We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach. The material is accessible to practitioners with a one-year college-level exposure to statistics and probability.

I often use this quote from Isaac Newton in my teaching. AI is a vast and a complex subject. No matter how much you know - you realise that there is really a vast amount more to learn. So, my way of learning a subject as complex and dynamic as AI, is to share my insights. This helps me to refine my own thinking.

We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach. The material is accessible to practitioners with a one-year college-level exposure to statistics and probability.

Enterprise AI: An applications perspective takes a use case driven approach to understanding the deployment of AI in the Enterprise. Designed for strategists and developers, the book provides a simple and practical roadmap based on application use cases for AI in Enterprises. The authors (Ajit Jaokar and Cheuk Ting Ho) are data scientists and AI researchers who have deployed AI applications for Enterprise domains. The book is used as a reference for Ajit and Cheuk's new course on Implementing Enterprise AI. The term'Enterprise' can be understood in terms of Enterprise workflows.

Image source: Glenfinnan Viaduct – aka "The Harry Potter Bridge" source Wikipedia – an apt analogy bridging the known to the unknown! In April this year, I posted about the seven books to grasp the mathematical foundations of data science which was one of my most popular posts ever. It demonstrated to me that there is a real need to understand the maths foundations behind Data Science. As part of my teaching at the University of Oxford(Data Science for Internet of Things), I have often encountered the same issue in working with participants. I am also personally interested in democratising AI knowledge, especially for the younger generation.

The course combines elements of teaching, coaching and community. For this reason, the batch sizes are small and selective. I will be working with a small/selective group of people to actively transfer their career to AI through education and my network towards specific outcomes/goals. "Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful. As for myself, it allowed me to go into topics of interests that help me in reshaping my career."

Created by industry thought leader Ajit Jaokar in 1999, our research is based on two courses conducted by Ajit: Big Data for Telecoms at Oxford University and the newly launched citysciences program program at UPM ( Technical University of Madrid) which apply machine learning techniques to IoT and Smart cities applications. "Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful. As for myself, it allowed me to go into topics of interests that help me in reshaping my career. "This DSIOT course is a great way to get up-to-speed.

We welcome call for Papers - please email me at ajit.jaokar at futuretext.com if you are interested in the below Artificial Intelligence (AI) and Deep Learning technologies are impacting many aspects of our lives. In future, this impact is expected to accelerate to many more areas including Smart cities. To address them through AI technologies, we need to think beyond the current silo-based approach. We need to look to the interconnections between city areas. Most importantly, we wish to engage with new ideas in an'agile' way.