Recently I completed the Data Engineering on Google Cloud Platform Specialization (link here) through Coursera, here is my review. Only problem was a couple of issues in the final labs of the course. You can take each module out of order or complete sequentially. Its up to you, I'd recommend to keep it sequential at least roughly. I went from 1 to 3 then went back to 2, 4 and then 5. The courses are hosted by Valliappa Lakshmanan from Google.

In September, San Diego robotics startup Brain Corporation will introduce artificial intelligence software that allows giant commercial floor-cleaning machines to navigate autonomously. The follow-up offering it wants to develop may be even more forward-looking: A training and certification program for janitors to operate the machines. The program, still in early stages of planning, is aimed at helping janitors maximize efficiency and establishing standards and best practices for the use of robots in janitorial work, according to Brain Corporation. The company says it is not aware any other such training program exists. There's additional incentive for Brain Corp. to offer training options.

Artificial Intelligence (AI) is one of the most booming topics in every industry. Based on the demand, Artificial Intelligence Courses are offered by a number of massive open online courses (MOOCs) providers like Udemy, Coursera, and edX. Some of this popular MOOC providers offer some in-depth artificial intelligence programs. Majority of these artificial intelligence tutorials are often taught by industry top AI researchers or experts. However, these courses are cheaper compared to the university courses.

Imagine if something not designed with you or anyone like you in mind was the driving force of how regular interactions permeate your life. Imagine it controls what products are marketed to you, how you can use certain consumer products (or not), influences your interactions with law enforcement, and even determines your health care diagnoses and medical decisions. There are problems brewing at the core of artificial intelligence and machine learning (ML). AI algorithms are essentially opinions embedded in code. AI can create, formalize, or exacerbate biases by not including diverse perspectives during ideation, testing, and implementation.

Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator's actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD's performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.

An artificial intelligence enabled robot has passed the written test of China's national medical licensing examination for the first time, marking another milestone in the quest for AI technology to match or surpass human intelligence. Named Xiaoyi, the robot developed by Tsinghua University and Chinese information technology firm iFlytek, achieved a score of 456, 96 points higher than the required mark of 360 points, according to a company announcement. To pass the text, Xiaoyi was required to memorize and understand the contents of one million medical images, 53 medical books, two million medical records, and 400,000 pieces of medical literature and medical reports, a task which normally takes five years of study by a medical student. The robot reportedly failed an earlier attempt to pass the test. "Xiaoyi's successful pass in the written exam represent a significant development in the field of cognitive intelligence," said iFlytek in the company announcement.

It isn't just the tech entrepreneurs and Hollywood directors who dream about the role that artificial intelligence can play, or will play, in everyday human life--educators have begin to join them. However, those dreams aren't always pleasant and may, in fact, sometimes turn into nightmares. If computer systems are able to perform tasks that humans have performed for thousands of years, will it render teachers and administrators a thing of the past? Or is artificial intelligence the secret to freeing up educators' time for other, non-routine tasks, like mentoring and spending more one-on-one time with students? To find out, I went straight to the source--eight educators, including superintendents, coaches and teachers--to find out whether AI tickles their fancy or scares them straight.

If your computer doesn't have a GPU or has a non-Nvidia GPU, you have several great options: Use Crestle, through your browser: Crestle is a service (developed by fast.ai

There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... There's also a great appetite for learning about Data Science too. Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list.

The old paradigms of Data Architecture are evolving at an ever-increasing rate. Past enterprise architectures are undergoing significant technological changes in the face of new trends, including Big Data, non-relational data stores, Blockchain, Internet of Things, Machine Learning and Artificial Intelligence, Data Lakes, and many others. Enterprises must still contend with their foundational data assets and legacy systems though -- Data Governance, Master Data Management, Data Quality, Data Modeling, and other traditional Data Management concepts and practices are more important than ever. What are the emerging trends in Data Architecture? How can next-generation architectures help an enterprise to become data-driven?