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.
So what does it take to become a data scientist? For some pointers on the skills for success, I interviewed Ben Chu, who is a Senior Data Scientist at Refinitiv Labs.
A greatly expanded v2.0 of the open-source Orbyter toolkit helps data science teams continue to streamline machine learning delivery pipelines, with an emphasis on seamless deployment to production.
This article explains why TOPS isn’t as accurate a gauge as many people think, and discusses other criteria that should be considered when evaluating a solution to a real application.
There is still a long way to go before machine agents match overall human gaming prowess, but Deepmind’s gaming research focus has shown a clear progression of substantial progress.
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.
One of the most exciting features of StellarGraph 1.0 is a new graph data structure — built using NumPy and Pandas — that results in significantly lower memory usage and faster construction times.
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.
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
Stanford's Natural Language Processing with Deep Learning is one of the most respected courses on the topic that you will find anywhere, and the course materials are freely available online.
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.
Also: Everything you need to become a self-taught #MachineLearning Engineer ; SQL Cheat Sheet (2020) - a useful cheat sheet that documents some of the more commonly used elements of SQL;
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.
We show a comparative performance benchmarking of Julia with an equivalent Python code to show why Julia is great for data science and machine learning.
There's a lot of excitement out there about machine learning jobs. So, it's always good to start off with a healthy dose of reality and proper expectations.
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.
Python comes with a lot of handy and easily accessible libraries and we’re going to look at how we can deliver text-to-speech with Python in this article.
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.
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.
Traditional business and technology sectors are not the only fields being impacted by AI. Healthcare is a field that is thought to be highly suitable for the applications of AI tools and techniques.
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.
DeepMind has been sharing resources for learning AI at home on their Twitter account. Check out a few of these suggestions here, and keep your eye on the #AtHomeWithAI hashtag for more.
With so many pioneering online resources for open education, check out this organized collection of courses you can follow to become a well-rounded machine learning and AI engineer.
This article details an automated machine-learned approach to predict customer churn and its results across selected communication service providers around the globe.
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.
The majority of data exists in the textual form which is a highly unstructured format. In order to produce meaningful insights from the text data then we need to follow a method called Text Analysis.
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.
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.
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.
How to Think Like a Data Scientist
Model Evaluation Metrics in Machine Learning
The Best NLP with Deep Learning Course is Free
Natural Language Processing Recipes: Best Practices and Examples
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