Structure: Good structure and went through all the basic principles of statistics in detail. Appreciated how it did not have to go through the methodology of each method, but taught us how to appreciate it and understand the data as it was presented in the literature. I liked how John went through the examples in the literature so it was good to see how it was utilised in practice. I wish there was a separate course to teach us how to use these methods with sample data, perhaps a taster of this would have been good to include? but I do understand that would be challenging for some. I think some in-video questions would have been good to check-up on the progress of learning.

For some us, AI is kind of an iffy proposition. To many, it is nebulous enough to seem like it might replace us or our jobs. And the harbingers of this sea change aren't exactly affirming: every other week in the news, self-driving smart cars keep crashing, with injuries and sometimes fatalities. AI generally doesn't seem to be that well-received in mass media, either, like in movies like Minority Report or TV shows like Westworld. Because of all this, the public perception of AI might be on the negative side. A good way to overcome uneasiness, anxiety or fear is simply be learning more about whatever seems to be the issue or problem.

I must have read hundreds of articles preaching strategies to achieve success. They tell me pick up these habits, follow these simple steps, adopt these rules to live by etc. etc. etc. They all insist I get up approximately 4 hours earlier than I want to and eat exclusively plant based substitutes that cost 3 times the price of the original product. The question I have is, if I were to follow the steps and achieve success what would that mean? How can I tell if I am successful?

Artificial Intelligence is one of the hottest field in computer science at the moment and has taken the world by storm as a major field of development and research. Python has emerged as a dominant language in AI/ML programming because of its simplicity and flexibility. Are you a Python developer who is interested to build real-world Artificial Intelligence applications? If so, A-Z Artificial Intelligence with Python is for you! This comprehensive 5-in-1 training course is designed such that you can add an intelligence layer to any application that's based on images, text, stock market, or some other form of data.

Artificial Intelligence is a broader umbrella under which Machine Learning (ML) and Deep Learning (DL) comes. Diagram shows, ML is subset of AI and DL is subset of ML. AI is composed of 2 words Artificial and intelligence. Anything which is not natural and created by humans is artificial. Intelligence means ability to understand, reason, plan etc.

Does your passion lie in Data Science / Analytics? Currently, data science and machine learning are changing the world. Here's your chance to live your passion. To become better at what you do, you no longer need to stick around your laptop for long hours. Take a break and switch to faster way of learning. Did you know you can run Python in your phone?

About this course: This course will teach you how to create useful chatbots without the need to write any code. Leveraging IBM Watson's Natural Language Processing capabilities, you'll learn how to plan, implement, test, and deploy chatbots that delight your users, rather than frustrate them. True to our promise of not requiring any code, you'll learn how to visually create chatbots with Watson Assistant (formerly Watson Conversation) and how to deploy them on your own website through a handy WordPress plugin. No worries, one will be provided to you. Chatbots are a hot topic in our industry and are about to go big.

This is the May edition of the AI/ML learning resources newsletter -- I compiled a list learning resources, most of which are recently announced or upcoming in the near future. A few of these may have been around for a while but I recently discovered them. We are living in very exciting times when so many AI/ML learning resources are being launched at such a rapid pace. Many companies and individuals are working hard to bring AI/ML and deep learning to everyone, and I'm joining that effort by sharing. Google I/O 2018 (5/8–5/10) has tons of sessions on AI/ML.

We present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT). We investigate the reliability of human bandit feedback, and analyze the influence of reliability on the learnability of a reward estimator, and the effect of the quality of reward estimates on the overall RL task. Our analysis of cardinal (5-point ratings) and ordinal (pairwise preferences) feedback shows that their intra- and inter-annotator $\alpha$-agreement is comparable. Best reliability is obtained for standardized cardinal feedback, and cardinal feedback is also easiest to learn and generalize from. Finally, improvements of over 1 BLEU can be obtained by integrating a regression-based reward estimator trained on cardinal feedback for 800 translations into RL for NMT. This shows that RL is possible even from small amounts of fairly reliable human feedback, pointing to a great potential for applications at larger scale.

Existing research highlight the myriad of benefits realized when technology is sufficiently democratized and made accessible to non-technical or novice users. However, democratizing complex technologies such as artificial intelligence (AI) remains hard. In this work, we draw on theoretical underpinnings from the democratization of innovation, in exploring the design of maker kits that help introduce novice users to complex technologies. We report on our work designing TJBot: an open source cardboard robot that can be programmed using pre-built AI services. We highlight principles we adopted in this process (approachable design, simplicity, extensibility and accessibility), insights we learned from showing the kit at workshops (66 participants) and how users interacted with the project on GitHub over a 12-month period (Nov 2016 - Nov 2017). We find that the project succeeds in attracting novice users (40\% of users who forked the project are new to GitHub) and a variety of demographics are interested in prototyping use cases such as home automation, task delegation, teaching and learning.