After much consideration, the General Chairs, Executive Committee and Organizing Committee for KDD 2020 have decided to take the conference fully virtual. Clear your calendar for August 23-27, 2020, and enjoy access to all the virtual content live and on demand the week of the event.
Various data privacy threats can result from the usual process of building and constructing data and AI-based systems. Avoiding these challenges can be supported by utilizing state-of-the-art technologies in the domain of privacy-preserving AI.
Also: #Linearalgebra and optimization and machine learning: A textbook; Everything you need to become a self-taught #MachineLearning Engineer ; #SQL Cheat Sheet (2020); Automated Machine Learning: The Free eBook - KDnuggets
Geographic Information Systems Analysis is the analysis of spatial relationships and patterns. Spatial components are being ingrained into society with the advent of the Internet of Things (IoT) in which more data can be connected and is likely to have a spatio-temporal component as well.
To help you truly rock your next virtual data interview, we’ve pulled together a few tips that we recommend when conducting our online interviews for The Data Incubator’s Data Science Fellowship Program.
If you are splitting your dataset into training and testing data you need to keep some things in mind. This discussion of 3 best practices to keep in mind when doing so includes demonstration of how to implement these particular considerations in Python.
Dive into experimenting with machine learning techniques using this open-source collection of interactive demos built on multilayer perceptrons, convolutional neural networks, and recurrent neural networks. Each package consists of ready-to-try web browser interfaces and fully-developed notebooks for you to fine tune the training for better performance.
Microsoft is bringing the latest research in responsible AI to Azure (both Azure Machine Learning and their open source toolkits), to empower data scientists and developers to understand machine learning models, protect people and their data, and control the end-to-end machine learning process.
While you may be a data scientist, you are still a developer at the core. This means your code should be skillful. Follow these 10 tips to make sure you quickly deliver bug-free machine learning solutions.
Also: Automated Machine Learning: The Free eBook; Sparse Matrix Representation in Python; Build and deploy your first machine learning web app; Complex logic at breakneck speed: Try Julia for data science
Many statisticians in industry agree that blindly imputing the missing values in your dataset is a dangerous move and should be avoided without first understanding why the data is missing in the first place.
Agile project management for Data Science development continues to be an effective framework that enables flexibility and productivity in a field that can experience continuous changes in data and evolving stakeholder expectations. Learn more about the leading approaches for developing Data Science models, and apply them to your next project.
Caserta is offering a limited number of virtual pro-bono data and analytics workshops conducted by industry leaders Joe Caserta and Doug Laney exclusively for eligible senior leadership. Learn more and sign up now.
In this article, I’ll introduce you to a hot-topic in financial services and describe how a leading data provider is using data science and NLP to streamline how they find insights in unstructured data.
Pandas is instantly familiar to anyone who’s used spreadsheet software, whether that’s Google Sheets or good old Excel. It’s got columns, it’s got grids, it’s got rows; but pandas is far more powerful. Save 40% with code nlkdpandas40 on this book, and other Manning books and videos.
This book teaches linear algebra and optimization as the primary topics of interest, and solutions to machine learning problems as applications of these methods. Therefore, the book also provides significant exposure to machine learning.
Big Data generated by people -- such as, social media posts, mobile phone GPS locations, and browsing history -- provide enormous prediction value for AI systems. However, explaining how these models predict with the data remains challenging. This interesting explanation approach considers how a model would behave if it didn't have the original set of data to work with.
Check out 5 new features of the latest Scikit-learn release, including the ability to visualize estimators in notebooks, improvements to both k-means and gradient boosting, some new linear model implementations, and sample weight support for a pair of existing regressors.
AI is certainly playing an important role in our global fight against the novel coronavirus. These YouTube channels are recommended to keep you covered with the latest advancements in the field and how it is impacting our world.
While the core machine learning algorithms might only take up a few lines of code, it's the rest of your program that can get messy fast. Learn about some techniques for identifying bad coding habits in ML that add to complexity in code as well as start new habits that can help partition complexity.
We were asked to build ML models using the novel xBD dataset provided by the organizers to estimate damage to infrastructure with the goal of reducing the amount of human labour and time required to plan an appropriate response. This article will focus on the technical aspects of our solution and share our experiences.
How does deep learning solve the challenges of scale and complexity in reinforcement learning? Learn how combining these approaches will make more progress toward the notion of Artificial General Intelligence.
Also: How use the Coronavirus crisis to kickstart your Data Science career; 5 Concepts You Should Know About Gradient Descent and Cost Function; Five Cool Python Libraries for Data Science; Natural Language Processing Recipes: Best Practices and Examples
As can be common in many technical fields, the landscape of specialized roles is evolving quickly. With more people learning at least a little machine learning, this could eventually become a common skill set for every software engineer.
Understanding the types of statistical bias that pop up in popular media and reporting is especially important during this pandemic where the data -- and our global response to the data -- directly impact peoples' lives.
At present, the data scientist is one of the most sought after professions. That’s one of the main reasons why we decided to cover the latest data visualization tools that every data scientist can use to make their work more effective.
Also: Coronavirus COVID-19 Genome Analysis using Biopython; LSTM for time series prediction; A Concise Course in Statistical Inference: The Free eBook; Exploring the Impact of Geographic Information Systems
AI tools and services are expanding at a rapid clip, and keeping a handle on this evolving ecosystem is crucial for the success of your AI projects. This framework will help you build your technical stack to deploy AI projects faster and at scale.
Here is an overview of another great natural language processing resource, this time from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.