A machine learning model is a system that learns the relationship between the input (independent) features and the target (dependent) feature of a dataset to be useful in making predictions in the future. In this article, we are going to test the effectiveness of 3 popular models that vary in complexity.
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This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEO’s to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results. This blog drills down to explain ten Emotional and Social Intelligence skills required in building an AI Leadership Brain Trust.
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There’s no doubt about it – Artificial Intelligence has been a bit of a buzzword this year. Artificial intelligence has been established as the main driver of emerging technologies such as big data, robotics, and the IoT. So, what do the next 12 months look like for AI?
As a result of the global pandemic, consumer trends have changed significantly, which has resulted in some notable trends in the world of AI for 2021…
So much happened in the world during 2020 that it may have been easy to miss the great progress in the world of AI. To catch you up quickly, check out this curated list of the latest breakthroughs in AI by release date, along with a video explanation, link to an in-depth article, and code.
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The amount of data being generated continued to soar in 2020. And with the rollout of 5G networks, the deployment of edge computing, and the expanding adoption of cloud computing, most projections show the creation and collection of data continuing to accelerate. Earlier this year, IDC had projected that 59 zettabytes of data would be “created, captured, copied, and consumed” in 2020, up from 41ZB in 2019. By 2024, that number is expected to reach 149ZB.
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Machine Learning (ML) and Artificial Intelligence (AI) is a technology that is still finding its footing in the commercial sector. Although few systems are touted as a complete solution, there are many new AI/ML based companies that are capitalising on the benefits, and traditional business will need to follow suit. ML Ops, the ML equivalent of DevOps will become increasingly important.
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The concept of Artificial Intelligence (AI) is almost as old as the field of modern computer sciences. From the early days in the late 1950s to the shape it has transformed into, computer scientists have inspired, and at times, bamboozled humankind with its seemingly infinite capabilities. Having said that, it seems that there is hardly any field in computer sciences that has remained so misunderstood as AI, the general comprehension of the layman is generally mixed: precisely 34% according to the Department of Technological Policy Management.
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The right way to think about Data Quality, from Kimball and Uber’s points of view. If you’ve spent any amount of time in business intelligence, you would know that data quality is a perennial challenge. It never really goes away. For instance, how many times have you been in a meeting, and find that someone has to vouch for the numbers being presented?
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