News / Blog
September: All | Courses, Education | Meetings | News | Opinions | Top Stories, Tweets | Tutorials, Overviews | Webcasts & Webinars
Top stories: Most viewed and Most shared
KDnuggets News: 2019 News | Schedule | Submissions | Archive
Top stories: Most viewed and Most shared
KDnuggets News: 2019 News | Schedule | Submissions | Archive
Latest:
Top KDnuggets tweets, Sep 11-17: Python Libraries for Interpretable Machine Learning - Sep 18, 2019.
Also: Cartoon: Unsupervised #MachineLearning?; Cartoon: Unsupervised Machine Learning ? How to Become More Marketable as a Data Scientist; Ensemble Methods for Machine Learning: AdaBoost
Python 2 End of Life Survey – Are You Prepared? - Sep 18, 2019.
Support for Python 2 will expire on Jan. 1, 2020, after which the Python core language and many third-party packages will no longer be supported or maintained. Take this survey to help determine and share your level of preparation.
The 5 Sampling Algorithms every Data Scientist need to know - Sep 18, 2019.
Algorithms are at the core of data science and sampling is a critical technical that can make or break a project. Learn more about the most common sampling techniques used, so you can select the best approach while working with your data.- Reddit Post Classification - Sep 18, 2019.
This article covers the implementation of a data scraping and natural language processing project which had two parts: scrape as many posts from Reddit’s API as allowed &then use classification models to predict the origin of the posts. 
Data Science is Boring (Part 1) - Sep 18, 2019.
Read about how one data scientist copes with his boring days of deploying machine learning.- KDnuggets™ News 19:n35, Sep 18: Which Data Science Skills are core and which are hot/emerging ones?; There is No Free Lunch in Data Science Features - Sep 18, 2019.
Check the results of KDnuggets' latest poll to find out which data science skills are core and which are hot/emerging ones; why is there no free lunch in data science?; training Scikit-learn 100x faster; poking fun at unsupervised machine learning; exploring the case for ensemble learning. All this and much more this week on KDnuggets. - Which Data Science Skills are core and which are hot/emerging ones? - Sep 17, 2019.
We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis. 
Turbo-Charging Data Science with AutoML - Sep 17, 2019.
Join this technical webinar on Oct 3, where Domino Chief Data Scientist Josh Poduska will dive into popular open source and proprietary AutoML tools, and walk through hands-on examples of how to install and use these tools, so you can start using these technologies in your work right away.- 5 Alternative Data Science Tools - Sep 17, 2019.
What other creative tools for data science beyond Python and R can you use to make an impression? It's not about the tool -- it's about its impact. - Explore the world of Bioinformatics with Machine Learning - Sep 17, 2019.
The article contains a brief introduction of Bioinformatics and how a machine learning classification algorithm can be used to classify the type of cancer in each patient by their gene expressions. - BERT, RoBERTa, DistilBERT, XLNet: Which one to use? - Sep 17, 2019.
Lately, varying improvements over BERT have been shown — and here I will contrast the main similarities and differences so you can choose which one to use in your research or application. - How Bad is Multicollinearity? - Sep 17, 2019.
For some people anything below 60% is acceptable and for certain others, even a correlation of 30% to 40% is considered too high because it one variable may just end up exaggerating the performance of the model or completely messing up parameter estimates. 
Data Science Symposium 2019, Oct 10-11, Cincinnati - Sep 16, 2019.
The UC Center for Business Analytics will present the Data Science Symposium 2019 on Oct 10 & 11, featuring 3 keynote speakers and 16 tech talks/tutorials on a wide range of data science topics and tools.- My journey path from a Software Engineer to BI Specialist to a Data Scientist - Sep 16, 2019.
The career path of the Data Scientist remains a hot target for many with its continuing high demand. Becoming one requires developing a broad set of skills including statistics, programming, and even business acumen. Learn more about one person's experience making this journey, and discover the many resources available to help you find your way into a world of data science. - Top Stories, Sep 9-15: 10 Great Python Resources for Aspiring Data Scientists - Sep 16, 2019.
Also: The 5 Graph Algorithms That Data Scientists Should Know; Many Heads Are Better Than One: The Case For Ensemble Learning; BERT is changing the NLP landscape; I wasn't getting hired as a Data Scientist; There is No Free Lunch in Data Science - 5 Step Guide to Scalable Deep Learning Pipelines with d6tflow - Sep 16, 2019.
How to turn a typical pytorch script into a scalable d6tflow DAG for faster research & development. - What is Machine Behavior? - Sep 16, 2019.
The new emerging field that wants to study AI agents the way social scientists study humans. - Cartoon: Unsupervised Machine Learning? - Sep 14, 2019.
New KDnuggets Cartoon looks at one of the hottest directions in Machine Learning and asks "Can Machine Learning be too unsupervised?" - Many Heads Are Better Than One: The Case For Ensemble Learning - Sep 13, 2019.
While ensembling techniques are notoriously hard to set up, operate, and explain, with the latest modeling, explainability and monitoring tools, they can produce more accurate and stable predictions. And better predictions can be better for business. - Version Control for Data Science: Tracking Machine Learning Models and Datasets - Sep 13, 2019.
I am a Git god, why do I need another version control system for Machine Learning Projects? - The State of Transfer Learning in NLP - Sep 13, 2019.
This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP organized by Matthew Peters, Swabha Swayamdipta, Thomas Wolf, and Sebastian Ruder. This post highlights key insights and takeaways and provides updates based on recent work. - Clearsense chooses Io-Tahoe’s Smart Data Discovery to navigate healthcare data challenges - Sep 12, 2019.
Io-Tahoe, a pioneer in Smart Data Discovery and AI-Driven Data Catalog products, has announced that Clearsense, a scalable data platform as a service built for healthcare, has chosen the smart data discovery platform to automatically discover and catalog relationships across immense amounts of medical and clinical data. - There is No Free Lunch in Data Science - Sep 12, 2019.
There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science. - Ensemble Methods for Machine Learning: AdaBoost - Sep 12, 2019.
It turned out that, if we ask the weak algorithm to create a whole bunch of classifiers (all weak for definition), and then combine them all, what may figure out is a stronger classifier. - A Friendly Introduction to Support Vector Machines - Sep 12, 2019.
This article explains the Support Vector Machines (SVM) algorithm in an easy way. - Classification vs Prediction - Sep 12, 2019.
It is important to distinguish prediction and classification. In many decision-making contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions. - Top KDnuggets tweets, Sep 04-10: How #AI will transform #healthcare; 10 Great Python Resources for Aspiring Data Scientists - Sep 11, 2019.
Python Libraries for Interpretable Machine Learning; How #AI will transform #healthcare (and can it fix US healthcare system?); Building Recommendation System - an overview ; I wasn't getting hired as a Data Scientist. So I sought data on who is. - Data Driven Government – Agenda, Washington, DC, Sep 25 - Sep 11, 2019.
Data Driven Government is coming to Washington, DC, Sep 26, and includes a stellar lineup of experts who will share the emerging trends and best practices of government agencies in the current use of data analytics to enhance mission outcomes. Use code KDNUGGETS to get 15% off. - Can graph machine learning identify hate speech in online social networks? - Sep 11, 2019.
Online hate speech is a complex subject. Follow this demonstration using state-of-the-art graph neural network models to detect hateful users based on their activities on the Twitter social network. - Train sklearn 100x Faster - Sep 11, 2019.
As compute gets cheaper and time to market for machine learning solutions becomes more critical, we’ve explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution. - KDnuggets™ News 19:n34, Sep 11: I wasn’t getting hired as a Data Scientist. So I sought data on who is - Sep 11, 2019.
How one person overcame rejections applying to Data Scientist positions by getting actual data on who is getting hired; Advice from Andrew Ng on building ML career and reading research papers; 10 Great Python resources for Data Scientists; Python Libraries for Interpretable ML. - Top August Stories: How to Become More Marketable as a Data Scientist - Sep 10, 2019.
Also: Top Handy SQL Features for Data Scientists; 12 NLP Researchers, Practitioners & Innovators You Should Be Following; Knowing Your Neighbours: Machine Learning on Graphs 
Discover Your Path Toward Data Science with ODSC’s Mini-Bootcamp - Sep 10, 2019.
ODSC has developed a mini-bootcamp, designed to reduce the time and monetary costs of discovering which pathway into data science you should take. In this article, we’ll discuss seven reasons why ODSC’s Mini-Bootcamp might be right for you.- Scikit-Learn vs mlr for Machine Learning - Sep 10, 2019.
How does the scikit-learn machine learning library for Python compare to the mlr package for R? Following along with a machine learning workflow through each approach, and see if you can gain a competitive advantage by knowing both frameworks. -
The 5 Graph Algorithms That Data Scientists Should Know - Sep 10, 2019.
In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python. - Common Machine Learning Obstacles - Sep 9, 2019.
In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting. - Top Stories, Sep 2-8: I wasn’t getting hired as a Data Scientist. So I sought data on who is. - Sep 9, 2019.
Also: Python Libraries for Interpretable Machine Learning; TensorFlow vs PyTorch vs Keras for NLP; Advice on building a machine learning career and reading research papers by Prof. Andrew Ng; Object-oriented programming for data scientists: Build your ML estimator - BERT is changing the NLP landscape - Sep 9, 2019.
BERT is changing the NLP landscape and making chatbots much smarter by enabling computers to better understand speech and respond intelligently in real-time. - A 2019 Guide to Speech Synthesis with Deep Learning - Sep 9, 2019.
In this article, we’ll look at research and model architectures that have been written and developed to do just that using deep learning. - OpenStreetMap Data to ML Training Labels for Object Detection - Sep 9, 2019.
I am really interested in creating a tight, clean pipeline for disaster relief applications, where we can use something like crowd sourced building polygons from OSM to train a supervised object detector to discover buildings in an unmapped location. - How DeepMind and Waymo are Using Evolutionary Competition to Train Self-Driving Vehicles - Sep 9, 2019.
Recently, Alphabet’s subsidiaries Waymo and DeepMind partnered to find a more efficient process to train self-driving vehicles algorithms and their work took them back to one of the cornerstones of our history as species: evolution. -
10 Great Python Resources for Aspiring Data Scientists - Sep 9, 2019.
This is a collection of 10 interesting resources in the form of articles and tutorials for the aspiring data scientist new to Python, meant to provide both insight and practical instruction when starting on your journey. - Designing Dashboards that Users Actually Like – Free Webcast - Sep 6, 2019.
See how creating a system of purpose-specific displays enables users to quickly get answers to their data-related questions. - What’s the difference between analytics and statistics? - Sep 6, 2019.
From asking the best questions about data to answering those questions with certainty, understanding the value of these two seemingly different professions is clarified when you see how they should work together. - I wasn’t getting hired as a Data Scientist. So I sought data on who is. - Sep 6, 2019.
Instead of focusing on skills thought to be required of data scientists, we can look at what they have actually done before. - Build Your First Voice Assistant - Sep 6, 2019.
Hone your practical speech recognition application skills with this overview of building a voice assistant using Python. - TensorFlow Optimization Showdown: ActiveState vs. Anaconda - Sep 5, 2019.
In this TensorFlow tutorial, you’ll learn the impact of optimizing both operators and entire graphs, how to efficiently organize data in training and testing datasets to minimize data shuffling, and how to identify a well-optimized model using Anaconda and ActivePython. - 3 Ways to Manage Human Bias in the Analytics Process - Sep 5, 2019.
Managing human bias is an important part of the analytics process. Learn about three areas to watch out for to ensure your models are as unbiased as possible. - Automated Machine Learning: Just How Much? - Sep 5, 2019.
This is an interview between Rosaria Silipo and data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz, asking a few questions on the topic of automated machine learning from their point of view, and some interesting examples of its practical use. - Advice on building a machine learning career and reading research papers by Prof. Andrew Ng - Sep 5, 2019.
This blog summarizes the career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University on YouTube, and includes advice from Andrew Ng on how to read research papers. - Top KDnuggets tweets, Aug 28 – Sep 03: The 8 Neural Network Architectures #MachineLearning Researchers Need to Learn - Sep 4, 2019.
Also: The secret sauce for growing from a data analyst to a data scientist; 4 Tips for Advanced Feature Engineering and Preprocessing; R Users’ Salaries from the 2019 Stackoverflow Survey; Emoji Analytics - Learn Quantum Computing with Python and Q#, Get Programming with Python, Data Science with Python and Dask - Sep 4, 2019.
Save 40% on Get Programming with Python, Data Science with Python and Dask, and Learn Quantum Computing with Python and Q# with code nlpython40. - An Easy Introduction to Machine Learning Recommender Systems - Sep 4, 2019.
Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code. - Python Libraries for Interpretable Machine Learning - Sep 4, 2019.
In the following post, I am going to give a brief guide to four of the most established packages for interpreting and explaining machine learning models. - An Overview of Topics Extraction in Python with Latent Dirichlet Allocation - Sep 4, 2019.
A recurring subject in NLP is to understand large corpus of texts through topics extraction. Whether you analyze users’ online reviews, products’ descriptions, or text entered in search bars, understanding key topics will always come in handy. - KDnuggets™ News 19:n33, Sep 4: Data Science Skills Poll; Object-oriented Programming for Data Scientists - Sep 4, 2019.
This week: Object-oriented programming for data scientists; Deep Learning Next Step: Transformers and Attention Mechanism; R Users' Salaries from the 2019 Stackoverflow Survey; Types of Bias in Machine Learning; 4 Tips for Advanced Feature Engineering and Preprocessing; and much more! - Starting out in Data Science? Top tips and advice from DataScienceGO Speakers - Sep 3, 2019.
DataScienceGO returns to San Diego Sep 27-29, for a three-day career-focused conference designed to unite newcomers, practitioners, managers and executives under one umbrella, speakers weigh in on how to forge the best teams, increase your hiring chances, and prepare for the future. - TensorFlow vs PyTorch vs Keras for NLP - Sep 3, 2019.
These three deep learning frameworks are your go-to tools for NLP, so which is the best? Check out this comparative analysis based on the needs of NLP, and find out where things are headed in the future. - Beyond Neurons: Five Cognitive Functions of the Human Brain that we are Trying to Recreate with Artificial Intelligence - Sep 3, 2019.
The quest for recreating cognitive capabilities of the brain in deep neural networks remains one of the elusive goals of AI. Let’s explore some human cognitive skills that are serving as inspiration to a new generation of AI techniques. - Automate your Python Scripts with Task Scheduler: Windows Task Scheduler to Scrape Alternative Data - Sep 3, 2019.
In this tutorial, you will learn how to run task scheduler to web scrape data from Lazada (eCommerce) website and dump it into SQLite RDBMS Database. - 6 Tips for Building a Training Data Strategy for Machine Learning - Sep 2, 2019.
Without a well-defined approach for collecting and structuring training data, launching an AI initiative becomes an uphill battle. These six recommendations will help you craft a successful strategy. - Cartoon: Labor Day in the age of AI - Sep 2, 2019.
KDnuggets cartoon looks at how AI will impact Labor Day in the year 2050. - Top Stories, Aug 26 – Sep 1: Object-oriented programming for data scientists; Why Data Visualization Is The Most Important Skill in a Data Analyst Arsenal - Sep 2, 2019.
Also: Types of Bias in Machine Learning; Deep Learning Next Step: Transformers and Attention Mechanism; New Poll: Data Science Skills; R Users Salaries from the 2019 Stackoverflow Survey; How to Sell Your Boss on the Need for Data Analytics - Top 10 Data Science Use Cases in Energy and Utilities - Sep 2, 2019.
In this article, we will consider the most vivid data science use cases in the industry of energy and utilities. - Get KDnuggets Pass to Strata Data or TensorFlow World - Aug 30, 2019.
As a media partner for O'Reilly, KDnuggets is pleased to offer to our readers a chance to win a 2-day Bronze Conference pass to either Strata Data NYC or TensorFlow in Santa Clara. Enter by Sep 8, 2019. - Emoji Analytics - Aug 30, 2019.
Emoji is becoming a global language understandable by anyone who expresses... emotion. With the pervasiveness of these little Unicode blocks, we can perform analytics on their use throughout social media to gain insight into sentiments around the world. - R Users’ Salaries from the 2019 Stackoverflow Survey - Aug 30, 2019.
Let’s take a look on what R users are saying about their salaries. Note that the following results could be biased because of unrepresentative and in some cases small samples. -
Object-oriented programming for data scientists: Build your ML estimator - Aug 30, 2019.
Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better. -
Deep Learning Next Step: Transformers and Attention Mechanism - Aug 29, 2019.
With the pervasive importance of NLP in so many of today's applications of deep learning, find out how advanced translation techniques can be further enhanced by transformers and attention mechanisms. - 4 Tips for Advanced Feature Engineering and Preprocessing - Aug 29, 2019.
Techniques for creating new features, detecting outliers, handling imbalanced data, and impute missing values. - Types of Bias in Machine Learning - Aug 29, 2019.
The sample data used for training has to be as close a representation of the real scenario as possible. There are many factors that can bias a sample from the beginning and those reasons differ from each domain (i.e. business, security, medical, education etc.) - The Death of Centralized AI and the Rise of Open AI - Aug 29, 2019.
Centralized AI is giving way to more democratic AI systems, which are becoming more and more accessible to data scientists, both through code and through open ecosystems. - Top KDnuggets tweets, Aug 21-27: Algorithms Notes for Professionals – Free Book - Aug 28, 2019.
Algorithms Notes for Professionals - Free Book; 10 simple Linux tips which save 50% of my time in the command line; Why so many #DataScientists are leaving their jobs; Order Matters: Alibaba Transformer-based Recommender System - New Poll: Data Science Skills - Aug 28, 2019.
New KDnuggets poll asks 1) What Data Science/Machine Learning-related skills you currently have, and 2) Which skills you want to add or improve? If you are human, please vote and we will analyze and publish the results. - A 2019 Guide to Human Pose Estimation - Aug 28, 2019.
Human pose estimation refers to the process of inferring poses in an image. Essentially, it entails predicting the positions of a person’s joints in an image or video. This problem is also sometimes referred to as the localization of human joints. - KDnuggets™ News 19:n32, Aug 28: Handy SQL Features for Data Scientists; Nothing but NumPy: Creating Neural Networks with Computational Graphs - Aug 28, 2019.
Most useful SQL features for Data Scientist; Excellent tutorial on creating neural nets from scratch with Numpy; TensorFlow 2.0 highlights, explained; How to sell your boss on Data Analytics; and more. - TensorFlow 2.0: Dynamic, Readable, and Highly Extended - Aug 27, 2019.
With substantial changes coming with TensorFlow 2.0, and the release candidate version now available, learn more in this guide about the major updates and how to get started on the machine learning platform. - Introducing AI Explainability 360: A New Toolkit to Help You Understand what Machine Learning Models are Doing - Aug 27, 2019.
Recently, AI researchers from IBM open sourced AI Explainability 360, a new toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models. - The secret sauce for growing from a data analyst to a data scientist - Aug 27, 2019.
Despite the increasing demand and appetite for experienced data scientists, the job is ambiguously described most of the times. Also, the delineation between data science and data analytics or engineering is still loosely defined by a lot of hiring managers. - Why Data Visualization Is The Most Important Skill in a Data Analyst Arsenal - Aug 26, 2019.
Visually-displayed data is much more accessible, and it’s critical to promptly identify the weaknesses of an organization, accurately forecast trading volumes and sale prices, or make the right business choices. - How to count Big Data: Probabilistic data structures and algorithms - Aug 26, 2019.
Learn how probabilistic data structures and algorithms can be used for cardinality estimation in Big Data streams. - Artificial Intelligence vs. Machine Learning vs. Deep Learning: What is the Difference? - Aug 26, 2019.
Over the past few years, artificial intelligence continues to be one of the hottest topics. And in order to work effectively with it, you need to understand its constituent parts. - Top Stories, Aug 19-25: Top Handy SQL Features for Data Scientists; Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch - Aug 26, 2019.
Also: Deep Learning for NLP: Creating a Chatbot with Keras!; Understanding Decision Trees for Classification in Python; How to Become More Marketable as a Data Scientist; Is Kaggle Learn a Faster Data Science Education? - How to Sell Your Boss on the Need for Data Analytics - Aug 26, 2019.
Here are some ways you can make the case to your boss that analytics investments are smart for your company to pursue. - Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch - Aug 23, 2019.
Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch with computational graphs. - Top Handy SQL Features for Data Scientists - Aug 23, 2019.
Whenever we hear "data," the first thing that comes to mind is SQL! SQL comes with easy and quick to learn features to organize and retrieve data, as well as perform actions on it in order to gain useful insights. - Order Matters: Alibaba’s Transformer-based Recommender System - Aug 23, 2019.
Alibaba, the largest e-commerce platform in China, is a powerhouse not only when it comes to e-commerce, but also when it comes to recommender systems research. Their latest paper, Behaviour Sequence Transformer for E-commerce Recommendation in Alibaba, is yet another publication that pushes the state of the art in recommender systems. - eBook: How to Enhance Privacy in Data Science - Aug 22, 2019.
Check out this eBook, How to Enhance Privacy in Data Science, to equip yourself with the tools to enhance privacy in data science, including transforming data in a manner that protects the privacy, an overview of the challenges and opportunities of privacy-aware analytics, and more. - Proptech and the proper use of technology for house sales prediction - Aug 22, 2019.
Using the ATTOM dataset, we extracted data on sales transactions in the USA, loans, and estimated values of property. We developed an optimal prediction model from correlations in the time and status of ownership as well as the time of the year of sales fluctuations. - How LinkedIn, Uber, Lyft, Airbnb and Netflix are Solving Data Management and Discovery for Machine Learning Solutions - Aug 22, 2019.
As machine learning evolves, the need for tools and platforms that automate the lifecycle management of training and testing datasets is becoming increasingly important. Fast growing technology companies like Uber or LinkedIn have been forced to build their own in-house data lifecycle management solutions to power different groups of machine learning models.
