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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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Why data catalogs aren’t meeting the needs of the modern data stack, and how a new approach – data discovery – is needed to better facilitate metadata management and data reliability.

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A famous Casino-inspired trick for data science, statistics, and all of science. How to do it in Python?

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Machine learning models deployed today — as will many more in the future — impact people and society directly. With that power and influence resting in the hands of Data Scientists and machine learning engineers, taking the time to evaluate and understand if model results are fair will become the linchpin for the future success of AI/ML solutions. These are critical considerations, and using a recently developed fairness module in the dalex Python package is a unified and accessible way to ensure your models remain fair.

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Amalgamation or advances of computer vision with different technologies will dominate the year 2021. Here are top 9 Computer Vision trends that will dominate the Industry in 2021.

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People assume that NoSQL is a counterpart to SQL. Instead, it’s a different type of database designed for use-cases where SQL is not ideal. The differences between the two are many, although some are so crucial that they define both databases at their cores.

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Machine Learning — How machines learn like other life forms.

This article will help you understand what is Machine Learning (ML) and why it is called Machine Learning, and how it resembles with how other living creatures learn and evolve right from their birth. Let’s see below:

Machine Learning is an art of teaching computers or letting them learn patterns from the data. This is often done because it helps in making informed decisions and sometimes accurate predictions. I promise that’ all the definition you’ll have in this article.

Always remember, if you can hit upon an analogy of what the unknown concept is like, you are half way there. The rest is to practice explaining to a 6 year old. So let’s take an easy example to understand ML:

1. Learning the pattern

Let’s say a baby panda while playing in the forest sees a burning bright sun for the first time. Mersmerized, he continues to gaze at the sun. After a few seconds he realizes he is in pain and looks down. So now his mind learns that something that is “Orange and Bright” hurts and burns his eyes.

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2. Remembering and recalling the pattern

A few days later, that same baby panda comes across a light bulb for the first time. Again he is curious. While he is about to gaze at this light he suddenly remembers that orange and bright means pain and will hurt his eyes. Thus he stops staring at the light and looks away. In simple terms, this is Machine Learning since the panda learned a pattern from its past experiences.

But note that, orange and bright pattern might not always mean pain. What happens when the panda sees an Orange? Sure, his instincts will tell him to stay away, but some curiosity and courage will lead him to learning a new pattern. Say, “Orange and Bright but not hot” means food.

In real world you can always re-train and updated your machine learning model as you collect new data. And as you collect more and more data, the ML model is able to learn new patterns in the data and can generate better insights.

Similar to the baby panda example, our instincts and our knowledge of things and surroundings is the result of years of implicit and explicit observations. For e.g. if the baby panda was to be warned by his mother against staring at the sun, that would be explicit programming not machine learning.

So when do we use machine learning?

Sure, choosing between what food to eat, what movie to watch or making calculations like 2+2 is easy and can be done by the human brain. Sometimes, it is not so easy, like deciding on whether to buy a condo or a house, or calculating the result of 143 x 221. Then there are decisions like which stock to buy or identifying where the next terror attack would take place. These kind of decision making needs analysis and requires complex tools to deal with collecting and processing huge amounts of data. Machine learning operates in this category of decision making which requires processing and analyzing large volumes of data.

Conclusion:

Thank you for reading.

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Machine Learning — How machines learn like other life forms. was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/machine-learning-how-machines-learn-like-other-life-forms-ce0a913fd57b?source=rss—-5e5bef33608a—4

Automated Machine Learning, or AutoML, tries hundreds or even thousands of different ML pipelines to deliver models that often beat the experts and win competitions. But, is this the ultimate goal? Can a model developed with this approach be trusted without guarantees of predictive performance? The issue of overfitting must be closely considered because these methods can lead to overestimation — and the Winner’s Curse.

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XGBoost is a tree based ensemble machine learning algorithm which is a scalable machine learning system for tree boosting. Read more for an overview of the parameters that make it work, and when you would use the algorithm.

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In this last issue of the year learn how to crack SQL interviews, find why and how of MLOps, check top online courses Data Science, and read the predictions for AI, Data Science, and Machine Learning from our panel of experts and a group of innovative companies.

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