Every Business rely on data nowadays to analyze the fundamental information. They use these data to understand the current business performance and find out their past performance trends. This will help businesses to make important business decisions and also help them improve their revenue growth and profits by implementing best practises and key decisions. Not sure how many of you know about the following. I will give you a glimpse on these topics as these are the basics that one should know.

Autonomous technology continues to make an impact on the supply chain. The autonomous supply chain, as I am writing about it here, applies to moving goods without human intervention (to some degree at least). One of the more interesting examples I have seen is from the Belgian brewery De Halve Maan, which in an effort to reduce congestion on the city streets, built a beer pipeline under the streets. The pipeline is capable of carrying 1,500 gallons of beer an hour at 12 mph to a bottling facility two miles away. As we've written about here quite often, autonomous technology is mainly seen in warehouses, on highways, and in last mile deliveries.

Welcome to this course "Complete Machine Learning Masterclass – Learn From Scratch". In this course you will learn from scratch. We will assume that you are a complete beginner and by the end of the course you will be at advanced level. This course contain Real-World examples and Hands On practicals. We will guide you step by step so that you can understand better.

In a recent blog post, Google announced the beta of Cloud AI Platform Pipelines, which provides users with a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. With Cloud AI Pipelines, Google can help organizations adopt the practice of Machine Learning Operations, also known as MLOps – a term for applying DevOps practices to help users automate, manage, and audit ML workflows. Typically, these practices involve data preparation and analysis, training, evaluation, deployment, and more. When you're just prototyping a machine learning (ML) model in a notebook, it can seem fairly straightforward. But when you need to start paying attention to the other pieces required to make an ML workflow sustainable and scalable, things become more complex.

As the Agent interacts with the Environment, it learns a policy. A policy is a "learned strategy" that governs the agents' behaviour in selecting an action at a particular time t of the Environment. A policy can be seen as a mapping from states of an Environment to the actions taken in those states. The goal of the reinforcement Agent is to maximize its long-term rewards as it interacts with the Environment in the feedback configuration. The response the Agent gets from each state-action cycle (where an Agent selects an action from a set of actions at each state of the Environment) is called the reward function.

Dr. David Ferrucci is one of the few people who have created a benchmark in the history of AI because when IBM Watson won Jeopardy we reached a milestone many thought impossible. I was very privileged to have Ferrucci on my podcast in early 2012 when we spent an hour on Watson's intricacies and importance. Well, it's been almost 8 years since our original conversation and it was time to catch up with David to talk about the things that have happened in the world of AI, the things that didn't happen but were supposed to, and our present and future in relation to Artificial Intelligence. All in all, I was super excited to have Ferrucci back on my podcast and hope you enjoy our conversation as much as I did. During this 90 min interview with David Ferffucci, we cover a variety of interesting topics such as: his perspective on IBM Watson; AI, hype and human cognition; benchmarks on the singularity timeline; his move away from IBM to the biggest hedge fund in the world; Elemental Cognition and its goals, mission and architecture; Noam Chomsky and Marvin Minsky's skepticism of Watson; deductive, inductive and abductive learning; leading and managing from the architecture down; Black Box vs Open Box AI; CLARA – Collaborative Learning and Reading Agent and the best and worst applications thereof; the importance of meaning and whether AI can be the source of it; whether AI is the greatest danger humanity is facing today; why technology is a magnifying mirror; why the world is transformed by asking questions.

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The same survey reports that those that use these technologies are noticing improved customer experience performance and higher customer satisfaction. But what do these technologies look like in real life? And what retail innovations can we expect to see today as a result? We interviewed 5 retail innovation leaders at NRF 2020's Innovation Lab, and they showed us how they're using emerging tech to change customer experience in 2020 and beyond. The subject of inventory management may not evoke fun and excitement – at least not in the traditional sense.

While artificial intelligence continues its sweeping disruption across the enterprise, the last couple of years, in particular, have demonstrated a growing ability to transform human resources administration. We've seen applications for reducing recruiting biases, advanced analytics for determining the voice of employees, and a rise in virtual assistants to offload work for HR professionals. Still, with an increasingly digitally native workforce, AI is finally being trusted with more complicated and sophisticated issues. One of those more complicated areas, in particular, is workplace wellness and, more specifically, mental health. Employers now know that ensuring employee wellness is core to creating a positive employee experience, and study after study has shown that modern-day employees evaluate their potential jobs based on employee experience.

Mr. Kant is a former firefighter, paramedic and emergency manager with a proven history of saving lives with innovation, applying operational expertise, and offering hands-on guidance at significant events and disasters worldwide. He serves as the disaster portal coordinator for the International Association of Emergency Mangers and works as an innovation entrepreneur. He has been recognized by the Department of Defense, National Geospatial Intelligence Agency, NATO, DHS S&T and others for innovative thinking and applying expert analysis to complex cascading operational interdependencies. During his career, Mr. Kant has supported real-time command/control operations for multiple agencies around the world, including the Florida Night of Tornadoes, the World Trade Center Disaster, hurricanes Katrina and Sandy, and many other significant events and disasters over the past two decades.