This is a basic awareness level course with case studies multiple industries such as Retail, Railways, Pharma, Insurance, Banking, Hospitality, Beverages, Real Estate, Warehouse, etc. In the contemporary period of gigantic volumes and multi-regional global business internet reach, the new innovative technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have significant importance in the global business. This course can help to enhance the digital skills of any individual as it is a "street-smart view" of the AI usage in the industry. This course explains in the most simple manner giving examples such as Growth of the child, parentage, Chess Player, AlphaGO of Google, Robot usage, farming etc.

Industry 5.0 and 5G wireless communication has led to the development of cost-effective sensors, thereby leading to the emergence of Internet of things (IoT). It has an indispensable role in today's healthcare system. Internet of medical things (IoMT) is a crucial component of the modern healthcare system [1]. IoT is an ever-expanding, limitless ecosystem that integrates software, hardware, or any other device equipment that collects or exchanges data. IoT in the healthcare service network is known as IoMT. The need for telemedicine, remote patient monitoring, automated diagnosis, detection, and treatment of acute diseases has become eminent, especially after the COVID-19 pandemic.

Artificial Intelligence (the primary and most important sub-domain of data science), Machine Learning, and Deep Learning are the trending topics of this century. Their broad scope of uses has changed the aspects of innovation in each field, going from Healthcare, Manufacturing, Business, Education, Banking, Information Technology, and so forth. Though these terminologies are widely used and familiar, they are frequently used interchangeably. However, there is a huge difference between these three terms. Artificial intelligence is an umbrella discipline that covers everything identified with making machines more insightful.

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are often applied in cybersecurity, but their applications may not always work as intended. ISACA's new publication, AI Uses in Blue Team Security, looks at AI, ML and DL applications in cybersecurity to determine what is working, what is not, what looks encouraging for the future and what may be more hype than substance. Leveraging interviews with some of the engineers behind these technologies, firsthand examination and use of some of the related products, and observations of chief information security officers (CISOs) and chief information officers (CIOs), AI Uses in Blue Team Security seeks to determine whether marketing tactics obscure reality when it comes to new security technology. Of the 13 engineers who commented for this publication, none felt that the marketing associated with the products they were working on was completely accurate with respect to advertised capabilities. However, the engineers were optimistic about the direction they were heading and the technologies they would be creating as they relate to ML and DL.

Artificial Intelligence (the primary and most important sub-domain of data science), Machine Learning, and Deep Learning are the trending topics of this century. Their broad scope of uses has changed the aspects of innovation in each field, going from Healthcare, Manufacturing, Business, Education, Banking, Information Technology, and so forth. Though these terminologies are widely used and familiar, they are frequently used interchangeably. However, there is a huge difference between these three terms. Artificial intelligence is an umbrella discipline that covers everything identified with making machines more insightful.

Distinguishing between artificial intelligence, machine learning, and deep learning has always been a demanding task especially for newbies in this field. It is therefore necessary to explain briefly, ways to help tell them apart. Artificial Intelligence(AI): In simple terms, AI is training computers to understand data as humans do. A machine is said to exhibit intelligent behavior whenever it solves a problem based on a defined set of rules called an algorithm. The algorithm could be a deep learning algorithm or a machine learning algorithm.

Follow this order to get the most benefit if you are still a beginner. This is one of the best books out there to start with your Data Science journey. It introduces us to various Machine Learning concepts with practical coding examples and a humongous GitHub repository to practice later. It also introduces us to basic Deep Learning concepts, explained brilliantly, and how to implement them using the deep learning framework, Tensorflow. You will keep on coming back to this book for years to follow to get a quick revision on your ML and DL concepts.

AI vs. ML vs. DL vs. Data Science – How are they different from each other and how are they related? Data professionals work with many technologies that may, at first, seem similar. Adding to the confusion is that some media sources use the terms interchangeably. In this article, we're going to learn the differences between artificial intelligence (AI) and machine learning (ML) and how does deep learning (DL) relate to those two. Then, we'll find out how data science fit into all these terms.

Multinational automaker Nissan manufactures vehicles in 20 countries worldwide, with production volume exceeding 5.6 million vehicles. And while its production assets were generating an abundance of operational and production data, the company lacked sufficient skilled resources to perform analysis on all of that data adequately. However, through an engagement with its vendor, Senseye, and using artificial intelligence (AI), Nissan was able to analyze that data and generate predictions on when its production machines would need maintenance. Before, the automaker used static metrics (such as the number of process cycles or the number of hours in service) to determine when a machine needs to be taken out of service for maintenance. Nissan used machine learning (ML) algorithms to monitor and spot patterns in the operational data of more than 9,000 connected assets and more than 30 different machine types, including robots, conveyors, drop lifters, pumps, motors, and press/stamping machines.

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.