Nov2018 Dec
All (51) | Courses, Education (3) | Meetings (3) | News (8) | Opinions (14) | Top Stories, Tweets (6) | Tutorials, Overviews (13) | Webcasts & Webinars (4)
- 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?
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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.
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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