Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Nassim Taleb is a former financial derivatives trader and probability researcher, his book'The Black Swan: The Impact of the Highly Improbable' highlights how highly improbable events impact our daily life and financial markets.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. So in this post, we will discuss some of the essential intermediate SQL queries for data professionals.

Seek AI launched today a cloud-based AI platform that automates some of the repetitive work that data professionals perform. Thy are often asked by business users to write new code to query a database to answer ad-hoc questions. Using generative AI like DALL-E, Stable Diffusion or GPT-3, Seek AI automates this process, improving the productivity of data professionals. Business users can access Seek AI's natural language interface by means of email, Slack, text, and a range of customer relationship management (CRM) systems. In an interview with Authority Magazine, Seek AI co-founder and CEO Sarah Nagy highlighted the challenge of managing the tradeoff between data accuracy and accessibility: "On one hand, accessibility allows less technical folks to start interacting with the knowledge wellspring that is a company's data. On the other hand, what good is a wellspring of polluted water (i.e. The Talend second annual Data Health Barometer, based on a recent worldwide survey of 900 data ...

In the global tech market, cutting-edge technologies like artificial intelligence, neural net, machine learning, and data analytics are flourishing. ML professionals need cheatsheets to get a deeper understanding of the details. It is not easy to grasp these technologies in a short time. Advanced mechanisms make datasets and machinery concepts more complicated. ML cheatsheets, data analysis cheatsheets, and neuron cheatsheets are all necessary to be successful in this highly competitive market.

What is the most intuitive, efficient, and least mentally draining way to ask a question? It is using the simplest words possible in your own language. Modern search engines such as Google has made searching for information online using simple sentences commonplace. This had helped create our modern society and improved access to information globally; it's hard to overstate how transformational the advent of the search engine truly was. However, searching for information on the internet didn't truly become democratized and popular until we could ask the internet questions using natural language in the same way we would talk to another person.

Cutting-edge technologies such as artificial intelligence, neural network, data analytics, and machine learning (ML) are thriving in the global tech market. There are so many minute details to remember for AI, data, and ML professionals that they need cheat sheets for the deeper understanding. Yes, it is overwhelming to get a grip on these technologies within a short period of time. Datasets and machinery concepts are complicated with more advanced mechanisms. ML cheat sheets, data analytics cheat sheets, and neural network cheat sheets are necessary to look out for help to become successful in this highly competitive market. Thus, let's explore some of the top ten cheat sheets for neural networks, data analytics, and ML to work on in 2022.

Data science offers all the opportunities of the hottest profession to the current tech-savvy generation, who are highly interested to work in data science than traditional engineering careers. Data analytics courses are in huge demand among the courses for data professionals. Students can access multiple online data science courses on multiple educational platforms having collaborations with reputed educational institutes. Courses on data science are providing a sufficient and deep understanding of all key concepts and hands-on experience with real-life projects to candidates. Let's explore some of the top trending data science courses in 2022 to take up to become a competent data science professional.

As the world of Data Science progresses, more engineers and professionals need to deploy their work. Whether it's to test, to obtain user input, or simply to demostrate the model capabilites, it's becoming fundamental for data professionals to know the best ways to deploy their models. Moreover, being able to deploy models will not only help the data science field become more versatile and in-demand, but it will also benefit the development and ops teams, transforming you into a key player in your workplace. So, are you ready to jump in and learn how to use the most powerful web development technologies and boost your data science career? Learn how to take a Data Science or Machine Learning model and deploy it to a Web App and API using some of the most in-demand and popular technologies, including Flask, NodeJS, and ReactJS. Get ready to take a DS model and deploy it in a practical and hands-on manner, simulating a real-world scenario that can be applied to industry practices.

Today we are excited to announce our most anticipated feature ever. Model upload allows data scientists to upload trained Scikit-Learn models and automate their workflows to send the output to business applications quickly. According to TechRepublic, 52% of data professionals say they have trouble demonstrating the impact data science has on business outcomes, and half of all Big Data projects fail, according to Gartner.com. The leading causes are the difficulty to put models into production, security, and organization. However, it turns out that building a model is just the beginning of the journey in terms of a Machine Learning workflow.

Many of us (myself included) have felt discouraged from using Bayesian statistics for analysis. Supposedly, Bayesian statistics has a bad reputation: it is difficult and heavily dependent on math. Also, because of its relevance to many fields, Data Science included, writers and professionals, want to get a head start by publishing articles on how the formula works. I believe data professionals, academics, existing books, and online courses are responsible for creating the negative stereotype of Bayes' hard work. We can all agree that not everyone is attracted to mathematical formulas.