In the past few years, the number of people entering the field of data science has increased drastically because of higher salaries, an increasing job market, and more demand. Undoubtedly, there are unlimited programs to learn data science, several companies offering in-depth Data Science Bootcamp, and a ton of channels on YouTube that are covering data science content. The abundance of data science content can easily confuse one with where to begin or how to start their data science career. To ease this data science journey for beginners, intermediate, or starters, we are going to list a couple of data science tutorials, crash courses, webinars, and videos. The aim of this blog is to help beginners navigate their data science path, and also help them to determine if data science is the most perfect career choice for them or not.

Do you want to begin a career in Artificial Intelligence? If yes, then this webinar is for you! The AI-900 certification takes you through Azure AI Fundamentals and therefore is a great first step in your AI journey. In this Free Webinar, we will introduce you to the basic concepts of Azure AI and help you understand the benefits of taking up this certification, exam preparation methods, recommended resources, and much more. We will also have our own AI experts facilitate this session to give you the best insights possible! Who Can Attend? Absolutely Anybody! What’s In It For You? 1. Learn from Microsoft Certified in-house experts who curate our training modules for industry professionals to address their unique requirements. 2. Get access to CloudThat’s Exam Ready Assessment Platform, where you can practice exam questions and assess your preparedness before taking the AZ-500 certification exam.

In order to implement your artificial intelligence strategy, you need to understand how AI works, find out potential business use cases, gather high quality data and check whether your team is skilled. To make the most out of AI offerings and to drive competitive advantage, you should jumpstart your artificial intelligence strategy by following the guidelines mentioned in this blog post. As the pace of digital transformation goes up, business leaders should prepare to make faster, smarter, and more tangible decisions around the continuously growing data. Hence, companies need the assistance of artificial intelligence solutions that will allow them to make more, real-time, and accurate decisions for their business with maximum efficiency. But, for leveraging artificial intelligence in an organization, there should be an effective artificial intelligence strategy in place, which acts as a roadmap that guides business leaders to use the technology for the right business use cases.

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This is a core part of AI and ML (Mechanical Learning), and having a working knowledge of probability concepts including conditional probability, Markov models, Bayesian principles, and more. It is used extensively in AI to handle pattern recognition and datasets classification.

Artificial Intelligence (AI) is a skill one can use for a successful career in any field. It is no longer restricted to the IT industry and professionals with non-technical backgrounds are also entering the field of AI through upskilling. Picture this: the experts' estimation about AI is that by 2030, the contribution of the AI market to the world's economy will be more than USD 15$ trillion. However, there is a huge shortage of skilled (aka certified) professionals in the field of AI. For those who wish to make their career in the field of AI, this is the right time.

MLOps follows a set of practices to deploy and maintain machine learning models in production efficiently and reliably. While the data science team has a deep understanding of the data, the operations team holds the business acumen. MLOps combines the expertise of each team, leveraging both data and operations skill sets to enhance ML efficiency. According to the Algorithmia report, nearly 22 percent of companies have had ML models in production for one to two years. With practice, MLOps professionals can enhance their skills, and develop a solid pipeline for developing machine learning models.

"Andrew Ng will soon be launching a campaign; a competition to push for data-centric models." A lot of people joke about how 80 percent of machine learning is simply data cleaning. Additionally, many people look at machine learning as a glorified, technical version of statistics--a field that places a great deal of importance on data. If anything, this tells us one thing for sure: Data is critical. Even a well known face in the ML community, Andrew Ng has stressed how ML needs to take a more data-centric stance rather than a model-centric one.

Most top global companies have investments in Artificial Intelligence and the market for AI is expected to grow to $267 billion by 2027. Understanding AI and its applications is increasingly important. The University of Hong Kong (HKU) Business School MBA program offers a specialist AI module, which introduces students to the technology and its uses within various industries. With an understanding of cutting-edge tech topics like AI, HKU MBA students go on to land jobs in a variety of high-tech industries, many benefiting from Hong Kong's vibrant tech startup scene. HKU MBA alums, Aakriti Jain (class of 2020) and Geeseok Oh (class of 2019), used what they learned about AI from the MBA to secure exciting jobs in fintech.

A few years ago, I learned about the billions of dollars banks lose to credit card fraud on an annual basis. Better detection or prediction of fraud would be incredibly valuable. And so I considered the possibility of convincing a bank to share their transactional data in the hope of building a better fraud detection algorithm. The catch, unsurprisingly, was that no major bank is willing to share such data. They feel they're better off hiring a team of data scientists to work on the problem internally. My startup idea died a quick death.