These are the best Deep Learning courses on the web (November 2018)

For those with a lot of experience in machine learning and that are looking to gear up to another level, this course is great. You will learn Supervised, Unsupervised, Reinforcement, and Deep Learning. The program also includes a capstone project that functions as a great addition to your portfolio.

Probably the highest acclaimed course on the internet in regards to Deep Learning. Deep learning is the driver of most machine learning and AI implementations that we see happening in the world. A student that enrolls in this course will build and apply their own neural networks to tackle challenges put forth by the course creators. For anybody that wants to step into the world of AI, this is a great starting point. More information

Deep reinforcement learning is what powers the current advances that we see happening in Aritificial Intelligence. This particular subsection of the industry has the potential to literally change the world. As a student of this program, you will learn skills like Deep Q-learning and actor-critic methods. You will get in touch with experts from the great companies NVIDIA and Unity.

"Neural Networks and Deep Learning" by Coursera is an excellent course that helps you understand the major technology trends in Deep Learning and teaches your how to build, train, and implement neural networks. This is more than a surface-level course, diving deep into the fundamentals of what makes Deep Learning work. More information

In this course, you will get to understand what makes deep learning work. No longer will you see it as a black box. After this course, students will understand what drives good performance of a deep learning model and be able to work on neural networks using initialization, L2 regularization, batch normalization, and so forth. During this all, TensorFlow will be used and taught to the student. More information

Every technical worker that want to master AI and guide a team through a project will love this 2-week course. You will be able to practice through real examples and learn to diagnose errors in a machine learning system, prioritize errors, understand how ML works and how to transfer your knowledge. Basic machine learning knowledge is needed since this is the 3rd part of the Deep Learning Specialization. More information

The course, the fourth course of the Deep Learning Specialization Program teaches students how to build convolutional neural networks including residual networks and other recent variations and apply them to image data. Participants will learn how to use neural file transfer to generate art and apply the algorithms to a variety of images, videos and other 2D or 3D data. More information

This course gives you the opportunity to deeply understand the C# language's generics feature. As one of the most powerful features in C#, learning generics is key if you want to be a more complete programmer. This course features tips for hiding generic noise from business logic as well as a thorough demonstration of constraints in building APIs.

Do you want to have a deeper level of understanding of machine learning? This expert course from Udacity is great for you. In here, students will become a participant in the ever-growing research community of reinforcement learning and improve your knowledge about the field itself. More information

An in-depth course on Deep Learning Specialization is available now for intermediate students. This course will cover on building and training Recurrent Neural Networks, how to apply sequence models to natural language problems, audio applications and speech recognition. After this great Coursera course has been completed, students will have an opportunity to build a deep learning project with relevant content. More information

Choose this company if you want to learn how to make deep learning algorithms in Python. There are skilled and experienced individuals who provide templates, tutorials and discussions on machine learning and data science. Having wrapped up the program, the student will be able to apply concepts and skills learned.

Probably the highest acclaimed course on the internet in regards to Deep Learning. Deep learning is the driver of most machine learning and AI implementations that we see happening in the world. A student that enrolls in this course will build and apply their own neural networks to tackle challenges put forth by the course creators. For anybody that wants to step into the world of AI, this is a great starting point. More information

Artificial intelligence is already enhanced and innovated at present and has made life more convenient for a lot of people. That is why learning about the neural network is essential not only for the mind, but for the technological aspect as well. Take this hands-on course and learn various techniques on how to use these advance machines to make life easier.

Artificial intelligence is one of those inventions that makes businesses flourish. That is why people want to learn more about it and this course tackles it all. You will learn more about the Numpy, Pandas, Scipy and Matplotlib stack. There will be a lot hands-on machine learning which is open to beginners, intermediate and advanced users.

For intermediate students, TensorFlow with Python is important for most IT projects. That is why taking up this course today will put you one step ahead of the game. Be able to solve problems with unique and effective techniques at the end of this course.

For beginners, intermediate and advanced programming students, be sure to take this course today. This course provides a clear understanding of data science, deep learning and machine learning which is important in creating Real-World applications. Having wrapped up the program, expect to create your own application without much challenge.

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Apply Deep Learning with TensorFlow to typically unstructured data (images, sound, and text) in order to resolve real-world problems. You

Data science: Learn linear regression from scratch and build your own working program in Python for data analysis.

Learn how to frame reinforcement learning problems, tackle classic examples, explore basic algorithms from dynamic programming, temporal difference learning, and progress towards larger state space using function approximation and DQN (Deep Q Network). This course is part of the Microsoft Profession More information

Now you can master your deep reinforcement learning skills and get new insights to advance your understanding of artificial intelligence and the concept of machine learning with this comprehensive course. Learn how to use Python to create a simple approach to prepare for deep reinforcement learning.

Data science techniques for professionals and students - learn the theory behind logistic regression and code in Python