Above two are intro course to deep learning. This is a Youtube channel which contains courses by Prof. Andrew Ng on various topics in deep learning. This a 14 week course, taught by Jeremy Howard. It cover most of the topics in deep learning. This course is a gentle introduction to Reinforcement Learning. It walks you through most of the topics in Reinforcement Learning in high level. This course is not taught at a basic level, so you need to be familiar with basic concepts and perhaps a little more. For more stories follow AiMantra.

Are you looking for game design and development courses? Here is the list of best game development courses, tutorials, training and certification for the individuals interested in becoming a game developer, game designer, game artist or a game programmer. Do you want to learn how to develop games? Then these Game Development Online Courses will show you the right path to get started. Building games is an innovative and technical art form.

Artificial Intelligence or Machine Learning, as it's better known as, is something that has garnered my interest over recent years. I even used the technology to design a company logo for a side hustle project. The expanding uses for Artificial Intelligence in business is fascinating and only becomes more exciting to see what is in store as the technology continues to mature. After being in the business for 25 years I have learnt and experienced first-hand the ever-changing landscape in which marketing continues to grow. We have seen a huge change in the industry over the last ten years, with the most prevalent and revolutionary change being the utter dependence of the online sphere. I have grown my business from strength to strength in the online realm, with platforms such as Twitter being a core tool.

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This course is focused on the question: How do we do matrix computations with acceptable speed and acceptable accuracy? This course was taught in the University of San Francisco's Masters of Science in Analytics program, summer 2017 (for graduate students studying to become data scientists). The course is taught in Python with Jupyter Notebooks, using libraries such as Scikit-Learn and Numpy for most lessons, as well as Numba (a library that compiles Python to C for faster performance) and PyTorch (an alternative to Numpy for the GPU) in a few lessons. Accompanying the notebooks is a playlist of lecture videos, available on YouTube. If you are ever confused by a lecture or it goes too quickly, check out the beginning of the next video, where I review concepts from the previous lecture, often explaining things from a new perspective or with different illustrations, and answer questions.

Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks.

The power of modern search engines is undeniable: you can summon knowledge from the internet at a moment's notice. There are many situations where search is relegated to strict keyword search, or when the objects aren't text, search may not be available. Furthermore, strict keyword search doesn't allow the user to search semantically, which means information is not as discoverable. Today, we share a reproducible, minimally viable product that illustrates how you can enable semantic search for arbitrary objects! Concretely, we will show you how to create a system that searches python code semantically -- but this approach can be generalized to other entities (such as pictures or sound clips).

Generating text is one of those projects that seems like a lot of fun to machine learning and NLP beginners, but one which is also pretty daunting. Or, at least it was for me. Thankfully, there are all sorts of great materials online for learning how RNNs can be used for generating text, ranging from the theoretical to the technically in-depth to those decidedly focused on the practical. There are also some very good posts which cover it all and are now considered canon in this space. All of these materials share one thing in particular: at some point along the way, you have to build and tune an RNN to do the work.

Artificial intelligence could be heading to Australian classrooms -- and in schools overseas, it's already there. In Bahia, Brazil, 15-year-old students David and Roama from Colegio Perfil often start their school day at home, or on the bus. They pick up their phones, log into the education app Geekie Lab, and begin their classes from wherever they are. "You can access it everywhere, as long as you have your phone with you," David said. Students from Colegio Perfil in Bahia use phones or computers to access the Geekie app.

This webinar covers a high-level introduction to data science and machine learning and their potential in a data-driven organization. Learn key trends and concepts, proven use cases and an overview of leading technologies.