Gradient boosted decision trees involves implementing several models and aggregating their results. These boosted models have become popular thanks to their performance in machine learning competitions on Kaggle. In this article, we’ll see what gradient boosted decision trees are all about.
We are amidst a healthcare data explosion. AI/ML will be more vital than ever in the prevention and handling of future pandemics. Here, we walk you through the different facets of modeling infectious diseases, focusing on influenza and COVID-19.
Still haven't come across enough quality contemporary natural language processing resources? Here is yet another freely-accessible offering from a top-notch university that might help quench your thirst for learning materials.
Data is beautiful, and lots of data is simply sublime, but be wary of the pitfalls. Sometimes you have so much data you can waste hours exploring without answering the important questions. These 5 tips will show you how to analyse large complex datasets productively by constraining yourself.
The NLP Index is a brand new resource for NLP code discovery, combining and indexing more than 3,000 paper and code pairs at launch. If you are interested in NLP research and locating the code and papers needed to understand an implement the latest research, you should check it out.
Every one of us needs a resume to showcase our skills and experience but how much effort are we putting into it to make it impactful. It is undeniable that resumes play a key role in our job application process. This article will explore some simple strategies to significantly improve the presentation as well as the content of data science resumes.
Podcasts, especially those featuring interviews, are great for learning about the subfields and tools of AI, as well as the rock stars and superheroes of the AI world. Here, we highlight some of the best podcasts today that are perfect for both those learning about machine learning and seasoned practitioners.
Do you have an interest in data science but lack an understanding of what, exactly, it can be used to accomplish in the real world? Read this article for a few examples of just how helpful data science can be for predicting and preventing real world problems.
KDD 2021, the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) flagship conference, will take place virtually Aug 14-18.
As two very popular tech roles for 2021, the Data Scientist and Machine Learning Engineer can overlap or be entirely distinct, depending on the organization you work for. However, general differences between these positions require certain skill sets that you must be prepared for when applying for jobs.
Also: How to ace A/B Testing Data Science Interviews; Top 10 Must-Know Machine Learning Algorithms for Data Scientists – Part 1; The Most In-Demand Skills for Data Scientists in 2021; Free From Stanford: Machine Learning with Graphs
Join this webinar, May 6 @ 2PM ET, to discover how Yum! Brands and other organizations are leveraging location-based data to boost in-app location accuracy, increase in-store foot traffic, and expand ecommerce business.
What is Data Science really about? Is it the data, or the algorithms, or something else? Similar foundational philosophical struggles exist with other scientific fields, including computer science, and maybe we can look to these resolutions to better understand the true 'meaning' of data science.
The author shares the 3 top challenges faced as they led and established a data & analytics function, as well as ways in which these challenges were addressed. How have you solved the one challenge which has remained elusive to the author?
Thriving as a data professional is about more than just making good money! It’s about FULFILLMENT & IMPACT. In this article, I will help you discover the BEST data role for you given your unique skill sets, personality & goals.
Certain industries, such as medicine and finance, are sensitive to false positives. Using human input in the model inference loop can increase the final precision and recall. Here, we describe how to incorporate human feedback at inference time, so that Machines + Humans = Higher Precision & Recall.
Edge cases occur for three basic reasons: Bias – the ML system is too ‘simple’; Variance – the ML system is too ‘inexperienced’; Unpredictability – the ML system operates in an environment full of surprises. How do we recognize these edge cases situations, and what can we do about them?
Understanding the process of A/B testing and knowing how to discuss this approach during data science job interviews can give you a leg up over other candidates. This mock interview provides a step-by-step guide through how to demonstrate your mastery of the key concepts and logical considerations.
New to data science? Interested in the must-know machine learning algorithms in the field? Check out the first part of our list and introductory descriptions of the top 10 algorithms for data scientists to know.
At Uber, where ML is fundamental to most products, a mechanism to manage offline experiments easily is needed to improve developer velocity. To solve for this, Uber AI was looking for a solution that will potentially complement and extend its in-house experiment management and collaboration capabilities.
Learn how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER), and see why the author used FastAPI to quickly build a fast and robust machine learning API.
Statistics is a building block of data science. If you are working or plan to work in this field, then you will encounter the fundamental concepts reviewed for you here. Certainly, there is much more to learn in statistics, but once you understand these basics, then you can steadily build your way up to advanced topics.
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. See how to use PyCaret's Regression Module for Time Series Forecasting.
Whether you are getting started with Data Science / Machine Learning or are an experienced professional looking to learn something new, check out these top 10 data science courses for 2021.
Normalization, standardization, and regularization all sound similar. However, each plays a unique role in your data preparation and model building process, so you must know when and how to use these important procedures.
Also: Top 3 Statistical Paradoxes in Data Science; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 2; ETL in the Cloud: Transforming Big Data Analytics with Data Warehouse Automation; Essential Math for Data Science: Linear Transformation with Matrices
Maintaining proper organization of all your data science projects will increase your productivity, minimize errors, and increase your development efficiency. This tutorial will guide you through a framework on how to keep everything in order on your local machine and in the cloud.
Check out the freely-available Stanford course Machine Learning with Graphs, taught by Jure Leskovec, and see how a world renowned researcher teaches their topic of expertise. Accessible materials include slides, videos, and more.
Latest KDnuggets Poll results: The Job satisfaction has declined for ML Engineers, Data Scientists, and Data Analysts, but remained the same for Data Engineers, and Managers/Directors. Data Scientist job satisfaction has an alarming drop in mid-career. Finally, which regions have the highest and lowest job satisfactions?
With so many great (and not-so-great) songs out there, it can be hard to find those that match your musical preferences. Follow along this ML model building project to explore the extensive song data available on Spotify and design a recommendation engine that could help you discover your next favorite artist!
You’ll start seeing matrices, not only as operations on numbers, but also as a way to transform vector spaces. This conception will give you the foundations needed to understand more complex linear algebra concepts like matrix decomposition.
While training the AI model, multi-stage activities are performed to utilize the training data in the best manner, so that outcomes are satisfying. So, here are the 6 common mistakes you need to understand to make sure your AI model is successful.
If you are preparing to make a career as a Data Scientist or are looking for opportunities to skill-up in your current role, this analysis of in-demand skills for 2021, based on over 15,000 Data Scientist job postings, should offer you a good idea as to which programming languages and software tools are increasing and decreasing in importance.
Today, organizations are increasingly implementing cloud ETL tools to handle large data sets. With data sets becoming larger by the day, unified ETL tools have become crucial for data integration needs of enterprises.
WeightWatcher is based on theoretical research (done injoint with UC Berkeley) into Why Deep Learning Works, based on our Theory of Heavy Tailed Self-Regularization (HT-SR). It uses ideas from Random Matrix Theory (RMT), Statistical Mechanics, and Strongly Correlated Systems.
A basic appreciation by anyone who builds machine learning models is that the model is not useful without useful data. This doesn't change after a model is deployed to production. Effectively monitoring and retraining models with updated data is key to maintaining valuable ML solutions, and can be accomplished with effective approaches to production-level continuous training that is guided by the data.
Should we still consider data scientists and data engineers as separate roles? When should a team grow with full-stack data developers? Introducing the Checkers-like data team.
Join technology experts, partners and analysts in the industry for this webinar series to see how SAS Viya can help you make the most of AI, analytics and the cloud for faster decisions and trusted results.
If you are looking for a new role as a Data Scientist -- either as a first job fresh out of school, a career change, or a shift to another organization -- then check off as many of these critical points as possible to stand out in the crowd and pass the hiring manager's initial CV screen.
Also: Shapash: Making Machine Learning Models Understandable; A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 2; How to deploy Machine Learning/Deep Learning models to the web; Working With Time Series Using SQL
Interested in learning more about interpretability in machine learning? Check out this free eBook to learn about the basics, simple interpretable models, and strategies for interpreting more complex black box models.
The currency in the 21st century is no longer just data. It's the attention of people. This deep dive article presents the architecture and deployment issues experienced with the deep learning recommendation model, DLRM, which was open-sourced by Facebook in March 2019.
Get ready to trade that “Zoom fatigue” for Zoom euphoria at the DataYap Virtual Conference, Apr 17, where you’ll have your pick of 15 panels on some of the hottest topics in the data and technology space led by some of the top names in data science.
If there's one thing people know how to do, and that's guess what caused something else to happen. Usually these guesses are good, especially when making a visual observation of something in the physical world. AI continues to wrestle with such inference of causality, and fundamental challenges must be overcome before we can have "intuitive" machine learning.
In this second article in this series, we’ll continue to take an interview-driven approach by linking some of the most commonly asked interview questions to different components of A/B testing, including selecting ideas for testing, designing A/B tests, evaluating test results, and making ship or no ship decisions.
Google's recently launched Data Analytics Professional Certificate on Coursera is great for anyone, regardless of background or experience. The program is completely online, self-paced, and costs $39 per month. Interested in preparing for a new career in a high-growth field?
Few things are as demoralizing as seeing your data analysis tossed aside. Learn from these tips -- assembled from experience, academic research, and industry best practice -- on how to make sure your hard work receives the credit it deserves and delivers the value to your organization that you expect.
Not all training data labeling errors have the same impact on the performance of the Machine Learning system. The structure of the labeling errors make a difference. Read iMerit’s latest blog to learn how to minimize the impact of labeling errors.
To encourage more high-quality and especially original contributions to KDnuggets, we announce KDnuggets Top Blogs Reward program, where we will pay the authors of top blogs published each month, starting with blogs published in May 2021.
How can you -- an awesome Data Scientist -- also be known as an awesome software engineer? Docker. And these 3 simple steps to use it for your solutions over and over again.
Learn how EvalML leverages Woodwork, Featuretools and the nlp-primitives library to process text data and create a machine learning model that can detect spam text messages.
Also: The 8 Most Common Data Scientists; Easy AutoML in Python; How to Succeed in Becoming a Freelance Data Scientist; The 8 Most Common Data Scientists
The full value of your deep learning models comes from enabling others to use them. Learn how to deploy your model to the web and access it as a REST API, and begin to share the power of your machine learning development with the world.
Establishing an expectation for trust around AI technologies may soon become one of the most important skills provided by Data Scientists. Significant research investments are underway in this area, and new tools are being developed, such as Shapash, an open-source Python library that helps Data Scientists make machine learning models more transparent and understandable.
We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.
Admit it all you wanna-be, newbie, and old-old-school Data Scientists on the planet, whether you like it or not, you've probably behaved like one of these types. Or two. Or all eight.
In this article, we’ll take an interview-driven approach by linking some of the most commonly asked interview questions to different components of A/B testing, including selecting ideas for testing, designing A/B tests, evaluating test results, and making ship or no ship decisions.
Why You Should Consider Being a Data Engineer Instead of a Data Scientist
Data Scientist vs Machine Learning Engineer – what are their skills?
The Most In-Demand Skills for Data Scientists in 2021
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