As a data scientist writing code for your models, it's quite possible that your work will make its way into a production environment to be used by the masses. But, writing code that is deployed as software is much different than writing code for exploratory data analysis. Learn about the key approaches for making your code production-ready that will save you time and future headaches.
This week's free eBook is a classic of data science, An Introduction to Statistical Learning, with Applications in R. If interested in picking up elementary statistical learning concepts, and learning how to implement them in R, this book is for you.
This is a slightly more intricate example of MCMC, compared to many with a fairly simple model, a single predictor (maybe two), and not much else, which highlights a couple of issues and tricks worth noting for a handwritten implementation.
Here is a selection of courses for those interested in diversifying their domain knowledge into the related realms of economics and finance, with the goal of being able to apply your data science skills to these domains.
By using R, Flexdashboard and Leaflet, we can build a customized and branded web application to showcase location based data interactively across the organization. Instead of crowding the application with many widgets, we use menu tabs and pages to separate the interactive aspects.
When we consider the complexity of an algorithm, we shouldn’t really care about the exact number of operations that are performed; instead, we should care about how the number of operations relates to the problem size.
This article investigates TensorFlow components for building a toolset to make modeling evaluation more efficient. Specifically, TensorFlow Datasets (TFDS) and TensorBoard (TB) can be quite helpful in this task.
Those interested in studying AI bias, but who lack a starting point, would do well to check out this introductory set of slides and the accompanying talk on the subject from Google researcher Margaret Mitchell.
Just as there is no Data Science without data, there's no science in data without mathematics. Strengthening your foundational skills in math will level you up as a data scientist that will enable you to perform with greater expertise.
This work explores how genetic relationships can be exploited alongside genomic information to predict genetic traits with the aid of graph machine learning algorithms.
modelStudio is an R package that automates the exploration of ML models and allows for interactive examination. It works in a model agnostic fashion, therefore is compatible with most of the ML frameworks.
PyTorch is a constantly developing deep learning framework with many exciting additions and features. We review its basic elements and show an example of building a simple Deep Neural Network (DNN) step-by-step.
LightGBM is a histogram-based algorithm which places continuous values into discrete bins, which leads to faster training and more efficient memory usage. In this piece, we’ll explore LightGBM in depth.
This post takes you through the basic steps for creating a cloud-based deep learning dog classifier, with everything accomplished from the AWS Management Console.
Classification is a core technique in the fields of data science and machine learning that is used to predict the categories to which data should belong. Follow this learning guide that demonstrates how to consider multiple classification models to predict data scrapped from the web.
Computer vision has tremendous promise for improving crop monitoring at scale. We present our learnings from building such models for detecting stem and wheat rust in crops.
The Youtube videos on this list cover concepts such as what machine learning is, the basics of natural language processing, how computer vision works, and machine learning in video games.
Google Colab is a widely popular cloud service for machine learning that features free access to GPU and TPU computing. Follow this detailed guide to help you get up and running fast to develop your next deep learning algorithms with Colab.
There is a quick and easy way to perform preprocessing on mixed feature type data in Scikit-Learn, which can be integrated into your machine learning pipelines.
Time to get back to basics. This week we have a look at a book on foundational machine learning concepts, Understanding Machine Learning: From Theory to Algorithms.
In this tutorial, we will use a previously-built machine learning pipeline and Flask app to demonstrate how to deploy a machine learning pipeline as a web app using the Microsoft Azure Web App Service.
Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work. Learn more about five biases that can all too easily make your seemingly objective work become surprisingly subjective.
In this blog, I am demonstrating how to convert speech to text using Python. This can be done with the help of the “Speech Recognition” API and “PyAudio” library.
With so many types of data distributions to consider in data science, how do you choose the right one to model your data? This guide will overview the most important distributions you should be familiar with in your work.
Data pre-processing is not only the largest time sink for most Data Scientists, but it is also the most crucial aspect of the work. Learn more about training data and data processing tasks from 5 leading academic papers.
This article jumps into the latest skill set observations in the Data Engineering Job Market which could definitely add a boost to your existing career or assist you in starting off your Data Engineering journey.
Natural language processing (NLP) is increasingly used to review unstructured content or spot trends in markets. How is Refinitiv Labs applying NLP in financial services to meet challenges around investment decision-making and risk management?
This article is about the story of taking effective business decisions basis a combined model. Let us together study how these components work hand in hand.
If you are interested in a top-down, example-driven book on deep learning, check out the draft of the upcoming Deep Learning for Coders with fastai & PyTorch from fast.ai team.
4 Free Math Courses to do and Level up your Data Science Skills
A Complete guide to Google Colab for Deep Learning
Easy Speech-to-Text with Python
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