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Free Online Neural Networks Courses and Certifications 2024

Neural Networks: Neural networks are a type of artificial intelligence that is modeled after the human brain. They are used for tasks such as image and speech recognition, natural language processing, and machine translation. Neural networks are a rapidly developing field with many applications in science and technology.

Popular Courses

MIT 6S191: Recurrent Neural Networks Transformers and Attention
MIT 6S191: Recurrent Neural Networks Transformers and Attention
This MIT 6S191 course provides an introduction to deep learning with a focus on recurrent neural networks, transformers, and attention. Lecturer Ava Amini will cover topics such as sequence modeling, neurons with recurrence, RNNs from scratch, design criteria for sequential modeling, backpropagation through time, long short term memory (LSTM), RNN applications, attention fundamentals, learning attention with neural networks, and scaling attention and applications. With all lectures, slides, and lab materials available online, this course is perfect for anyone looking to gain a better understanding of deep learning. Subscribe now to stay up to date with new deep learning lectures at MIT!
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Neural Networks
Neural Networks
StatQuest's Neural Networks playlist is the perfect way to learn about this powerful Machine Learning technique. From the basics of Neural Networks to image classification with Convolutional Neural Networks, this playlist has it all. Get ready to become an expert in Neural Networks with StatQuest!
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Multilayer Perceptron In 3 Hours Back Propagation In Neural Networks Great Learning
Multilayer Perceptron In 3 Hours Back Propagation In Neural Networks Great Learning
Learn to build a multi-layer perceptron in just 3 hours with this great learning tutorial. It covers the fundamentals of back propagation in neural networks and provides a comprehensive overview of the MLP architecture. Get ready to dive into the world of deep learning!
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Fundamentals of CNNs and RNNs
Fundamentals of CNNs and RNNs
This course covers the fundamentals of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two powerful tools used in computer vision and natural language processing. Learn the concepts of CNNs, the two major operators (convolution and pooling), and the structure of CNNs. Also, understand the concept and structure of RNNs, and the two variants of RNNs, LSTMs and GRUs. By the end of this course, you will have the skills required for computer vision and natural language processing.
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Neural Networks and Random Forests
Neural Networks and Random Forests
This course dives deep into two of the most powerful AI techniques: neural networks and random forests. We'll start by exploring the structure and properties of neural networks, then code some simple models and learn to avoid overfitting. After a project predicting heart disease, we'll move to random forests and explore their differences and origins. Finally, we'll complete a project predicting similarity between health patients.
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Mask R-CNN - Practical Deep Learning Instance Segmentation Tutorials
Mask R-CNN - Practical Deep Learning Instance Segmentation Tutorials
Learn how to use Mask R-CNN for instance segmentation with this practical deep learning tutorial. This course covers the installation and configuration of the Mask R-CNN framework, its concept and intuition, and how to interpret its output. You'll also learn how to write and tweak code for real-time Mask R-CNN webcam applications, train models for segmentation and detection of potholes, and develop your own applications using Mask R-CNN. Become an expert in OpenCV Python and Mask R-CNN today!
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Neural Network Tutorial Artificial Neural Network Backpropagation in Neural Networks AI Course
Neural Network Tutorial Artificial Neural Network Backpropagation in Neural Networks AI Course
Enroll now and get a comprehensive understanding of Artificial Neural Networks and Backpropagation in Neural Networks. Learn the fundamentals of AI and become an expert in the field.
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Neural Networks Demystified
Neural Networks Demystified
Learn the fundamentals of neural networks and how to apply them to solve real-world problems. Get an intuitive understanding of the mathematics behind neural networks and gain the skills to build your own.
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Efficient Geometry-aware 3D Generative Adversarial Networks GAN Paper Explained
Efficient Geometry-aware 3D Generative Adversarial Networks GAN Paper Explained
Discover the power of 3D Generative Adversarial Networks (GANs) with this comprehensive paper explanation. Learn about tri-plane 3D scene representation, NeRF in depth, explicit voxel grid methods, pose correlated facial features, dual discrimination, and super-resolution. Explore ethical considerations and robustness of intrinsics and extrinsics. Get started now!
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EI Seminar - Michael Carbin - The Lottery Ticket Hypothesis
EI Seminar - Michael Carbin - The Lottery Ticket Hypothesis
Learn from Michael Carbin, a renowned expert in Neural Networks, about the Lottery Ticket Hypothesis and its implications for training pruned networks. Explore the scalability challenges of larger-scale settings, and gain insight into the instability of linear mode connectivity. Discover the takeaways from our current understanding and the implications for follow-up research.
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Introduction To Soft Computing
Introduction To Soft Computing
This course will provide an introduction to the fundamentals of soft computing and its applications.
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Stanford Seminar: Concepts and Questions as Programs
Stanford Seminar: Concepts and Questions as Programs
This Stanford Seminar will help you understand the concepts and questions behind programming. Learn how to think like a programmer and develop the skills to create your own programs. Get ready to explore the world of programming and take your skills to the next level.
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Generative Adversarial Networks (GANs) and Stable Diffusion
Generative Adversarial Networks (GANs) and Stable Diffusion
This course provides an in-depth exploration of Generative Adversarial Networks (GANs) and Stable Diffusion. Learn how to build GANs with Tensorflow Keras, understand the differences between Generator and Discriminator, and explore the performance results of Stable Diffusion with KaraCV. Get ready to take your deep learning skills to the next level!
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Neural Networks for Signal Processing - I
Neural Networks for Signal Processing - I
This course provides an introduction to neural networks for signal processing. Learn how to build artificial neural networks inspired by the human brain, and explore topics such as Rosenblatt's perceptron, regression modeling, multilayer perceptron (MLP), kernel methods, support vector machines (SVM), regularization theory, and principal component analysis. Suitable for graduate students, senior undergraduates, engineers, and scientists in related industries. Prerequisites include basic mathematical background in probability, linear algebra, signals and systems or equivalent.
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Reseaux neuronaux convolutifs
Reseaux neuronaux convolutifs
Rejoignez ce cours pour apprendre à créer des réseaux neuronaux convolutifs et à les appliquer aux données d'image. Découvrez comment les réseaux résiduels et le transfert de style neuronal peuvent améliorer la vision par ordinateur et générer de l'art. Apprenez à appliquer ces algorithmes à une variété de données 2D et 3D.
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Build Decision Trees SVMs and Artificial Neural Networks
Build Decision Trees SVMs and Artificial Neural Networks
Enroll in this course to learn how to build decision trees, SVMs, and artificial neural networks to solve complex business problems. Gain the skills to select the best algorithm for the job and understand the workflow of building models. Become a Certified Artificial Intelligence Practitioner and take your career to the next level.
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Neural Networks Courses

Career Trends


Career Prospects

Average Salary

Position Overview

Python Engineer$166,648 per year

As a Python engineer, your responsibilities include utilizing the Python programming language to develop code for your company. This can involve creating new applications, integrating Python software with existing systems, and testing existing code to identify and fix bugs.

Senior Software Engineer$166,416 per year

A senior software engineer leads the development of software architecture, oversees software monitoring and testing, and implements necessary upgrades. This position requires extensive knowledge and experience in programming and software development.

Machine Learning Engineer$194,750 per year

A machine learning engineer is responsible for designing and developing AI algorithms that can learn and make predictions, which are essential in the field of machine learning. They typically work in collaboration with other members of a data science team, such as data scientists, administrators, data analysts, data engineers and data architects, to ensure the successful implementation and deployment of these algorithms.

Algorithm Engineer$151,856 per year

An algorithm engineer is tasked with enhancing AI applications by enabling clients or employers to identify patterns or issues within data sets.

Machine Learning Specialist$129,524 per year

A Machine Learning Specialist is a professional who specializes in developing algorithms within the field of computer science known as machine learning. Their focus is on creating algorithms that can learn from data, adapt to new situations, and make predictions based on that learning.


Educational Paths

1. Online Courses: There are many online courses available that cover the fundamentals of neural networks, including Coursera, Udemy, and edX.

2. Books: There are many books available on neural networks, including "Neural Networks and Deep Learning: A Textbook" by Charu Aggarwal and "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

3. Tutorials and Blogs: There are many tutorials and blogs available online that provide step-by-step guidance on building neural networks, including Medium, Towards Data Science, and Keras.

4. University Courses: Many universities offer courses in neural networks as part of their computer science or data science programs. Some examples include Stanford University, MIT, and Carnegie Mellon University.

5. YouTube Videos: There are many YouTube channels that offer educational videos on neural networks, including Sentdex, 3Blue1Brown, and deeplizard.

Frequently Asked Questions and Answers

Q1: What Neural Networks courses can I find on AZ Class?

On this page, we have collected free or certified 191 Neural Networks 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 Neural Networks for free?

Yes, If you don’t know Neural Networks, 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!

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