Continental develops pioneering technologies and services for sustainable and connected mobility of people and their goods. Founded in 1871, the technology company offers safe, efficient, intelligent and affordable solutions for vehicles, machines, traffic and transportation. In 2020, Continental generated sales of €37.7 billion and currently employs more than 192,000 people in 58 countries and markets. The Autonomous Mobility and Safety business area develops, produces and integrates active and passive safety technologies and controls vehicle dynamics. The product portfolio ranges from electronic and hydraulic brake and chassis control systems to sensors, advanced driver assistance systems, airbag electronics and sensors, electronic air suspension systems and cleaning systems for windscreens and headlights.

We have all seen movies depicting AI as a threat. In Avengers 2, Ironman created Ultron so that it can help avengers to defend people from enemies. But Ultron went out of control and wanted to dominate people, becoming an enemy. But as a professional machine learning engineer, I do not think AI will dominate us. There are a lot of pessimistic opinions making you think that AI and machine learning are turning modern society into a dystopia.

Applications of artificial intelligence are relentlessly growing, touching every aspect of business operations and society. Today, AI has become integral to diverse industries and revolutionized the way organizations perform and make decisions. As business leaders and innovators race to reach the promise of this disruptive technology to gain a competitive edge, job opportunities in AI are in high demand. This growing need of AI experts from businesses will significantly disrupt economic activity in a big way. As AI has a high learning curve, succeeding in this field requires germane skills and knowledge, and investment of time and energy.

Artificial Intelligence continues to evolve through businesses across diverse industries, opening opportunities for organisations to operate and drive values to customers. From managing global supply chains to optimising delivery routes, the technology is augmenting its dominance in today's digital world. This is why, AI is in high demand as businesses, be it small or large, are seeking to garner a competitive edge. According to reports, demand for AI jobs continues to increase, and with more open jobs than qualified candidates to fill them, many AI-related roles rule high salaries. Similar to every country in the world, India is also racing to gain AI's potential benefits that could be game-changing.

It seems Amazon may be putting profits before customers. The Wall Street Journal has reported that the retail giant tweaked its product-search algorithms in order to favor its own'private label' and higher profit margin products– instead of what is most relevant for consumers. Programmers involved with the search algorithm are said to have opposed the change, as Amazon's principles stress they'work to earn and keep customer trust'. The changes were cited by sources familiar with the situation, who claimed Amazon's product-search system was changed last year. The Wall Street Journal has reported that the retail giant tweaked its product-search algorithms in order to favor its own'private label' and higher profit margin products– instead of what is most relevant for consumers Prior to the switch, algorithms would first show products that were bestsellers or relevant to what customers were looking to purchase.

In one of my job interviews for a data scientist position, I was given a home assignment I'd like to share with you. The interviewer sent me a CSV file containing samples of measured quantities x and y, where y is a response variable which can be written as an explicit function of x. It is known that the technique used for measuring x is twice as better than that for measuring y in the sense of standard deviation. Here are all the imports I'll need: It clearly looks like linear regression case. First I'll manually remove the outliers: I'll use LinearRegression to fit the best line: If you're not familiar with the linear regression assumptions, you can read about it in the article Going Deeper into Regression Analysis with Assumptions, Plots & Solutions.

H2O.ai builds an open source parallel distributed in-memory machine learning platform which allows customers to quickly build high-performance, sophisticated ML models on terabytes of data. The server is implemented in Java with the expertise of our technical founder, who wrote the HotSpot server JIT for JavaSoft. The ML algorithms are developed in consultation with our three Stanford advisors, Trevor Hastie, Rob Tibshirani and Stephen Boyd. The platform is accessible via R, Python, Scala, Java, a REST API and a notebook-style web interface. Our paying customer list includes many of the largest insurance companies, banks and healthcare companies, many of the big name unicorns in tech, startups, etc..