Smart homes and cities, Industry 4.0, drones and more
Have you ever wondered how our life in the future may look like? What technology will bring us? How will it influence our lifestyle? Let us look at one possible scenario.
Smart Homes
It’s Saturday morning, automatic window curtains open when your alarm clock goes off. You open your eyes and your voice assistant Amazon Alexa welcomes you and briefly summarizes your today’s schedule. Meantime a smart cafe machine starts preparing your favorite café Americano and a toast roaster heats two slices of bread for your breakfast. When you go under the digital shower the intelligent system remembers your preferred temperature, plays morning music in the background and later during teeth brushing a magic mirror displays emails you received overnight alongside with news highlights, plus the current weather forecast. …
Learn how to maximize the chances of getting a work offer by optimizing the number of interviews per month using a statistical approach and a bit of Python.
On a job market, every job-seeker tries to maximize their chances of a successful outcome (getting a work offer). One of the popular discussion topics between themselves and job advisors is a recommended number of active applications and job interview per month. Having it too small means not giving yourself enough opportunities/chances for the success but also having too many appointment means not being able to properly prepare for them.
So how to strike a happy medium? So here’s where a particular statistical approach comes to play - thanks to a binomial distribution and some assumptions about ourselves we can optimize the number of interviews per month. …
It is not always necessary to run an optimization algorithm to perform linear regression. You can solve a specific algebraic equation — the normal equation — to get the results directly. Although for big datasets it is not even close to being computationally optimal, it‘s still one of the options good to be aware of.
1. Introduction
Linear regression is one of the most important and popular predictive techniques in data analysis. It’s also one of the oldest - famous C.F. Gauss at the beginning of 19th-century was using it in the astronomy for calculation of orbits (more).
Its objective is to fit the best line (or a hyper-/plane) to the set of given points (observations) by calculating regression function parameters that minimize specific cost function (error), e.g. mean squared error (MSE).
As a reminder, below there is a linear regression equation in the expanded form.
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