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KDnuggets News: 2019 News | Schedule | Archive
Top stories: Most viewed and Most shared
How to submit a blog to KDnuggets
KDnuggets News: 2019 News | Schedule | Archive
Latest:
Testing Your Machine Learning Pipelines - Nov 14, 2019.
Let’s take a look at traditional testing methodologies and how we can apply these to our data/ML pipelines.
Top KDnuggets tweets, Nov 06-12: 10 FREE must-read ebooks on AI. Things just keep getting more interesting in the field, so use these resources to stay up to speed. - Nov 13, 2019.
Also: It's time to make your Data Science LinkedIn profile ready for recruiters.; Python Libraries for Interpretable Machine Learning - KDnuggets; Process your data with Pandas up to 4x faster with this new Python library.; How to Extract Google Maps Coordinates
Python Workout / Practices of a Python Pro / Classic Computer Science Problems in Python - Nov 13, 2019.
Whether you’re a beginner or an expert, there’s always new ways you can improve your Python coding. Save 40% off this trio of Manning Python books today! Just enter the code nlpropython40 at checkout when you buy from manning.com.- Transfer Learning Made Easy: Coding a Powerful Technique - Nov 13, 2019.
While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop. - Beginners Guide to the Three Types of Machine Learning - Nov 13, 2019.
The following article is an introduction to classification and regression — which are known as supervised learning — and unsupervised learning — which in the context of machine learning applications often refers to clustering — and will include a walkthrough in the popular python library scikit-learn. 
How I Got Better at Machine Learning - Nov 13, 2019.
Check out this author's collection of tips and tricks that I learned over the years to get better at Machine Learning.- KDnuggets™ News 19:n43, Nov 13: Dynamic Reports in Python and R; Creating NLP Vocabularies; What is Data Science? - Nov 13, 2019.
On KDnuggets this week: Orchestrating Dynamic Reports in Python and R with Rmd Files; How to Create a Vocabulary for NLP Tasks in Python; What is Data Science?; The Complete Data Science LinkedIn Profile Guide; Set Operations Applied to Pandas DataFrames; and much, much more. - How to Speed up Pandas by 4x with one line of code - Nov 12, 2019.
While Pandas is the library for data processing in Python, it isn't really built for speed. Learn more about the new library, Modin, developed to distribute Pandas' computation to speedup your data prep. - Understanding NLP and Topic Modeling Part 1 - Nov 12, 2019.
In this post, we seek to understand why topic modeling is important and how it helps us as data scientists. 
MLOps for production-level machine learning [Nov 14 Webinar] - Nov 12, 2019.
This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now.- Research Guide for Depth Estimation with Deep Learning - Nov 12, 2019.
In this guide, we’ll look at papers aimed at solving the problems of depth estimation using deep learning. - How to Extract Google Maps Coordinates - Nov 11, 2019.
In this article, I will show you how to quickly extract Google Maps coordinates with a simple and easy method. - The Complete Data Science LinkedIn Profile Guide - Nov 11, 2019.
With so many Data Scientists showing up on LinkedIn, it's time to make sure your profile is top-notch because your talent is still highly sought after. Recruitment specialists want to find you fast, and this guide will help you create the best profile to feature your expertise. - Top Stories, Nov 4-10: 10 Free Must-read Books on AI - Nov 11, 2019.
Also: Understanding Boxplots; Probability Learning: Maximum Likelihood; Designing Your Neural Networks; Facebook Has Been Quietly Open Sourcing Some Amazing Deep Learning Capabilities for PyTorch; 5 Statistical Traps Data Scientists Should Avoid - How Data Analytics Can Assist in Fraud Detection - Nov 11, 2019.
A primary advantage of data analytics tools is that they can handle massive quantities of information at once. These solutions typically learn what's normal within a collection of information and how to spot anomalies. - Facebook Adds This New Framework to It’s Reinforcement Learning Arsenal - Nov 11, 2019.
ReAgent is a new framework that streamlines the implementation of reasoning systems. - Top October Stories: How to Become a (Good) Data Scientist; Everything a Data Scientist Should Know About Data Management; The Last SQL Guide for Data Analysis - Nov 8, 2019.
Also A European Approach to Master's Degrees in Data Science; How YouTube is Recommending Your Next Video - What is Data Science? - Nov 8, 2019.
Data Science is pitched as a modern and exciting job offering high satisfaction. Does its reality really live up to the hype? Here, we show what it's really like to work as a Data Scientist. -
Understanding Boxplots - Nov 8, 2019.
A boxplot. It can tell you about your outliers and what their values are. It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed. - Orchestrating Dynamic Reports in Python and R with Rmd Files - Nov 8, 2019.
Do you want to extract csv files with Python and visualize them in R? How does preparing everything in R and make conclusions with Python sound? Both are possible if you know the right libraries and techniques. Here, we’ll walk through a use-case using both languages in one analysis 
3 Reasons to attend Data Natives, 25-26 November, Berlin - Nov 8, 2019.
Data Natives is an outstanding conference that lets you meet many talented Data Scientists and Data Professionals. Find your dream company or your dream employee and level up for 2020. Use code DN19_KDNuggets_50 to save.- Monitoring Models at Scale - Nov 7, 2019.
Catch this Domino webinar on monitoring models at scale, Dec 11 @ 10am PT, covering detecting changes in pattern of real-world data your models are seeing in production, tracking how model accuracy and other quality metrics are changing over time, and getting alerted when health checks fail so that resolution workflows can be triggered. - Data Cleaning and Preprocessing for Beginners - Nov 7, 2019.
Careful preprocessing of data for your machine learning project is crucial. This overview describes the process of data cleaning and dealing with noise and missing data. - Set Operations Applied to Pandas DataFrames - Nov 7, 2019.
In this tutorial, we show how to apply mathematical set operations (union, intersection, and difference) to Pandas DataFrames with the goal of easing the task of comparing the rows of two datasets. - How to Create a Vocabulary for NLP Tasks in Python - Nov 7, 2019.
This post will walkthrough a Python implementation of a vocabulary class for storing processed text data and related metadata in a manner useful for subsequently performing NLP tasks. - Top KDnuggets tweets, Oct 30 – Nov 05: Everything a Data Scientist Should Know About Data Management - Nov 6, 2019.
Which Data Science Skills are core and which are hot/emerging ones?; The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral DataViz; Microsoft open sources #SandDance, a visual data exploration tool. - Meet Neebo: The Virtual Analytics Hub - Nov 6, 2019.
Neebo is a SaaS solution that enables analytics teams to connect to, find, combine and collaborate on trusted data assets in hybrid cloud landscapes, and provides a unified access point where they can more effectively leverage all their analytics assets and knowledge. In this blog, we will highlight some of the features of Neebo and how they can completely transform the way analytics teams operate. - An Eight-Step Checklist for An Analytics Project - Nov 6, 2019.
Follow these eight headings of an audit sheet that business analysts should address before submitting the results of their analytics project. One recommended approach is to rewrite each step as a question, answer it, and then attach it to your project. - Research Guide: Advanced Loss Functions for Machine Learning Models - Nov 6, 2019.
This guide explores research centered on a variety of advanced loss functions for machine learning models. - The Last Defense Against Another AI Winter - Nov 6, 2019.
My short answer is this: Yes, another AI Winter will be here if you don’t deploy more ML solutions. You and your Data Science teams are the last line of defense against the AI Winter. You need to solve five key challenges to keep the momentum up. - KDnuggets™ News 19:n42, Nov 6: 5 Statistical Traps Data Scientists Should Avoid; 10 Free Must-Read Books on AI - Nov 6, 2019.
Learn about statistical fallacies Data Scientists should avoid; New and quite amazing Deep Learning capabilities FB has been quietly open-sourcing; Top Machine Learning tools for Developers; How to build a Neural Network from scratch and more. -
10 Free Must-read Books on AI - Nov 5, 2019.
Artificial Intelligence continues to fill the media headlines while scientists and engineers rapidly expand its capabilities and applications. With such explosive growth in the field, there is a great deal to learn. Dive into these 10 free books that are must-reads to support your AI study and work. - Probability Learning: Maximum Likelihood - Nov 5, 2019.
The maths behind Bayes will be better understood if we first cover the theory and maths underlying another fundamental method of probabilistic machine learning: Maximum Likelihood. This post will be dedicated to explaining it. - How to Become a Successful Healthcare Data Analyst - Nov 5, 2019.
Are you interested in starting your career in the data analysis domain? Read this informative blog on how to get your career off the ground. - Top Stories, Oct 28 – Nov 3: 5 Statistical Traps Data Scientists Should Avoid; Top Machine Learning Software Tools for Developers - Nov 4, 2019.
Also: Why is Machine Learning Deployment Hard?; Data Sources 101; 5 Statistical Traps Data Scientists Should Avoid; Everything a Data Scientist Should Know About Data Management; How to Become a (Good) Data Scientist — Beginner Guide 
Practical Computer Vision Course with Real-Life Cases, Nov 18, Washington, DC - Nov 4, 2019.
This course, Practical Computer Vision Course with Real-Life Cases, Nov 18 in Washington, DC, will move you on the next step, providing you with practical means of solving business-specific tasks.Reserve your seat now.- Designing Your Neural Networks - Nov 4, 2019.
Check out this step-by-step walk through of some of the more confusing aspects of neural nets to guide you to making smart decisions about your neural network architecture. - Customer Segmentation Using K Means Clustering - Nov 4, 2019.
Customer Segmentation can be a powerful means to identify unsatisfied customer needs. This technique can be used by companies to outperform the competition by developing uniquely appealing products and services. - Facebook Has Been Quietly Open Sourcing Some Amazing Deep Learning Capabilities for PyTorch - Nov 4, 2019.
The new release of PyTorch includes some impressive open source projects for deep learning researchers and developers. - Top Machine Learning Software Tools for Developers - Nov 1, 2019.
As a developer who is excited about leveraging machine learning for faster and more effective development, these software tools are worth trying out. - Build an Artificial Neural Network From Scratch: Part 1 - Nov 1, 2019.
This article focused on building an Artificial Neural Network using the Numpy Python library. - MLOps for production-level machine learning - Nov 1, 2019.
This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now. - What is Machine Learning on Code? - Nov 1, 2019.
Not only can MLonCode help companies streamline their codebase and software delivery processes, but it also helps organizations better understand and manage their engineering talents. - How to Make an Agile Team Work for Big Data Analytics - Oct 31, 2019.
Learn how to approach the challenges when merging an agile methodology into a data science team to bring out the best value for your Big Data products. - How Data Labeling Facilitates AI Models - Oct 31, 2019.
AI-based models are highly dependent on accurate, clean, well-labeled, and prepared data in order to produce the desired output and cognition. These models are fed with bulky datasets covering an array of probabilities and computations to make its functioning as smart and gifted as human intelligence. - How to Build Your Own Logistic Regression Model in Python - Oct 31, 2019.
A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer. - Top KDnuggets tweets, Oct 23-29: End To End Guide For Machine Learning Project – Explained - Oct 30, 2019.
Also: Highest paid positions in 2019 are DevOps, Data Scientist, Data Engineer (all over $100K) - Stack Overflow Salary Calculator, Updated; A neural net solves the three-body problem 100 million times faster; The Last SQL Guide for Data Analysis You’ll Ever Need; How YouTube is Recommending Your Next Video - AutoML for Temporal Relational Data: A New Frontier - Oct 30, 2019.
While AutoML started out as an automation approach to develop optimal machine learning pipelines, extensions of AutoML to Data Science embedded products can now enable the processing of much more, including temporal relational data. - Research Guide for Transformers - Oct 30, 2019.
The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning. -
5 Statistical Traps Data Scientists Should Avoid - Oct 30, 2019.
Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid. - KDnuggets™ News 19:n41, Oct 30: Feature Selection: Beyond feature importance?; Time Series Analysis Using KNIME and Spark - Oct 30, 2019.
This week in KDnuggets: Feature Selection: Beyond feature importance?; Time Series Analysis: A Simple Example with KNIME and Spark; 5 Advanced Features of Pandas and How to Use Them; How to Measure Foot Traffic Using Data Analytics; Introduction to Natural Language Processing (NLP); and much, much more! -
Why is Machine Learning Deployment Hard? - Oct 29, 2019.
Developing an excellent machine learning model is one thing. Deploying it to production is another. Consider these lessons learned and recommendations for approaching this important challenge to help ensure value from your AI work. - About Google’s Self-Proclaimed Quantum Supremacy and its Impact on Artificial Intelligence - Oct 29, 2019.
Google claimed quantum supremacy, IBM challenged it… but the development is really important for the future of AI. - How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow - Oct 29, 2019.
In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce. - Top Stories, Oct 21-27: Everything a Data Scientist Should Know About Data Management; How YouTube is Recommending Your Next Video - Oct 28, 2019.
Also: Introduction to Natural Language Processing (NLP); Anomaly Detection, A Key Task for AI and Machine Learning, Explained; How to Become a (Good) Data Scientist — Beginner Guide - Data Sources 101 - Oct 28, 2019.
Data collection is one of the first steps of the data lifecycle — you need to get all the data you require in the first place. To collect the right data, you need to know where to find it and determine the effort involved in collecting it. This article answers the most basic question: where does all the data you need (or might need) come from? - DataTech20 Seeking Speaker Submissions (16 March 2020, Glasgow) - Oct 28, 2019.
DataTech is a one-day conference on 16 Mar 2020, at the Technology and Innovation Centre in Glasgow, focusing on key topics in data science, and welcoming members of industry, academia, and the public sector alike. DataTech provides a forum for these different communities to meet, share knowledge and expertise, and forge new collaborations. We are currently welcoming workshop, talk and poster proposals for the DataTech20 conference. - How Bayes’ Theorem is Applied in Machine Learning - Oct 28, 2019.
Learn how Bayes Theorem is in Machine Learning for classification and regression! - DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models - Oct 28, 2019.
Visualizing the datasets is an essential component to identify potential sources of bias and unfairness. DeepMind relied on a method called Causal Bayesian networks (CBNs) to represent and estimate unfairness in a dataset. - 5 Advanced Features of Pandas and How to Use Them - Oct 25, 2019.
The pandas library offers core functionality when preparing your data using Python. But, many don't go beyond the basics, so learn about these lesser-known advanced methods that will make handling your data easier and cleaner. - Harnessing Semiotics and Discourse Communities to Understand User Intent - Oct 25, 2019.
Semiotics helps us understand the importance of context to determining the meaning of a term and discourse communities provide us with the background context (mental model) by which to correctly interpret its meaning correctly. - Introduction to Natural Language Processing (NLP) - Oct 25, 2019.
Have you ever wondered how your personal assistant (e.g: Siri) is built? Do you want to build your own? Perfect! Let’s talk about Natural Language Processing. - Seven Myths About the True Costs of AI Systems - Oct 24, 2019.
While there is much excitement today around implementing AI at the enterprise level, the financial costs of this process are often unexpected and underappreciated. These seven myths are crucial lessons learned that executives should know before heading down the road to AI. - Feature Selection: Beyond feature importance? - Oct 24, 2019.
In this post, you will see 3 different techniques of how to do Feature Selection to your datasets and how to build an effective predictive model. - Convolutional Neural Network for Breast Cancer Classification - Oct 24, 2019.
See how Deep Learning can help in solving one of the most commonly diagnosed cancer in women. - Top KDnuggets tweets, Oct 16-22: How YouTube is Recommending Your Next Video - Oct 23, 2019.
Also: The 5 Classification Evaluation Metrics Every Data Scientist Must Know; How to Recognize a Good Data Scientist Job From a Bad One; How to Easily Deploy Machine Learning Models Using Flask. - Samsung Tech Day: Today’s Electronic Devices Seem Magical, But the Real Super-Power is in Silicon - Oct 23, 2019.
Samsung’s Tech Day event showcases processor and memory advances for 5G, AI, Cloud and Edge Computing, Automotive, IoT, and more. - Intro to Adversarial Machine Learning and Generative Adversarial Networks - Oct 23, 2019.
In this crash course on GANs, we explore where they fit into the pantheon of generative models, how they've changed over time, and what the future has in store for this area of machine learning. - How to Measure Foot Traffic Using Data Analytics - Oct 23, 2019.
You need to know how many people visit your store now and what sort of audience you're acquiring. Foot traffic data is going to be invaluable to the success of your business. - Time Series Analysis: A Simple Example with KNIME and Spark - Oct 23, 2019.
The task: train and evaluate a simple time series model using a random forest of regression trees and the NYC Yellow taxi dataset. - KDnuggets™ News 19:n40, Oct 23: How to Become a (Good) Data Scientist; Writing Your First Neural Net in 30 Lines with Keras - Oct 23, 2019.
Read useful advice on how to become a good data scientist; see how you can write your 1st neural net in under 30 lines of Keras code; Understand why AI salaries are heading skywards and what skills you need for them; and read about key ideas and methods in anomaly detection -
Everything a Data Scientist Should Know About Data Management - Oct 22, 2019.
For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions. - Addressing the Growing Need for Skills in Data Science - Oct 22, 2019.
To address the current difficulties in hiring data scientists due to their short supply, many companies can benefit from retraining existing analytically minded employees. - Bye Data Scientists, Hello AI? Not Likely! - Oct 22, 2019.
AI is becoming more mainstream. The fact that computers/robots will learn after being built and will surpass a human's intelligence is terrifying. - How to Write Web Apps Using Simple Python for Data Scientists - Oct 22, 2019.
Convert your Data Science Projects into cool apps easily without knowing any web frameworks. - Anomaly Detection, A Key Task for AI and Machine Learning, Explained - Oct 21, 2019.
One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by a human expert. - How YouTube is Recommending Your Next Video - Oct 21, 2019.
If you are interested in learning more about the latest Youtube recommendation algorithm paper, read this post for details on its approach and improvements. - Top Stories, Oct 14-20: How to Become a (Good) Data Scientist Beginner Guide - Oct 21, 2019.
Also: The 5 Classification Evaluation Metrics Every Data Scientist Must Know; Artificial Intelligence: Salaries Heading Skyward; Writing Your First Neural Net in Less Than 30 Lines of Code with Keras; How to select rows and columns in Pandas using [ ], .loc, iloc, .at and .iat; The Last SQL Guide for Data Analysis You'll Ever Need - This Microsoft Neural Network can Answer Questions About Scenic Images with Minimum Training - Oct 21, 2019.
Recently, a group of AI experts from Microsoft Research published a paper proposing a method for scene understanding that combines two key tasks: image captioning and visual question answering (VQA). - How to Get the Most out of ODSC West 2019 - Oct 18, 2019.
ODSC West comes to San Francisco on Oct 29 - Nov 1. With over 300 hours of content, 200+ speakers, and thousands of attendees, there is certainly a lot to see, learn, and do at the conference. Register by Friday for 10% off your pass. - 5 Tips for Novice Freelance Data Scientists - Oct 18, 2019.
If you want to launch your data science skills into freelance work, then check out these important tips to help you kick start your next adventure in data. - Building an intelligent Digital Assistant - Oct 18, 2019.
In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used. - Writing Your First Neural Net in Less Than 30 Lines of Code with Keras - Oct 18, 2019.
Read this quick overview of neural networks and learn how to implement your first in very few lines using Keras. - Real Data, Big Impact: UChicago Students Work to Improve Sales at Goose Island - Oct 17, 2019.
Watch UChicago Master of Science in Analytics capstone projects unfold in Real Data, Big Impact and see how students collaborate with their clients to deliver successful analytics projects. - Data Anonymization – History and Key Ideas - Oct 17, 2019.
While effective anonymization technology remains elusive, understanding the history of this challenge can guide data science practitioners to address these important concerns through ethical and responsible use of sensitive information. - Artificial Intelligence: Salaries Heading Skyward - Oct 17, 2019.
While the average salary for a Software Engineer is around $100,000 to $150,000, to make the big bucks you want to be an AI or Machine Learning (Specialist/Scientist/Engineer.) - How to Easily Deploy Machine Learning Models Using Flask - Oct 17, 2019.
This post aims to make you get started with putting your trained machine learning models into production using Flask API. - Top KDnuggets tweets, Oct 09-15: #DeepLearning for Natural Language Processing (#NLP) using RNNs & CNNs #KDN Post - Oct 16, 2019.
Also: Kannada-MNIST: A new handwritten digits dataset in ML town; Math for Programmers; The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization; The Last SQL Guide for Data Analysis You’ll Ever Need - How to Become a (Good) Data Scientist – Beginner Guide - Oct 16, 2019.
A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning. - Probability Learning I: Bayes’ Theorem - Oct 16, 2019.
Learn about one of the fundamental theorems of probability with an easy everyday example.
