An Introduction to #AI - updated for 2020; Free From MIT: Intro to Computer Science and Programming in Python; The Most Complete Guide to #PyTorch for Data Scientists; (Good) Data Cleaning is just reusable Data Transformations
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.
How are the fields of Data Analytics and Data Science related? Read this post by John Thompson, author of the new Packt book "Building Analytics Teams" to gain an understanding of the link between the two.
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
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.
Also: Online Certificates/Courses in #AI, #BusinessAnalytics, #DataScience, #MachineLearning from Top Universities; 24 Best (and #Free) #Books To Understand #MachineLearning; New Poll: What Python IDE / Editor you used the most in 2020?; Mathematics for #MachineLearning: The #Free eBook
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.
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.
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.
Also: Statistics with Julia: The Free eBook; Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities; Autograd: The Best Machine Learning Library You're Not Using?; Implementing a Deep Learning Library from Scratch in Python
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.
Coursera's Machine Learning for Everyone (free access) fulfills two different kinds of unmet learner needs, for both the technology side and the business side, covering state-of-the-art techniques, business leadership best practices, and a wide range of common pitfalls and how to avoid them.
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.
Will You Enroll At #Google University For $49/Month? On @Kaggle some prizes are only for Americans - here are international alternatives; Advanced #NumPy for #DataScience; Free From MIT: Intro to Computer Science and Programming in Python
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.
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.
The Big Data and AI Toronto Conference and Expo returns on September 29-30, 2020 with a brand new format and will be held exclusively online. KDnuggets readers get a 25% discount on all-access passes with promo code BDTORONTO-25. Register now.
Also: Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills; AI Papers to Read in 2020; A Deep Learning Dream: Accuracy and Interpretability in a Single Model; Creating Powerful Animated Visualizations in Tableau; 8 AI/Machine Learning Projects To Make Your Portfolio Stand Out
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.
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.
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.
Also: Tensorbook, a #DeepLearning laptop; 5 Concepts Every #dataScientist Should Know: Multicollinearity, encoding, sampling, error, and storytelling; Top 20 #Python AI and #MachineLearning #OpenSource Projects; How To Decide What Data Skills To Learn
Eric Siegel's new course series on Coursera, Machine Learning for Everyone, is for any learner who wishes to participate in the business deployment of machine learning. This end-to-end, three-course series is accessible to business-level learners and yet vital to techies as well. It covers both the state-of-the-art techniques and the business-side best practices.
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.
Video analytics that could save lives and property are just out of reach. A new prize challenge, Enhancing Computer Vision for Public Safety, is designed to help develop a new line of research that will bring such tools closer to reality.
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.
Join Immuta and the COVID-19 Alliance, a non-profit organization of MIT, for this virtual workshop on Sep 23 @ 1 PM ET, to learn how you can use legal automation to easily scale your data analytics compliance strategy. Register now.
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?
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.
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.
Also: Full Stack #DeepLearning course; Guide to Intelligent #DataScience book - updated/expanded content throughout, now an even better basis for the "educational" part of "becoming a successful data scientist"; Completely Free #MachineLearning Reading List by @vickdata; A Complete #DataScience Portfolio Project
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.
PyCaret is an open-source, low-code machine learning library in Python that automates the machine learning workflow. It is an end-to-end machine learning and model management tool that speeds up the machine learning experiment cycle and makes you 10x more productive. Read about what's new in PyCaret 2.1.
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.