Artificial intelligence is a driving force to change humanity by helping people and businesses create exciting, innovative products and services, drive critical decisions and achieve key goals.This is the reason why companies are hiring AI professionals at a jaw-dropping rate! The median salary of an AI engineer in the US is nothing less than $ 80,000 according to payscale.com.Nearly all big tech companies have an artificial intelligence project, and they are willing to pay experts millions of dollars to help get it done! Artificial intelligence is a driving force to change humanity by helping people and businesses create exciting, innovative products and services, drive critical decisions and achieve key goals.This is the reason why companies are hiring AI professionals at a jaw-dropping rate! The median salary of an AI engineer in the US is nothing less than $ 80,000 according to payscale.com.Nearly all big tech companies have an artificial intelligence project, and they are willing to pay experts millions of dollars to help get it done!

Tensorflow is Google's popular offering for machine learning and deep learning. It has quickly become a popular choice of tool for performing fast, efficient, and accurate deep learning. This course presents the implementation of practical, real-world projects, teaching you how to leverage Tensforflow's capabilties to perform efficient deep learning. In this video, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how they can be implemented using Tensorflow. This will be demonstrated with the help of end-to-end implementations of three real-world projects on popular topic areas such as natural language processing, image classification, fraud detection, and more.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or make business forecasting related decisions. Learning forecasting methods and models is indispensable for business or financial analysts in areas such as sales and financial forecasting, inventory optimization, demand and operations planning, and cash flow management. It is also essential for academic careers in data science, applied statistics, operations research, economics, econometrics and quantitative finance. And it is necessary for any business forecasting related decision. But as learning curve can become steep as complexity grows, this course helps by leading you through step by step real world practical examples for greater effectiveness.

No matter how many Amazon Echo commercials you see, it takes a little time to adjust to Alexa. Putting a virtual assistant in your home signals a change in lifestyle, sort of like adopting a puppy. There will be a lot of trial-and-error, but once you find your rhythm, you'll forget what life was like without her. The Amazon Echo listens for the wake word, "Alexa." But, frankly, I was shocked by how many conversations were recorded by my Echo that did not include the wake word. Click here to learn how to listen to everything Amazon Echo has ever heard.

Learning Machine Learning is a definite way to advance your career and will open doors to new Job opportunities.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do research as experienced investor. Learning stock technical analysis is indispensable for finance careers in areas such as equity research and equity trading. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors stock technical trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for back-testing to achieve greater effectiveness.

You've definitely heard of AI and Deep Learning. But when you ask yourself, what is my position with respect to this new industrial revolution, that might lead you to another fundamental question: am I a consumer or a creator? For most people nowadays, the answer would be, a consumer. But what if you could also become a creator? What if there was a way for you to easily break into the World of Artificial Intelligence and build amazing applications which leverage the latest technology to make the World a better place?

Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time we dive deep into Machine Learning.

We establish the equivalence of two very general theories: the first is the decision-theoretic formalisation of incomplete preferences based on the mixture independence axiom; the second is the theory of coherent sets of desirable gambles (bounded variables) developed in the context of imprecise probability and extended here to vector-valued gambles. Such an equivalence allows us to analyse the theory of incomplete preferences from the point of view of desirability. Among other things, this leads us to uncover an unexpected and clarifying relation: that the notion of `state independence'---the traditional assumption that we can have separate models for beliefs (probabilities) and values (utilities)---coincides with that of `strong independence' in imprecise probability; this connection leads us also to propose much weaker, and arguably more realistic, notions of state independence. Then we simplify the treatment of complete beliefs and values by putting them on a more equal footing. We study the role of the Archimedean condition---which allows us to actually talk of expected utility---, identify some weaknesses and propose alternatives that solve these. More generally speaking, we show that desirability is a valuable alternative foundation to preferences for decision theory that streamlines and unifies a number of concepts while preserving great generality. In addition, the mentioned equivalence shows for the first time how to extend the theory of desirability to imprecise non-linear utility, thus enabling us to formulate one of the most powerful self-consistent theories of reasoning and decision-making available today.

I have been looking to create this list for a while now. There are many people on quora who ask me how I started in the data science field. And so I wanted to create this reference. To be frank, when I first started learning it all looked very utopian and out of the world. The Andrew Ng course felt like black magic.