Quantum Metric gets answers from customer data at light speedQuantum Metric gets answers from customer data at light speedFounder & CEO, Quantum Metric

Editor’s note: Today we’re hearing from the founder of Quantum Metric, a digital intelligence platform that analyzes huge amounts of digital customer data to improve the customer experience, enhance sales, and increase loyalty. The company credits a huge leap in innovation—along with a 10-fold increase in business—to their decision to adopt Google Cloud. Here’s more … Read moreQuantum Metric gets answers from customer data at light speedQuantum Metric gets answers from customer data at light speedFounder & CEO, Quantum Metric

Experian: From credit bureau to technology company with APIsExperian: From credit bureau to technology company with APIsAPI Product Manager, Experian

Editor’s note: Today we hear from Dang Nguyen, API Platform Product Owner at Experian, on how the company uses the Apigee API management platform to digitally transform from a traditional credit bureau to a true technology and software provider. Read on to learn how Experian uses APIs to help businesses make smarter decisions and individuals … Read moreExperian: From credit bureau to technology company with APIsExperian: From credit bureau to technology company with APIsAPI Product Manager, Experian

5 Powerful Habits of the Most Productive Teams.

“Never give up. Never stop believing. Never stop fighting!” As a developer with a career spanning across 2 decades, there are two glowing events, which are permanently etched within my memory. · As part of the worst-performing team which was sacked (including me). · As part of the best performing team which won the president’s … Read more5 Powerful Habits of the Most Productive Teams.

Simple Football Data-set Exploration with Pandas

International Football Results From 1872 to 2019 This tutorial article details how the Python Pandas library can be used to explore a data-set efficiently. Specifically, this example will use the data-set, International football results from 1872 to 2019, which is available from the Kaggle website. Chiefly, this tutorial will explore simple visualizations and how they can be … Read moreSimple Football Data-set Exploration with Pandas

Getting on the meet-up bandwagon – our first meet up event

My company Draper and Dash have tasked me with organising a wider meet-up event for anyone who is interested in AI / ML in healthcare. This wider working group consists of people from different sectors, however they are interested in how we can apply AI / ML methods in their organisations. Why did we choose … Read moreGetting on the meet-up bandwagon – our first meet up event

Data Science and Machine Learning Adoption in Middle East Countries | Clevered

Market Trends Recently, the Middle East has seen a swarm of investments pooling in from domestic as well as international markets to boost their efforts in innovation and technological advancements. When Amazon accessed a regional online shopping site ‘Souq.com’ for around $580m, it became a milestone for the technology sector and digital economy in the … Read moreData Science and Machine Learning Adoption in Middle East Countries | Clevered

Natural Language Processing: A Crash Course!

Next word prediction is one of the applications of NLP Natural Language Processing (NLP) is an increasingly important sub-domain of Machine Learning that deals with deriving general understanding through developing various language models from various large corpora of text. In this article, we go over 5 common types of NLP problems and their relevant models: … Read moreNatural Language Processing: A Crash Course!

Building an ecosystem of partners to help broadcasters transform their businessBuilding an ecosystem of partners to help broadcasters transform their businessGlobal Head of Google Cloud Media & Entertainment Partnerships

The cloud has made it possible for audiences to find the content they love anywhere, on any device, and as a result many broadcasters are looking to the cloud to help them grow and meet customer needs. Broadcasters are using the cloud to streamline content management workflows, modernize their video delivery infrastructure, and develop deeper … Read moreBuilding an ecosystem of partners to help broadcasters transform their businessBuilding an ecosystem of partners to help broadcasters transform their businessGlobal Head of Google Cloud Media & Entertainment Partnerships

What Makes the News?

Before attempting any modeling, I pre-processed the data using NLTK’s WordNet Lemmatizer to reduce the words to their base forms. I then tokenized the data using scikit-learn’s CountVectorizer, which generates a basic count of words in the text in a bag-of-words approach. This approach created a sparse matrix of approximately 4,600 unigrams and bigrams. Some … Read moreWhat Makes the News?

Azure Media Services’ new AI-powered innovation

Animated character recognition, multilingual speech transcription and more now available At Microsoft, our mission is to empower every person and organization on the planet to achieve more. The media industry exemplifies this mission. We live in an age where more content is being created and consumed in more ways and on more devices than ever. … Read moreAzure Media Services’ new AI-powered innovation

Industry 4.0: Evolution to knowledge-based enterprises

Focus Area I: Defining purpose of knowledge for the enterprise Challenge: More information, less understanding Possible solution: Mission-performance-value knowledge assessment We are living in a highly information intense environment today. However, for enterprises, not all forms of information and knowledge are relevant. At the highest level, the purpose of knowledge should be derived (or understood) … Read moreIndustry 4.0: Evolution to knowledge-based enterprises

Regex Problem? Here’s an R package that will write Regex for you

REGEX is that thing that scares everyone almost all the time. Hence, finding some alternative is always very helpful and peaceful too. Here’s a nice R package thst helps us do REGEX without knowing REGEX. REGEX This is the REGEX pattern to test the validity of a URL: ^(http)(s)?(\:\/\/)(www\.)?([^\ ]*)$ A typical regular expression contains — Characters … Read moreRegex Problem? Here’s an R package that will write Regex for you

Fitting ‘complex’ mixed models with ‘nlme’: Example #2

Let’s imagine a field experiment, where different genotypes of khorasan wheat are to be compared under different nitrogen (N) fertilisation systems. Genotypes require bigger plots, with respect to fertilisation treatments and, therefore, the most convenient choice would be to lay-out the experiment as a split-plot, in a randomised complete block design. Genotypes would be randomly … Read moreFitting ‘complex’ mixed models with ‘nlme’: Example #2

Social Network Visualization with R

In this month’s we are going to look at data analysis and visualization of social networks using R programming. Social Networks – Data Visualization Friendster Networks Mapping Friendster was a yesteryear social media network, something akin to Facebook. I’ve never used it but it is one of those easily available datasets where you have a … Read moreSocial Network Visualization with R

A Minimalist End-to-End Scrapy Tutorial (Part IV)

Systematic Web Scraping for Beginners Photo by Paweł Czerwiński on Unsplash Part I, Part II, Part III, Part IV In the previous three parts, you have developed a spider that extracts quote information from http://quotes.toscrape.com and stores the data into a local SQLite database. In this part, I will show you how to deploy the … Read moreA Minimalist End-to-End Scrapy Tutorial (Part IV)

The What, Why, and How of Sankey Diagrams

Step One: Design First, solidify your purpose and the most important take-away for your audience. To avoid wasting time rebuilding your diagram or building an ineffective Sankey diagram, here are some questions I would recommend asking yourself before you start: Are you using this Sankey for exploratory data analysis? Are you using it to tell … Read moreThe What, Why, and How of Sankey Diagrams

Train and deploy the Mighty Transformer NLP models using FastBert and AWS SageMaker

In my earlier introduction to FastBert, I described it as a library that will allow developers and data scientists to train and deploy BERT based models for NLP tasks beginning with Text Classification. The scope of BERT (read Transformers) based models have widened a bit since I wrote my earlier blog and includes BERT, XLNet, … Read moreTrain and deploy the Mighty Transformer NLP models using FastBert and AWS SageMaker

Throwing dice with maximum entropy principle

“nobody knows what entropy really is, so in any discussion you will always have an advantage” von Neunmann Sounds as a good reason to dive into the meaning of entropy. This post is all about dice and maximum entropy. Photo by Jonathan Petersson on Unsplash The post has four parts. In the first part, I … Read moreThrowing dice with maximum entropy principle

What is a Deepfake and Why Should You Care?

Deepfakes are the result of a generative adversarial network (GAN) and unsupervised machine learning. After getting initial data, the computer teaches itself henceforth, using a Generator and a Discriminator. The generator creates the initial fake image, audio, or video and sends it over to the Discriminator for examination. If it determines the image is fake, … Read moreWhat is a Deepfake and Why Should You Care?

Automate Data Cleaning with Unsupervised Learning

Cleaning text for your NLP projects has never been so fun and easy! I like working with textual data. As for Computer Vision, in NLP nowadays there are a lot of ready accessible resources and opensource projects, which we can directly download or consume. Some of them are realy cool and permit us to speed … Read moreAutomate Data Cleaning with Unsupervised Learning

Instacart Market Basket Analysis Part 1: Which Grocery Items Are Popular?

Exploratory Data Analysis of Instacart orders via Saturn Cloud As a Data Scientist, a big part of my responsibility is sharing my results with the business stakeholders. As a Data Journalist, a big part of my role is sharing code chunks with my readers. Now the reports that I share need to be rendered properly … Read moreInstacart Market Basket Analysis Part 1: Which Grocery Items Are Popular?

Hierarchical Neural Architecture Search

Many researchers and developers are interested in what Neural Architecture Search can offer their Deep Learning models, but are deterred by monstrous computational costs. Many techniques have been developed to promote more efficient search, notably Differentiable Architecture Search, parameter sharing, predictive termination, and hierarchical representations of architectures. This article will explain the idea of hierarchical … Read moreHierarchical Neural Architecture Search

Survival analysis with strata, clusters, frailties and competing risks in in Finalfit

[This article was first published on R – DataSurg, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Background In healthcare, we deal with a lot of binary outcomes. … Read moreSurvival analysis with strata, clusters, frailties and competing risks in in Finalfit

What to expect from a causal inference business project: an executive’s guide III

Part III: Where causal inference stands in the current AI, Big Data, Data Science, Statistics, and Machine Learning scene? This is the third part of the post “What to expect from a causal inference business project: an executive’s guide”. You will find the second one here. Most of these words have fuzzy meaning, at least … Read moreWhat to expect from a causal inference business project: an executive’s guide III

What to expect from a causal inference business project: an executive’s guide II

Part II: Which are the project key points you need to know This is the second part of the post “What to expect from a causal inference business project: an executive’s guide”. You will find the third part here. Casual inference models how variables affect each other. Based on this information, uses some calculation tools … Read moreWhat to expect from a causal inference business project: an executive’s guide II

What to expect from a causal inference business project: an executive’s guide I

Part I: When do you need casual inference? This is the fifth post on a series about causal inference and data science. The previous one was “Solving Simpson’s Paradox”. You will find the second part of this post here. Causal inference is a new language to model causality to help understand better causes and impacts … Read moreWhat to expect from a causal inference business project: an executive’s guide I

Introduction to Web Scraping with Selenium And Python

Practical tutorial on how to get started with Selenium Web scraping is a fast, affordable and reliable way to get data when you need it. What is even better, the data is usually up-to-date. Now, bear in mind that when scraping a website, you might be violating its usage policy and can get kicked out … Read moreIntroduction to Web Scraping with Selenium And Python

Temporal-Difference learning

Reinforcement Learning using Temporal Difference Learning In this article I will cover Temporal-Difference Learning methods. Temporal-Difference(TD) method is a blend of Monte Carlo (MC) method and Dynamic Programming (DP) method. Below are key characteristics of Monte Carlo (MC) method: There is no model (agent does not know state MDP transitions) agent learn from sampled experience … Read moreTemporal-Difference learning

Expanded Azure Maps coverage, preview of Azure Maps feedback site, and more

This blog post was co-authored by Ricky Brundritt, Principal Technical Program Manager, Azure Maps. Azure Maps services continue to expand our support for Microsoft enterprise customers’ needs in Azure. And, we’ve been busy expanding our capabilities. Today we’re announcing Azure Maps is now available in Argentina, India, Morocco, and Pakistan. We have also launched a … Read moreExpanded Azure Maps coverage, preview of Azure Maps feedback site, and more

Monte Carlo Learning

Reinforcement Learning using Monte Carlo Method In this article I will cover Monte Carlo Method of reinforcement learning. I have briefly covered Dynamic programming (Value Iteration and Policy Iteration) method in earlier article. In Dynamic programming we need a model(agent knows the MDP transition and rewards) and agent does planning (once model is available agent … Read moreMonte Carlo Learning

How to master Python’s main data analysis library in 20 Minutes

Image by xresch from Pixabay Now that we are comfortable with filtering and sorting the data front to back and vice versa, let’s move to some more advanced analytical functionalities. Standard Functions: Like the read functions, there are also a lot of analytical functions implemented in Pandas. I will highlight and explain the ones I … Read moreHow to master Python’s main data analysis library in 20 Minutes

Use more of your data with matrix factorisation

Previously I posted on how to apply gradient descent on linear regression as an example. With that as background it’s relatively easy to extend the logic to other problems. One of those is matrix factorisation. There are many ways to factorise a matrix into components such as PCA, singular value decomposition (SVD), but one way … Read moreUse more of your data with matrix factorisation

Mastering the art of web scraping with Selenium and Python [Part 2/2]

Selenium is a powerful tool for advanced interactions with websites: login, clicks… Let’s use it for web scraping Alright let’s do something ‘simple’ here: collect all the artists available on Spotify. That’s a robot scrolling through Spotify’s catalog of artists ⚠️Obviously, I need to put a disclaimer here ⚠️Don’t use this method to resell data … Read moreMastering the art of web scraping with Selenium and Python [Part 2/2]

12 Best AI & ML Based App Ideas that’ll Make Money in 2020

According to recent research of PWC, 72% of business leaders said they believe AI is going to be fundamental in the future and they termed it a “business advantage”. No doubt, AI is one of the most crucial future technology which is being opted by many businesses — small or big — rapidly. 12 Best … Read more12 Best AI & ML Based App Ideas that’ll Make Money in 2020

Classifying pregnancy test results

My first attempt at Lesson 2 of “Practical Deep Learning for Coders” by fast.ai I’m a math adjunct and aspiring data scientist working through the “Practical Deep Learning for Coders” course by fast.ai (you can read about my experience with Lesson 1 here), and for Lesson 2, we’re to gather a set of images from … Read moreClassifying pregnancy test results

A guide for selecting an appropriate metric for your A/B test

And avoiding the common mistakes that derail most test efforts. This article is the 3rd one in my series of articles about A/B Testing. In the first article, I presented the intuition behind A/B testing and the importance of establishing the magnitude of the effect you hope to observe and corresponding sample size. In the … Read moreA guide for selecting an appropriate metric for your A/B test

5 Best Practices for AI- and Data-Driven Call Centers

Call centers have been revolutionized in the past decade. While some static call scripts and one-size-fits-all strategies still remain, technology has drastically changed the way call centers are capable of functioning. Today, call centers have the unique ability to leverage all available data to drive each customer interaction. These data sources include which digital marketing … Read more5 Best Practices for AI- and Data-Driven Call Centers

Call for Help: Lead R/Shiny Developer

Dear Fantasy Football Analytics Community, In 2013, we at Fantasy Football Analytics released web apps to help people make better decisions in fantasy football based on the wisdom of the crowd. Over the past six years, the community response has been incredibly supportive, and we continually improved the apps in response to user feedback. The … Read moreCall for Help: Lead R/Shiny Developer