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140 Machine Learning Formulas

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Data Science Leadership Exchange: Best Practices for Driving Outcomes Despite an increasing awareness of the role data science plays in successful business outcomes, data science leaders still struggle to organize, implement and communicate effective data science initiatives. Some of the industry's best and brightest from Bayer, S&P Global and Transamerica will be presenting their insights and experiences. Data Science Leadership Exchange: Best Practices for Driving Outcomes Despite an increasing awareness of the role data science plays in successful business outcomes, data science leaders still struggle to organize, implement and communicate effective data science initiatives. Some of the industry's best and brightest from Bayer, S&P Global and Transamerica will be presenting their insights and experiences.


Writing Action Adventure Fiction And Systems Thinking With Nick Thacker

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The life of a full-time independent author involves wearing many hats. You have to balance your time between learning your craft and pleasing readers with great books, as well as publishing, book marketing, and building a business that will support you for the long-term. In today's interview, Nick Thacker talks about the key aspects of action-adventure thrillers as well as how he runs his publishing company and thoughts on pricing, email list building, and creating systems to avoid overwhelm.


Artificial Intelligence in Retail

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Over the last few years, artificial intelligence technology has made some interesting advancements across multiple industries. While it may not be so obvious to the end consumers, artificial intelligence has been applied in the retail sector as well. Even though not every retailer has been using it due to high costs, inaccessibility, and proprietary systems, the largest players in the retailing have been pretty active about it. It's no wonder given the benefits AI can bring to the actual businesses. So how exactly can artificial intelligence help retail store owners?


Distance in Machine Learning -- a prominent element!

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It is no surprise that Machine Learning uses a lot of Mathematics into the implementation of its algorithms and models, and along it with comes some serious coordinate geometry. The coordinate geometry brings with itself distances, and that is what we will address today! Be it Physics, Geography, Nuclear Physics, or any kind of science, the word distance has always been familiar and therefore, we all have a basic understanding of what distance is. It's a numerical measurement of how far two objects or points are. Well, I'm here to give you it of a twist! Your life has been a lie because the distance is not exactly what we know in Machine Learning.


Machine Learning: Decision Trees

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This blog covers another interesting machine learning algorithm called Decision Trees and it's mathematical implementation. At every point in our life, we make some decisions to proceed further. Similarly, this machine learning algorithm also makes the same decisions on the dataset provided and figures out the best splitting or decision at each step to improve the accuracy and make better decisions. This, in turn, helps in giving valuable results. A decision tree is a machine learning algorithm which represents a hierarchical division of dataset to form a tree based on certain parameters.


The perfume makers that can't smell a thing

BBC News

Do you need a human to create a beautiful scent? That's the question being asked as artificial intelligence (AI) starts to infiltrate the perfume industry. Companies are increasingly turning to technology in order to create more bestselling, unique fragrances that can be produced in just minutes. Last year, Swiss-based fragrance developer Givaudan Fragrances launched Carto, an artificial Intelligence-powered tool to help perfumers. Through machine learning (a way computers improve outcomes automatically by learning from past results) Carto can suggest combinations of ingredients.


Statistics and Machine Learning -- When to Use What?

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We live in a world of data explosion where computers are like a commodity, that's why associating almost every problem with trending tech buzzwords like "artificial intelligence", "machine learning", and "deep learning" seems like an avant-garde thing to do. It is almost intuitive and convenient to do so given readily available software and programming libraries on the internet. The most daunting part is probably to pick the suitable one and feed it with your data, then voilà -- here are the results. A search on Google with "machine learning models" swiftly returns you more than 700 million results in less than a second, just to show you how easy it is to gather on the magnitude of availability for ML models but how difficult it is to actually decide on the one that suits you. And then here come the million-dollar questions-- Am I actually selecting the best ML for my use case?


Specific Explanation Multivariate Linear Regression in Python

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Learn to develop a multivariate linear regression for any number of variables in Python from scratch. Linear regression is probably the most simple machine learning algorithm. It is very good for starters because it uses simple formulas. So, it is good for learning machine-learning concepts. In this article, I will try to explain the multivariate linear regression step by step.


Correlation using R explained

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Correlation is the most misunderstood term in the history of Statistics, Machine Learning, and Data Science despite being the fact that it is one of the simplest concepts of statistics. The correlation also denoted as'r' is a measure of association of two variables. It is a statistical technique that helps us determine whether there exists a relationship between two variables and if yes then to what degree (high/ medium/ low). Where x and y are the data points of datasets and x and y stand for means of X and Y. The method of calculating the Kendall correlation is quite similar to the ρ.


Technology Can Help Organizations Reduce Racism, But Will It?

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Ironically, the same innovations we tend to regard as "creepy" (e.g., AI, algorithms, and Big Data) may help leaders make their workplace more inclusive. But there are reasons to be skeptical. I don't consider myself a techno-enthusiast, and I'm definitely not optimistic by nature. So, NO, this isn't another overhyped post on how AI will save the world, or how Big Data (does anyone still use the term?) will make our world better by eliminating racism from society. Sadly, the only way to achieve that would be to eliminate humans, too.