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KDnuggets News: 2020 News | Schedule | Archive
Top stories: Most viewed and Most shared
How to submit a blog to KDnuggets
KDnuggets News: 2020 News | Schedule | Archive
Latest:
How to reinvent your career with computer science - Jan 21, 2020.
There is great news for anyone looking to make a career switch into computer science. So, what does it take to make the leap? Check out three top tips for budding computer scientists, as well as the Computer Science online MSc from the University of Bath.
Top 5 AI trends for 2020 - Jan 21, 2020.
We are all witnessing a staggering growth of AI technology with so many new benefits for people while also changing the way we live and work. As AI continues to grow, which applications will have a significant impact in 2020?- Artificial Intelligence Books to Read in 2020 - Jan 21, 2020.
Here are some AI-related books that I’ve read and recommend for you to add to your 2020 reading list! 
Explaining Black Box Models: Ensemble and Deep Learning Using LIME and SHAP - Jan 21, 2020.
This article will demonstrate explainability on the decisions made by LightGBM and Keras models in classifying a transaction for fraudulence, using two state of the art open source explainability techniques, LIME and SHAP.
Learn from Industry Leaders Facebook, Walmart, Lyft, eBay & More - Jan 20, 2020.
The agenda for Deep Learning World 2020 in Las Vegas, May 31-Jun 4, has been released. Use code KDNUGGETS for a 15% discount on your ticket.- I wanna be a data scientist, but… how? - Jan 20, 2020.
It’s easy to say "I wanna be a data scientist," but... where do you start? How much time is needed to be desired by companies? Do you need a Master’s degree? Do you need to know every mathematical concept ever derived? The journey might be long, but follow this plan to help you keep moving forward toward your career goal. 
Top Stories, Jan 13-19: Math for Programmers!; Decision Tree Algorithm, Explained - Jan 20, 2020.
Also: Top 9 Mobile Apps for Learning and Practicing Data Science; Classify A Rare Event Using 5 Machine Learning Algorithms; The Future of Machine Learning; The Book to Start You on Machine Learning- We Created a Lazy AI - Jan 20, 2020.
This article is an overview of how to design and implement reinforcement learning for the real world. - Microsoft Introduces Project Petridish to Find the Best Neural Network for Your Problem - Jan 20, 2020.
The new algorithm takes a novel approach to neural architecture search. - 10 Python String Processing Tips & Tricks - Jan 20, 2020.
Pursuing a text analytics path but don't know where to start? Try this string processing primer to first gain an understanding of using Python to manipulate and process strings at a basic level. - The Future of Machine Learning - Jan 17, 2020.
This summary overviews the keynote at TensorFlow World by Jeff Dean, Head of AI at Google, that considered the advancements of computer vision and language models and predicted the direction machine learning model building should follow for the future. -
Top 9 Mobile Apps for Learning and Practicing Data Science - Jan 17, 2020.
This article will tell you about the top 9 mobile apps that help the user in learning and practicing data science and hence is improving their productivity. - Methods, challenges & applications of Deep Learning | Munich 11-12 May - Jan 16, 2020.
Visit Deep Learning World, 11-12 May in Munich, to broaden your knowledge, deepen your understanding and discuss your questions with other Deep Learning experts! - Top 10 Technology Trends for 2020 - Jan 16, 2020.
With integrations of multiple emerging technologies just in the past year, AI development continues at a fast pace. Following the blueprint of science and technology advancements in 2019, we predict 10 trends we expect to see in 2020 and beyond. - Schema Evolution in Data Lakes - Jan 16, 2020.
Whereas a data warehouse will need rigid data modeling and definitions, a data lake can store different types and shapes of data. In a data lake, the schema of the data can be inferred when it’s read, providing the aforementioned flexibility. However, this flexibility is a double-edged sword. - Handling Trees in Data Science Algorithmic Interview - Jan 16, 2020.
This post is about fast-tracking the study and explanation of tree concepts for the data scientists so that you breeze through the next time you get asked these in an interview. - Top KDnuggets tweets, Jan 08-14: A Beginners Guide to Data Engineering — Part I - Jan 15, 2020.
Also: The Book to Start You on Machine Learning - KDnuggets; Top KDnuggets tweets, Jan 1-7: Introduction to #DataVisualization and Storytelling: A Guide For The #DataScientist #eBook; 7 Steps to a Job-winning Data Science Resume - KDnuggets; Tips for open-sourcing research code -
Math for Programmers! - Jan 15, 2020.
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. - Disentangling disentanglement: Ideas from NeurIPS 2019 - Jan 15, 2020.
This year’s NEURIPS-2019 Vancouver conference recently concluded and featured a dozen papers on disentanglement in deep learning. What is this idea and why is it so interesting in machine learning? This summary of these papers will give you initial insight in disentanglement as well as ideas on what you can explore next. - Classify A Rare Event Using 5 Machine Learning Algorithms - Jan 15, 2020.
Which algorithm works best for unbalanced data? Are there any tradeoffs? - Geovisualization with Open Data - Jan 15, 2020.
In this post I want to show how to use public available (open) data to create geo visualizations in python. Maps are a great way to communicate and compare information when working with geolocation data. There are many frameworks to plot maps, here I focus on matplotlib and geopandas (and give a glimpse of mplleaflet). - KDnuggets™ News 20:n02, Jan 15: Top 5 Must-have Data Science Skills; Learn Machine Learning with THIS Book - Jan 15, 2020.
This week: learn the 5 must-have data science skills for the new year; find out which book is THE book to get started learning machine learning; pick up some Python tips and tricks; learn SQL, but learn it the hard way; and find an introductory guide to learning common NLP techniques. - Survey Segmentation Tutorial - Jan 14, 2020.
Learn the basics of verifying segmentation, analyzing the data, and creating segments in this tutorial. When reviewing survey data, you will typically be handed Likert questions (e.g., on a scale of 1 to 5), and by using a few techniques, you can verify the quality of the survey and start grouping respondents into populations. - 7 AI Use Cases Transforming Live Sports Production and Distribution - Jan 14, 2020.
Here are 7 powerful AI led use cases both for linear television and for OTT apps that are transforming the live sports production landscape. - Decision Tree Algorithm, Explained - Jan 14, 2020.
All you need to know about decision trees and how to build and optimize decision tree classifier. - Statistical Thinking for Industrial Problem Solving: a free online course. - Jan 13, 2020.
This online course is available – for free – to anyone interested in building practical skills in using data to solve problems better. - Idiot’s Guide to Precision, Recall, and Confusion Matrix - Jan 13, 2020.
Building Machine Learning models is fun, but making sure we build the best ones is what makes a difference. Follow this quick guide to appreciate how to effectively evaluate a classification model, especially for projects where accuracy alone is not enough. - Top Stories, Jan 6-12: Top 5 must-have Data Science skills for 2020; 7 Resources to Becoming a Data Engineer - Jan 13, 2020.
Also: The Book to Start You on Machine Learning; An Introductory Guide to NLP for Data Scientists with 7 Common Techniques; A Comprehensive Guide to Natural Language Generation; The Book to Start You on Machine Learning; 10 Python Tips and Tricks You Should Learn Today - Graph Machine Learning Meets UX: An uncharted love affair - Jan 13, 2020.
When machine learning tools are developed by technology first, they risk failing to deliver on what users actually need. It can also be difficult for development teams to establish meaningful direction. This article explores the challenges of designing an interface that enables users to visualise and interact with insights from graph machine learning, and explores the very new, uncharted relationship between machine learning and UX. - Uber Creates Generative Teaching Networks to Better Train Deep Neural Networks - Jan 13, 2020.
The new technique can really improve how deep learning models are trained at scale. - Top December Stories: What is a Data Scientist Worth? AI, ML, DS, DL Research Main Developments and Key Trends - Jan 10, 2020.
Also: Google's New Explainable AI Service; 10 Free Top Notch Machine Learning Courses. - Applying Occam’s razor to Deep Learning - Jan 10, 2020.
Finding a deep learning model to perform well is an exciting feat. But, might there be other -- less complex -- models that perform just as well for your application? A simple complexity measure based on the statistical physics concept of Cascading Periodic Spectral Ergodicity (cPSE) can help us be computationally efficient by considering the least complex during model selection. - Deepfakes Security Risks - Jan 10, 2020.
Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently. - 7 Steps to a Job-winning Data Science Resume - Jan 10, 2020.
A resume plays a key role in bagging that dream data science job. We break down the nuances of a job-winning data science resume so that you can go ahead and transform your own resume. - Fast Track Your Data Science Career - Jan 9, 2020.
Earn a Master of Professional Studies in Data Analytics online through Penn State World Campus – and you can add in-demand skills to your wheelhouse while you continue to work. - An Introductory Guide to NLP for Data Scientists with 7 Common Techniques - Jan 9, 2020.
Data Scientists work with tons of data, and many times that data includes natural language text. This guide reviews 7 common techniques with code examples to introduce you the essentials of NLP, so you can begin performing analysis and building models from textual data. - The Book to Start You on Machine Learning - Jan 9, 2020.
This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context. - Stock Market Forecasting Using Time Series Analysis - Jan 9, 2020.
Time series analysis will be the best tool for forecasting the trend or even future. The trend chart will provide adequate guidance for the investor. So let us understand this concept in great detail and use a machine learning technique to forecast stocks. - Top KDnuggets tweets, Jan 01-07: Introduction to Data Visualization and Storytelling: A Guide For The Data Scientist eBook - Jan 8, 2020.
Introduction to Data Visualization & Storytelling;The Data Science Interview Study Guide; Why Kaggle will NOT make you a great Data Scientist; Cartoon: Teaching Ethics to AI - Top 5 must-have Data Science skills for 2020 - Jan 8, 2020.
The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market. - Learning SQL the Hard Way - Jan 8, 2020.
Simply put: This post is about installing SQL, explaining SQL and running SQL. - KDnuggets™ News 20:n01, Jan 8: How to “Ultralearn” Data Science; How teams do AutoML? - Jan 8, 2020.
First issue of 2020 brings you a summary of how to "Ultralearn" Data Science - for those in a hurry; Explains how teams work on AutoML project; Why Python is a preferred language for Data Science; and a cartoon on teaching ethics to AI. - 10 Python Tips and Tricks You Should Learn Today - Jan 8, 2020.
Check out this collection of 10 Python snippets that can be taken as a reference for your daily work. - 5 Hands-on Skills Every Data Scientist Needs in 2020 – Coming to ODSC East - Jan 7, 2020.
Here are our top five hands-on training focus areas that every data scientist should know and that we’re paying extra attention to at ODSC East 2020 this April 13-17 in Boston. - 7 Resources to Becoming a Data Engineer - Jan 7, 2020.
An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable. - A Comprehensive Guide to Natural Language Generation - Jan 7, 2020.
Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models. - Introducing Generalized Integrated Gradients (GIG): A Practical Method for Explaining Diverse Ensemble Machine Learning Models - Jan 7, 2020.
There is a need for a new way to explain complex, ensembled ML models for high-stakes applications such as credit and lending. This is why we invented GIG. - 5 Ways AI Is Changing The Healthcare Industry - Jan 7, 2020.
The healthcare AI market is expected to reach 28 billion dollars by the year 2025. With such exponential growth, AI is undoubtedly likely to bring some drastic changes in the healthcare industry. Let’s look at five ways of how AI has changed the healthcare industry. - Live Webinar: Learn how to build better machine learning pipelines - Jan 6, 2020.
In this webinar, Jan 15 @ 12PM EST, we'll offer solutions to the common challenges data scientists and data engineers face when building a machine learning pipeline. Register now to attend live or to watch a recording afterwards. - 3 common data science career transitions, and how to make them happen - Jan 6, 2020.
Breaking into a career in Data Science can depend on where you start. See if you fit into one of these three categories of "newbies," and then find out how to make your professional transition into the field. - H2O Framework for Machine Learning - Jan 6, 2020.
This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks. - Top Stories, Dec 30 – Jan 5: How To Ultralearn Data Science; Automated Machine Learning: How do teams work together on an AutoML project? - Jan 6, 2020.
Also: Predict Electricity Consumption Using Time Series Analysis; What is the most important question for Data Science (and Digital Transformation); Why Python is One of the Most Preferred Languages for Data Science?; What is a Data Scientist Worth?; How to Speed up Pandas by 4x with one line of code - How to Convert a Picture to Numbers - Jan 6, 2020.
Reducing images to numbers makes them amenable to computation. Let's take a look at the why and the how using Python and Numpy. - Cartoon: Teaching Ethics to AI - Jan 4, 2020.
Ethics in AI has received significant attention recently, and the new KDnuggets cartoon examines the problem of teaching ethics to artificially intelligent entities. - Why Python is One of the Most Preferred Languages for Data Science? - Jan 3, 2020.
Why do most data scientists love Python? Learn more about how so many well-developed Python packages can help you accomplish your crucial data science tasks. - Beginner’s Guide to K-Nearest Neighbors in R: from Zero to Hero - Jan 3, 2020.
This post presents a pipeline of building a KNN model in R with various measurement metrics. - How HR Is Using Data Science and Analytics to Close the Gender Gap - Jan 3, 2020.
The gender gap can extend to the lack of equal representation in certain industries or career paths, and there's an extraordinarily long way to go before people will be on equal footing in the labor market. Human resources professionals can rely on data analytics to make progress. - Accuracy vs Speed – what Data Scientists can learn from Search - Jan 2, 2020.
Delivering accurate insights is the core function of any data scientist. Navigating the development road toward this goal can sometimes be tricky, especially when cross-collaboration is required, and these lessons learned from building a search application will help you negotiate the demands between accuracy and speed. - Automated Machine Learning: How do teams work together on an AutoML project? - Jan 2, 2020.
In this use case, available to the public on GitHub, we’ll see how a data scientist, project manager, and business lead at a retail grocer can leverage automated machine learning and Azure Machine Learning service to reduce product overstock. - Predict Electricity Consumption Using Time Series Analysis - Jan 2, 2020.
Time series forecasting is a technique for the prediction of events through a sequence of time. In this post, we will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside. - Top KDnuggets tweets, Dec 18-30: A Gentle Introduction to Math Behind Neural Networks - Dec 31, 2019.
A Gentle Introduction to #Math Behind #NeuralNetworks; Learn How to Quickly Create UIs in Python; I wanna be a data scientist, but... how!?; I created my own deepfake in two weeks - What is the most important question for Data Science (and Digital Transformation) - Dec 31, 2019.
With so many buzzwords surrounding AI and machine learning, understanding which can bring business value and which are best left in the lab to mature is difficult. While machine learning offers significant power in driving digital transformations, a business must start with the right questions and leave the math to the development teams. - Towards a Quantitative Measure of Intelligence: Breaking Down One of the Most Important AI Papers of 2019, Part II - Dec 31, 2019.
AI scientist Francois Chollet proposes a better framework for measuring the intelligence of AI systems. -
How To “Ultralearn” Data Science: summary, for those in a hurry - Dec 30, 2019.
For those of you in a hurry and interested in ultralearning (which should be all of you), this recap reviews the approach and summarizes its key elements -- focus, optimization, and deep understanding with experimentation -- geared toward learning Data Science. - Top Stories, Dec 16-29: What is a Data Scientist Worth?; Google’s New Explainable AI Service - Dec 30, 2019.
Also: Let’s Build an Intelligent Chatbot; 10 Best and Free Machine Learning Courses, Online; Build Pipelines with Pandas Using pdpipe; Alternative Cloud Hosted Data Science Environments - Towards a Quantitative Measure of Intelligence: Breaking Down One of the Most Important AI Papers of 2019, Part I - Dec 30, 2019.
AI scientist Francois Chollet proposes a better framework for measuring the intelligence of AI systems. - How To “Ultralearn” Data Science: deep understanding and experimentation, Part 4 - Dec 27, 2019.
In this fourth and final part of the ultralearning data science series, it's time to take the final steps toward developing a deep understanding of the fundamentals and learning how to experiment -- the two aspects that are the ultimate keys to ultralearning. - Fighting Overfitting in Deep Learning - Dec 27, 2019.
This post outlines an attack plan for fighting overfitting in neural networks. - KDnuggets™ News 19:n49, Dec 27: What is a Data Scientist Worth? New Explainable AI from Google - Dec 27, 2019.
What is a Data Scientist Worth?; Google's New Explainable AI Service; The Most In Demand Tech Skills for Data Scientists; The 4 fastest ways NOT to get hired as a data scientist; and KDnuggets Cartoon which was included in a surprising textbook. - 10 Best and Free Machine Learning Courses, Online - Dec 26, 2019.
Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field. - Random Forest vs Neural Networks for Predicting Customer Churn - Dec 26, 2019.
Let us see how random forest competes with neural networks for solving a real world business problem. - KDnuggets Cartoon in an English textbook? - Dec 24, 2019.
KDnuggets is not only for learning about AI, Data Science, and Machine Learning. A KDnuggets cartoon has been included in an English language and culture textbook for French high-school students. - Market Basket Analysis: A Tutorial - Dec 24, 2019.
This article is about Market Basket Analysis & the Apriori algorithm that works behind it. - What is Data Catalog and Why You Should Care? - Dec 23, 2019.
Learn why data catalogs could be just the thing you need to meet the challenges of data and metadata management and collaboration. -
What is a Data Scientist Worth? - Dec 23, 2019.
What is the Salary of a Data Scientist in 2019? Let's have a look at some data to see how we can answer that question. - Top KDnuggets tweets, Dec 11-17: Idiot’s Guide to Precision, Recall and Confusion - Dec 20, 2019.
Idiot's Guide to Precision, Recall and Confusion Matrix; 10 Free Must-Read Books for Machine Learning and Data Science; How to Speed up Pandas by 4x with one line of codes; #Math for Programmers teaches you the math you need to know. - How To “Ultralearn” Data Science: optimization learning, Part 3 - Dec 20, 2019.
This third part in a series about how to "ultralearn" data science will guide you through how to optimize your learning through five valuable techniques. - Google’s New Explainable AI Service - Dec 20, 2019.
Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction. - The Most In Demand Tech Skills for Data Scientists - Dec 20, 2019.
By the end of this article you’ll know which technologies are becoming more popular with employers and which are becoming less popular. - Alternative Cloud Hosted Data Science Environments - Dec 19, 2019.
Over the years new alternative providers have risen to provided a solitary data science environment hosted on the cloud for data scientist to analyze, host and share their work. - Interpretability part 3: opening the black box with LIME and SHAP - Dec 19, 2019.
The third part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers methods that try to explain each prediction instead of establishing a global explanation. - 5 Ways to Apply Ethics to AI - Dec 19, 2019.
Here are six more lessons based on real life examples that I think we should all remember as people working in machine learning, whether you’re a researcher, engineer, or a decision-maker. - Ontotext Platform 3.0 for Enterprise Knowledge Graphs Released - Dec 18, 2019.
Ontotext Platform 3.0 features significant technology improvements to enable simpler and faster graph navigation, including GraphQL interfaces to make it easier for application developers to access knowledge graphs without tedious development of back-end APIs or complex SPARQL. - The 4 fastest ways NOT to get hired as a data scientist - Dec 18, 2019.
Ready to try to get hired as a data scientist for the first time? Avoiding these common mistakes won’t guarantee an offer, but not avoiding them is a sure fire way for your application to be tossed into the trash bin. - Automatic Text Summarization in a Nutshell - Dec 18, 2019.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Automatic Text Summarization and the various ways it is used. - How to Convert an RGB Image to Grayscale - Dec 18, 2019.
This post is about working with a mixture of color and grayscale images and needing to transform them into a uniform format - all grayscale. We'll be working in Python using the Pillow, Numpy, and Matplotlib packages. - The ravages of concept drift in stream learning applications and how to deal with it - Dec 18, 2019.
Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. These streams of data evolve generally over time and may be occasionally affected by a change (concept drift). How to handle this change by using detection and adaptation mechanisms is crucial in many real-world systems. - KDnuggets™ News 19:n48, Dec 18: Build Pipelines with Pandas Using pdpipe; AI, Analytics, ML, DS, Technology Main Developments, Key Trends; Poll on AutoML - Dec 18, 2019.
Build Pipelines with Pandas Using pdpipe; AI, Analytics, ML, DS, Technology Main Developments, Key Trends; New Poll: Does AutoML work? Ultralearn Data Science; Python Dictionary How-To; Top stories of 2019 and more. - Top 2019 Stories: Top 10 Technology Trends of 2019; How to select rows and columns in Pandas - Dec 17, 2019.
Also: Your AI skills are worth less than you think; Another 10 Free Must-See Courses for Machine Learning and Data Science. - Building an Analytics Career at UChicago - Dec 17, 2019.
Michael Collela describes how UChicago’s Master of Science in Analytics has helped him define his career path. Michael currently works as a data scientist at dunnhumby.
