems for testing purposes. You will design testing procedures and coordinate with others to identify problems and solutions. You will also collaborate on standards for procedures and component requirements, and coordinate and communicate work efforts. |
Educational Paths
1. Online courses: Platforms like Coursera, Udemy, and edX offer online courses on Deep Learning from top universities and industry experts.
2. Certifications: Professional certifications like TensorFlow Developer Certificate or NVIDIA Deep Learning Institute Certification can help you demonstrate your skills and knowledge in Deep Learning.
3. Master's degree: Pursuing a Master's degree in Computer Science or Artificial Intelligence with a focus on Deep Learning can provide in-depth knowledge and hands-on experience.
4. PhD: For those interested in research and academia, pursuing a PhD in Computer Science or Machine Learning can be a good option.
5. Bootcamps: Bootcamps like Springboard or Metis offer intensive training programs in Deep Learning, often with a focus on practical applications and job placement assistance.
Frequently Asked Questions and Answers
Q1: What Deep Learning courses can I find on OeClass?
On this page, we have collected free or certified 85 Deep Learning online courses from various platforms. The list currently only displays up to 50 items. If you have other needs, please contact us.
Q2: Can I learn Deep Learning for free?
Yes, If you don’t know Deep Learning, we recommend that you try free online courses, some of which offer certification (please refer to the latest list on the webpage as the standard). Wish you a good online learning experience!
