Topics: 
Nov
2018 Dec
All (86) | Courses, Education (3) | Meetings (5) | News (14) | Opinions (28) | Top Stories, Tweets (9) | Tutorials, Overviews (23) | Webcasts & Webinars (4)
- Good Feature Building Techniques and Tricks for Kaggle
- Dec 31, 2018.
A selection of top tips to obtain great results on Kaggle leaderboards, including useful code examples showing how best to use Latitude and Longitude features.
-
Papers with Code: A Fantastic GitHub Resource for Machine Learning
- Dec 31, 2018.
Looking for papers with code? If so, this GitHub repository, a clearinghouse for research papers and their corresponding implementation code, is definitely worth checking out. - Comparison of the Top Speech Processing APIs
- Dec 28, 2018.
There are two main tasks in speech processing. First one is to transform speech to text. The second is to convert the text into human speech. We will describe the general aspects of each API and then compare their main features in the table.
- Manning Countdown to 2019 – Big Deals on AI, Data Science, Machine Learning books and videos
- Dec 28, 2018.
Introducing the Manning countdown to 2019, where each day you’ll be able to get a different one day deal on some of their biggest books and video courses.
- Supervised Learning: Model Popularity from Past to Present
- Dec 28, 2018.
An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?
- The Essence of Machine Learning
- Dec 28, 2018.
And so now, as an exercise in what may seem to be semantics, let's explore some 30,000 feet definitions of what machine learning is.
- World’s Biggest Deep Learning Summit 3 weeks away
- Dec 27, 2018.
RE•WORK will be running a New Year's discount next week, but are offering exclusive early access to KDnuggets subscribers - save 25% when you register with the code NEWYEAR before January 11th.
- Who is a Data Scientist?
- Dec 27, 2018.
An introduction to the Initiative for Analytics and Data Science Standards (IADSS), who have launched a global research study aiming to gain insight about the analytics profession in the industry and help support the development of standards regarding analytics role definitions.
- A Case For Explainable AI & Machine Learning
- Dec 27, 2018.
In support of the explainable AI cause, we present a variety of use cases covering operational needs, regulatory compliance and public trust and social acceptance.
- Using the Economics Value Curve to Drive Digital Transformation
- Dec 27, 2018.
Optimizing a single objective, or a single point, is actually quite easy because there are no conflicting objectives. The real business challenge, and the source of much innovation, is trying to optimize a decision across multiple variables. Let’s explore this further.
- Synthetic Data Generation: A must-have skill for new data scientists
- Dec 27, 2018.
A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.
- Deep learning in Satellite imagery
- Dec 26, 2018.
This article outlines possible sources of satellite imagery, what its properties are and how this data can be utilised using R.
- BERT: State of the Art NLP Model, Explained
- Dec 26, 2018.
BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks.
- Interspeech 2018: Highlights for Data Scientists
- Dec 24, 2018.
Key highlights from the Interspeech conference, with topics covering end-to-end models for automatic speech recognition, information theory approach to deep learning, speech processing and education, and more.
- Twas the Night Before Analysis or A Visit from the Chief Data Scientist
- Dec 24, 2018.
The holiday classic gets a data science makeover. Let your belly shake like a bowl full of jelly.
- Top Stories, Dec 17-23: Why You Shouldn’t be a Data Science Generalist; 10 More Must-See Free Courses for Machine Learning and Data Science
- Dec 24, 2018.
Also: Introduction to Statistics for Data Science; Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning; Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019
-
A Guide to Decision Trees for Machine Learning and Data Science
- Dec 24, 2018.
What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure. - Feature engineering, Explained
- Dec 21, 2018.
A brief introduction to feature engineering, covering coordinate transformation, continuous data, categorical features, missing values, normalization, and more.
- Six Steps to Master Machine Learning with Data Preparation
- Dec 21, 2018.
To prepare data for both analytics and machine learning initiatives teams can accelerate machine learning and data science projects to deliver an immersive business consumer experience that accelerates and automates the data-to-insight pipeline by following six critical steps.
- Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI
- Dec 20, 2018.
We explain the key differences between explainability and interpretability and why they're so important for machine learning and AI, before taking a look at several techniques and methods for improving machine learning interpretability.
- 10 More Must-See Free Courses for Machine Learning and Data Science
- Dec 20, 2018.
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.
- Top KDnuggets tweets, Dec 12-18: Deep Learning Cheat Sheets; The Nate Silver vs. Nassim Taleb Twitter War
- Dec 19, 2018.
Also: Should you become a data scientist?; Learning Machine Learning vs Learning Data Science; Math for Machine Learning; Automated Web Scraping in R
- Improve ML transparency without sacrificing accuracy
- Dec 19, 2018.
O'Reilly begins to shed some light on the accuracy/complexity tradeoff in machine learning, with An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI. Get the ebook now!
- Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning
- Dec 19, 2018.
Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning.
- The brain as a neural network: this is why we can’t get along
- Dec 19, 2018.
This article sets out to answer the question: what insights can we gain about ourselves by thinking of the brain as a machine learning model?
- Think Twice Before You Accept That Fancy Data Science Job
- Dec 19, 2018.
Before you figure out what skills you need to freshen up on, or the most optimal driving path to work to avoid traffic patterns, you need to make sure this new role is a right fit and that you'll be happy working there.
- How to do Deep Learning with SAS
- Dec 18, 2018.
Build a deep learning model using SAS. This paper offers a how-to guide so that you can get up and running.
- Exploring the Data Jungle Free eBook
- Dec 18, 2018.
This free eBook by Brian Godsey will provide you with real-world examples in Python, R, and other languages suitable for data science.
- How will automation tools change data science?
- Dec 18, 2018.
This article provides an overview of recent trends in machine learning and data science automation tools and addresses how those tools will change data science.
- Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019
- Dec 18, 2018.
This is a collection of data science, machine learning, analytics, and AI predictions for next year from a number of top industry organizations. See what the insiders feel is on the horizon for 2019!
- eBook: An Introduction to Active Learning
- Dec 17, 2018.
At Figure Eight, we're big believers in active learning. We think it holds the promise to better models, and that it's just about to go mainstream. In our new eBook, An Introduction to Active Learning, we cover the essentials. Download now!
- The 2019 PAW Business Agenda is Live – Super Early Bird expires this Friday
- Dec 17, 2018.
Breaking News: The 2019 PAW Business program is live! Predictive Analytics World for Business is coming to Caesars Palace in Las Vegas, Jun 16-20, 2019. Super Early Bird ends this Friday!
- 2018 Year-in-Review: Machine Learning Open Source Projects & Frameworks
- Dec 17, 2018.
This post is a look at the top open source projects and major developments in machine learning frameworks over the past 12 months.
- Introduction to Statistics for Data Science
- Dec 17, 2018.
This tutorial helps explain the central limit theorem, covering populations and samples, sampling distribution, intuition, and contains a useful video so you can continue your learning.
- Top Stories, Dec 10-16: Why You Shouldn’t be a Data Science Generalist; Machine Learning & AI Main Developments in 2018 and Key Trends for 2019
- Dec 17, 2018.
Also: Learning Machine Learning vs Learning Data Science; Should you become a data scientist?; Learning Machine Learning vs Learning Data Science; Common mistakes when carrying out machine learning and data science; How Different are Conventional Programming and Machine Learning?
- Spark + AI Summit: learn best practices in ML and DL, latest frameworks, and more – special KDnuggets offer
- Dec 14, 2018.
Check agenda for the Spark + AI Summit in San Francisco on April 23-25, 2019, comprising of 12 technical tracks on data and AI across verticals, and get the biggest discount: $700 off until Dec 31.
- Top Stories of 2018: 9 Must-have skills you need to become a Data Scientist, updated; Python eats away at R: Top Software for Analytics, Data Science, Machine Learning
- Dec 14, 2018.
Also 5 Data Science Projects That Will Get You Hired in 2018; Top 20 Python AI and Machine Learning Open Source Projects; Neural network AI is simple. So... Stop pretending you are a genius.
- NLP Breakthrough Imagenet Moment has arrived
- Dec 14, 2018.
A comprehensive review of the current state of Natural Language Processing, covering the process from shallow to deep pre-training, what's in an ImageNet, the case for language modelling, and more.
- Implementing ResNet with MXNET Gluon and Comet.ml for Image Classification
- Dec 14, 2018.
Whether MXNet is an entirely new framework for you or you have used the MXNet backend while training your Keras models, this tutorial illustrates how to build an image recognition model with an MXNet resnet_v1 model.
-
Why You Shouldn’t be a Data Science Generalist
- Dec 14, 2018.
But it’s hard to avoid becoming a generalist if you don’t know which common problem classes you could specialize in in the fist place. That’s why I put together a list of the five problem classes that are often lumped together under the “data science” heading. - Four Real-Life Machine Learning Use Cases
- Dec 13, 2018.
This ebook will walk you through four use cases for Machine Learning on Databricks, covering loan risk, advertising analytics and predictive use case, market basket analysis, suspicious behaviour identification in video use, and more.
- State of Deep Learning and Major Advances: H2 2018 Review
- Dec 13, 2018.
In this post we summarise some of the key developments in deep learning in the second half of 2018, before briefly discussing the road ahead for the deep learning community.
- Are you ready to tackle the data-driven revolution?
- Dec 13, 2018.
Get a Master's degree in Business Analytics combined with an International MBA from one of Europe's top 10 best business schools.
- Solve any Image Classification Problem Quickly and Easily
- Dec 13, 2018.
This article teaches you how to use transfer learning to solve image classification problems. A practical example using Keras and its pre-trained models is given for demonstration purposes.
- Top KDnuggets tweets, Dec 5-11: How to build a data science project from scratch; NeurIPS 2018 video talk collection #NeurIPS2018
- Dec 12, 2018.
Also 50+ Data Structure and Algorithms Interview Questions for Programmers; Feature Selection Techniques in Machine Learning with Python.
- Healthcare and Automated Machine Learning 101: How Healthcare Providers Can Adopt AI
- Dec 12, 2018.
AI, machine learning, and automated machine learning are transforming the healthcare industry and helping to solve some of today’s biggest healthcare challenges. In this DataRobot webinar, Dec 17, 1 PM EST, learn how AI technologies can help healthcare providers improve operational efficiency and patient experience.
- Four Approaches to Explaining AI and Machine Learning
- Dec 12, 2018.
We discuss several explainability techniques being championed today, including LOCO (leave one column out), permutation impact, and LIME (local interpretable model-agnostic explanations).
- Keras Hyperparameter Tuning in Google Colab Using Hyperas
- Dec 12, 2018.
In this post, I will show you how you can tune the hyperparameters of your existing keras models using Hyperas and run everything in a Google Colab Notebook.
- A Machine Learning Deep Dive [Webinar, Dec 13]
- Dec 11, 2018.
Learn how ShopRunner uses Databricks on AWS and Snowflake to tackle data science problems across personalization, recommendations, targeting, and analysis of text and images.
- Top November Stories: The Most in Demand Skills for Data Scientists; What is the Best Python IDE for Data Science?
- Dec 11, 2018.
Also: To get hired as a data scientist, don't follow the herd; 10 Free Must-See Courses for Machine Learning and Data Science.
- Machine Learning & AI Main Developments in 2018 and Key Trends for 2019
- Dec 11, 2018.
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2018 and their 2019 key trend predictions.
- Automated Web Scraping in R
- Dec 11, 2018.
How to automatically web scrape periodically so you can analyze timely/frequently updated data.
- Learning Machine Learning vs Learning Data Science
- Dec 11, 2018.
We clarify some important and often-overlooked distinctions between Machine Learning and Data Science, covering education, scalable vs non-scalable jobs, career paths, and more.
- Introduction to Named Entity Recognition
- Dec 11, 2018.
Named Entity Recognition is a tool which invariably comes handy when we do Natural Language Processing tasks. Read on to find out how.
- Expand Your Data Science Knowledge with Hands-on Labs and Bootcamp
- Dec 10, 2018.
Elevate your data skills with hands-on labs and bootcamp at TDWI Las Vegas, Feb 10-15. Super Early Bird ends Dec 14. KDnuggets readers save up to $915 with code KD20!
- Math for Machine Learning
- Dec 10, 2018.
This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.
- Should you become a data scientist?
- Dec 10, 2018.
An overview of the current situation for data scientists, from its origins and history, to the recent growth in job postings, and looking at what changes the future might bring.
- How Different are Conventional Programming and Machine Learning?
- Dec 10, 2018.
When I heard about Machine Learning I couldn't contain the amazement. I was not able to get my mind around the fact, that unlike normal software programs - which I was accustomed to - I wouldn't even have to teach a computer the "how" in detail about all the future scenarios up front.
- Top Stories, Dec 3-9: Common mistakes when carrying out machine learning and data science; AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019
- Dec 10, 2018.
Also: Data Science Projects Employers Want To See: How To Show A Business Impact; The Machine Learning Project Checklist; Here are the most popular Python IDEs / Editors; The Machine Learning Project Checklist
- Dr. Data Show Video: Five Reasons Computers Predict When You’ll Die
- Dec 8, 2018.
Death prediction is a very morbid topic indeed — but a very practical one. So how does it work? What technological sorcery is at play? And how accurate is it? And, gee whiz, what's it used for?
- Here are the most popular Python IDEs / Editors
- Dec 7, 2018.
We report on the most popular IDE and Editors, based on our poll. Jupyter is the favorite across all regions and employment types, but there is competition for no. 2 and no. 3 spots.
- Take a Look at Looker, Demo/Webinar Dec 13
- Dec 7, 2018.
Looker is designed for those building the next generation of data applications and analytic workflows. Join us for a live demonstration on Dec 13
- XGBoost on GPUs: Unlocking Machine Learning Performance and Productivity
- Dec 7, 2018.
On Dec 18, 11:00 AM PT, join NVIDIA for a technical deep dive into GPU-accelerated machine learning, to exploring the benefits of XGBoost on GPUs and much more.
- A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more
- Dec 7, 2018.
A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.
- The Machine Learning Project Checklist
- Dec 7, 2018.
In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow."
- One-stop-learning-shop for data pros – get exclusive access for less than a cup of coffee
- Dec 6, 2018.
TDWI Membership is the one-stop-learning-shop for data professionals, providing the necessary tools to move your career forward. Join in December for less than a cup of coffee.
- DATAx Presents: AI AND MACHINE LEARNING TRENDS IN 2019
- Dec 6, 2018.
This free Ebook from DATAx offers advice on using AI and machine learning to enhance customer satisfaction, how chief data officers are taking the reins on AI strategy, successful case studies from across the business, and more.
- Common mistakes when carrying out machine learning and data science
- Dec 6, 2018.
We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!
- Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies
- Dec 6, 2018.
The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.
- Four Techniques for Outlier Detection
- Dec 6, 2018.
There are many techniques to detect and optionally remove outliers from a dataset. In this blog post, we show an implementation in KNIME Analytics Platform of four of the most frequently used - traditional and novel - techniques for outlier detection.
- The Quick Python Book
- Dec 5, 2018.
Written for programmers new to Python, this latest edition includes new exercises throughout. It covers features common to other languages concisely, while introducing Python's comprehensive standard functions library and unique features in detail.
- Top KDnuggets tweets, Nov 28 – Dec 4: Deep Learning Cheat Sheets; Amazon opens its internal #machinelearning courses to all for free
- Dec 5, 2018.
Also: 7 Things You Need To Stop Doing To Be More Productive; #scikit-learn is used by 48% of @Kaggle champions, #Tensorflow by 16%, Keras by 14%; Linking Data Science Activities to Business Initiatives Using the Hypothesis Development Canvas
- How to build a data science project from scratch
- Dec 5, 2018.
A demonstration using an analysis of Berlin rental prices, covering how to extract data from the web and clean it, gaining deeper insights, engineering of features using external APIs, and more.
- 6 Step Plan to Starting Your Data Science Career
- Dec 5, 2018.
When people want to launch data science careers but haven't made the first move, they're in a scenario that's understandably daunting and full of uncertainty. Here are six steps to get started.
- Kick Start Your Data Career! Tips From the Frontline
- Dec 5, 2018.
I am going to provide very interesting and useful tips through this blog series which will help students to kick start their career in Data.
- Data Mining Book – Chapter Download
- Dec 4, 2018.
Download this immediately useful book chapter, and learn how to create derived variables, which allow the statistical and Data Science modeling to incorporate human insights.
- Open Source Data Science Adoption: The How & Why
- Dec 4, 2018.
Get the report on Enterprise Open Source Data Science Adoption which outlines the most popular open source tools for a host of jobs. Free download.
- Data Science Projects Employers Want To See: How To Show A Business Impact
- Dec 4, 2018.
The best way to create better data science projects that employers want to see is to provide a business impact. This article highlights the process using customer churn prediction in R as a case-study.
- Handling Imbalanced Datasets in Deep Learning
- Dec 4, 2018.
It’s important to understand why we should do it so that we can be sure it’s a valuable investment. Class balancing techniques are only really necessary when we actually care about the minority classes.
- Why Primary Research?
- Dec 4, 2018.
Primary studies have always been a strength of marketing research. Many younger marketing researchers, however, have only been exposed to standardized ready-made research products or big data. This is a concern. What is the point of the word research in marketing research?
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: December and Beyond
- Dec 3, 2018.
Coming soon: DataX New York, AI-2018 Cambridge UK, AI NEXTCon Seattle, Deep Learning Summit San Francisco, EGC France, H2O San Francisco, Business Of Bots Business of Bots San Francisco, TDWI Las Vegas, WSDM Melbourne, and more.
- AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019
- Dec 3, 2018.
Review of 2018 and Predictions for 2019 from our panel of experts, including Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Bill Schmarzo, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.
- Get ahead of your peers with a Certificate in Business Analytics
- Dec 3, 2018.
Move your career forward in one of the fields with the largest demand. Business Analytics at Clark University will give you the skills employers demand by teaching you how to synthesize data into powerful information.
- Graph-Powered Machine Learning
- Dec 3, 2018.
This book from Manning Publications is a wonderful introduction to graphs for machine learning enthusiasts, as well as a great entrée into machine learning for graph experts.
- Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools
- Dec 3, 2018.
We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.
- Top Stories, Nov 26 – Dec 2: Deep Learning Cheat Sheets; A Complete Guide to Choosing the Best Machine Learning Course
- Dec 3, 2018.
Also: A Complete Guide to Choosing the Best Machine Learning Course; My secret sauce to be in top 2% of a Kaggle competition; Deep Learning for the Masses ( and The Semantic Layer); What is the Best Python IDE for Data Science?; 9 Must-have skills you need to become a Data Scientist, updated
COVID-19 Visualized: The power of effective visualizations for pandemic storytelling
Coronavirus Data and Poll Analysis – yes, there is hope, if we act now