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.
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.
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.
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.
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?
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.
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.
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.
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.
A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.
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.
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.
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
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.
A brief introduction to feature engineering, covering coordinate transformation, continuous data, categorical features, missing values, normalization, and more.
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.
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.
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!
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.
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.
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!
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!
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!
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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
We discuss several explainability techniques being championed today, including LOCO (leave one column out), permutation impact, and LIME (local interpretable model-agnostic explanations).
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.
Learn how ShopRunner uses Databricks on AWS and Snowflake to tackle data science problems across personalization, recommendations, targeting, and analysis of text and images.
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.
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.
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!
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.
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.
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
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?
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.
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.
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."
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.
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.
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!
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.
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.
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.
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
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.
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
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
Papers with Code: A Fantastic GitHub Resource for Machine Learning
A Guide to Decision Trees for Machine Learning and Data Science
Why You Shouldn’t be a Data Science Generalist
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