The AI innovation in healthcare has been overwhelming with the Global Healthcare AI Market accounting for $0.95 billion in 2017, and is expected to reach $19.25 billion by 2026. What drives this vibrant growth?
Read this article on machine learning model deployment using serverless deployment. Serverless compute abstracts away provisioning, managing severs and configuring software, simplifying model deployment.
Detecting and handling missing values in the correct way is important, as they can impact the results of the analysis, and there are algorithms that can’t handle them. So what is the correct way?
While Kaggle might be the most well-known, go-to data science competition platform to test your skills at model building and performance, additional regional platforms are available around the world that offer even more opportunities to learn... and win.
In this blog, we look at a disruptive AI program - Morpheus - developed by Researchers at UC Santa Cruz that can analyze astronomical image data and classify galaxies and stars with surgical precision. If you're reading this with "starry" eyes, we bet we've got you hooked.
Also: How I Consistently Improve My Machine Learning Models From 80% to Over 90% Accuracy; I'm a Data Scientist, Not Just The Tiny Hands that Crunch your Data; New Poll: What Python IDE / Editor you used the most in 2020?; The Most Complete Guide to PyTorch for Data Scientists
When your data includes geographical information, rich map visualizations can offer significant value for you to understand your data and for the end user when interpreting analytical results.
Flask is a straightforward and lightweight web application framework for Python applications. This guide walks you through how to write an application using Flask with a deployment on Heroku.
Here's another free eBook for those looking to up their skills. If you are seeking a resource that exhaustively treats the topic of causal inference, this book has you covered.
Data that is updated in real-time requires additional handling and special care to prepare it for machine learning models. The important Python library, Pandas, can be used for most of this work, and this tutorial guides you through this process for analyzing time-series data.
Data science work typically requires a big lift near the end to increase the accuracy of any model developed. These five recommendations will help improve your machine learning models and help your projects reach their target goals.
In this article, we will focus on various machine learning, deep learning models, and applications of AI which can pave the way for a new data-centric era of discovery in healthcare.
Getting started in deep learning – and adopting an organized, sustainable, and reproducible workflow – can be challenging. This blog post will share some tips and tricks to help you develop a systematic, effective, attainable, and scalable deep learning workflow as you experiment with different deep learning models, datasets, and applications.
The latest KDnuggets polls asks which Python IDE / Editor you have used the most in 2020. Participate now, and share your experiences with the community.
If you are looking for a machine learning starter that gets right to the core of the concepts and the implementation, then this new free textbook will help you dive in to ML engineering with ease. By focusing on the basics of the underlying algorithms, you will be quickly up and running with code you construct yourself.
If EDA is not executed correctly, it can cause us to start modeling with “unclean” data. See how to use Pandas Profiling to perform EDA with a single line of code.
Looking at data one way can tell one story, but sometimes looking at it another way will tell the opposite story. Understanding this paradox and why it happens is essential, and new tools are available to help automatically detect this tricky issue in your datasets.
Can a do-it-all Data Scientist really be more effective at delivering new value from data? While it might sound exhausting, important efficiencies can exist that might bring better value to the business even faster.
Domino Data Lab was announced as a leader for the second year in a row in the recently released “Forrester Wave™: Notebook-based Predictive Analytics and Machine Learning (PAML), Q3 2020” analyst report. True to our data science roots, we’ve built a Maslow’s hierarchy of data science team needs.
In this article, we will talk about a Do-It-Yourself approach towards election analysis and coming to a conclusion whether the elections were conducted fairly or not.
More applications are being infused with machine learning while MLOps processes and best practices are becoming well established. Critical to these software and systems are the servers that run the models, which should feature key capabilities to drive successful enterprise-scale productionizing of machine learning.
Inspired by another concise data visualization, the author of this article has crafted and shared the code for a heatmap which visualizes the COVID-19 pandemic in the United States over time.
In the field of deep learning, there continues to be a deluge of research and new papers published daily. Many well-adopted ideas that have stood the test of time provide the foundation for much of this new work. To better understand modern deep learning, these techniques cover the basic necessary knowledge, especially as a starting point if you are new to the field.
This free eBook is a draft copy of the upcoming Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. Interested in learning Julia for data science? This might be the best intro out there.
Also: Netflix's Polynote is a New Open Source Framework to Build Better Data Science Notebooks; Must-read NLP and Deep Learning articles for Data Scientists.
Data feeds machine learning models, and the more the better, right? Well, sometimes numerical data isn't quite right for ingestion, so a variety of methods, detailed in this article, are available to transform raw numbers into something a bit more palatable.
Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer. Save 50% with code kdmath50.
As a novice or seasoned Data Scientist, your work depends on the data, which is rarely perfect. Properly handling the typical issues with data quality and completeness is crucial, and we review how to avoid six of these common scenarios.
The fields of Big Data, Data Analytics/Science, and Data Integration need to face a new truth: We are drowning in data, more and more so every second of every day.
This free introductory computer science and programming course is available via MIT's Open Courseware platform. It's a great resource for mastering the fundamentals of one of data science's major requirements.
If you are just starting down a path toward a career in Data Science, or you are already a seasoned practitioner, then keeping active to advance your experience through side projects is invaluable to take you to the next professional level. These eight interesting project ideas with source code and reference articles will jump start you to thinking outside of the box.
We analyze the results of the Data Science Skills poll, including 8 categories of skills, 13 core skills that over 50% of respondents have, the emerging/hot skills that data scientists want to learn, and what is the top skill that Data Scientists want to learn.
Also: How to Evaluate the Performance of Your Machine Learning Model; Which methods should be used for solving linear regression?; A Curious Theory About the Consciousness Debate in AI; If I had to start learning Data Science again, how would I do it?; PyCaret 2.1 is here: Whats new?
In this post we explore animated data visualization in Tableau,one of the tool's powerful features for making visualizations appealing and interactive.
With so much disruption in 2020 already, a recent report by Burtch Works looks ahead to next year and beyond, and shares insights about how today's hiring market trends may impact our work lives for years to come.
We tend to think it's all about the data. However, for real data science projects at real organizations in real life, there are more fundamental aspects to consider to do data science right.
Get a free book chapter from "The Art of Statistics: Learning from Data" by a leading researcher Sir David John Spiegelhalter. This excerpt takes a forensic look at data surrounding the victims of the UK most prolific serial killer and shows how a simple search for patterns reveals critical details.
You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.
This is an overview of a great computer vision resource from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.
As a foundational set of algorithms in any machine learning toolbox, linear regression can be solved with a variety of approaches. Here, we discuss. with with code examples, four methods and demonstrate how they should be used.
We provide an updated list of best online Masters in AI, Analytics, and Data Science, including rankings, tuition, and duration of the education program.
The focus of this blog is to bring to light that continued software optimizations can boost performance not only for the latest platforms, but also for the current install base from prior generations. This means customers can continue to extract value from their current platform investments.
The Best Free Data Science eBooks: 2020 Update
Introduction to Time Series Analysis in Python
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