Edge analytics is considered to be the future of sensor handling, and this article discusses its benefits and architecture of modern edge devices, gateways, and sensors. Deep Learning for edge analytics is also considered along with a review of experiments in human and chess figure detection using edge devices.
While the use of Decision Trees in machine learning has been around for awhile, the technique remains powerful and popular. This guide first provides an introductory understanding of the method and then shows you how to construct a decision tree, calculate important analysis parameters, and plot the resulting tree.
This article introduces the easy to use blogging platform fastpages. fastpages relies on Github pages for hosting, and Github Actions to automate the creation of your blog, and contains extra features for Jupyter Notebooks.
Are you asking the question, "how do I become a Data Scientist?" This list recommends the best essential topics to gain an introductory understanding for getting started in the field. After learning these basics, keep in mind that doing real data science projects through internships or competitions is crucial to acquiring the core skills necessary for the job.
Since Python and R are a must for today's data scientists, continuous learning is paramount. Online courses are arguably the best and most flexible way to upskill throughout ones career.
Some machine learning models are designed to work best under some distribution assumptions. Therefore, knowing with which distributions we are working with can help us to identify which models are best to use.
It's no secret that mathematics is the foundation of data science. Here are a selection of courses to help increase your maths skills to excel in data science, machine learning, and beyond.
This is a followup to the first article in this series. Once you are comfortable with the concepts explained in that article, you can come back and continue with this.
The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.
While deepfakes threaten to destroy our perception of reality, the tech giants are throwing down the gauntlet and working to enhance the state of the art in combating doctored videos and images.
There are plenty of ways to get actionable results by using passive data. However, such an outcome will not happen without careful forethought. Data analysts must consider several crucial specifics, including what questions they want and expect the information to answer, and how they'll apply the findings to aid the business.
As Kubernetes is capable of working with other solutions, it is possible to integrate it with a collection of tools that can almost fully automate your development pipeline. Some of those third-party tools even allow you to integrate AI into Kubernetes. One such tool you can integrate with Kubernetes is Kubeflow. Read more about it here.
Soon after tech giants Google and Microsoft introduced their AutoML services to the world, the popularity and interest in these services skyrocketed. We first review AutoML, compare the platforms available, and then test them out against real data scientists to answer the question: will AutoML replace us?
Data labeling is so hot right now… but could this rapidly emerging market face disruption from a small team at Stanford and the Snorkel open source project, which enables highly efficient programmatic labeling that is 10 to 1,000x as efficient as hand labeling?
Fitbit provides a Web API for accessing data from Fitbit activity trackers. Check out this updated tutorial to accessing this Fitbit data using the API with Python.
The educational and research focuses of machine learning tends to highlight the model building, training, testing, and optimization aspects of the data science process. To bring these models into use requires a suite of engineering feats and organization, a standard for which does not yet exist. Learn more about a framework for operating a collaborative data science and engineering team to deploy machine learning models to end-users.
With recent developments in machine learning and computer vision, we acquired the tools to provide the biodiversity community with an ability to tap the potential of the knowledge generated automatically with systems triggered by a combination of heat and motion.
You are a Data Scientist who knows how to develop machine learning models. You might also be a Data Scientist who is too afraid to ask how to deploy your machine learning models. The answer isn't entirely straightforward, and so is a major pain point of the community. This article will help you take a step in the right direction for production deployments that are automated, reproducible, and auditable.
When reviewing geographical data, it can be difficult to prepare the data for an analysis. This article helps by covering importing data into a SQL Server database; cleansing and grouping data into a map grid; adding time data points to the set of grid data and filling in the gaps where no crimes occurred; importing the data into R; running XGBoost model to determine where crimes will occur on a specific day
Learn how to implement adversarial validation that builds a classifier to determine if your data is from the training or testing sets. If you can do this, then your data has issues, and your adversarial validation model can help you diagnose the problem.
An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.
What can we do when we don't have a substantial amount of varied training data? This is a quick intro to using data augmentation in TensorFlow to perform in-memory image transformations during model training to help overcome this data impediment.
Snagging that job as a Data Scientist might not be exactly what you were expecting. Consider this advice on carefully considering job titles with what the position might really be like day-to-day.
Predictive Analytics World for Financial Services in Las Vegas, May 31-Jun 4 is honored to host an exceptional keynote by Fidelity Investments’ AI and Data Science Center of Excellence Leader, Victor Lo: "How to Find a Tailor-Fit 'Unicorn' Data Scientist for Financial Services". Use the code KDNUGGETS for a 15% discount on your Predictive Analytics World ticket.
While much focus today is on the rise in working from home and the challenges experienced, not as much is said about learning from home. For those lone wolfs studying Data Science in a self-directed way, a range of issues can get in the way of your goal. Learn about these common problems to prepare to focus yourself all the way to your educational goals.
This post provides basic information on audio processing using R as the programming language. It also walks through and understands some basics of sound and digital audio.
This article will discuss a sometimes-overlooked aspect of what distinguishes recommender systems from other machine learning tasks: added uncertainties of measuring them.
Going beyond traditional monitoring techniques and goals, understanding if a system is working as intended requires a new concept in DevOps, called Observability. Learn more about this essential approach to bring more context to your system metrics.
TL;DR Learn how to fine-tune the BERT model for text classification. Train and evaluate it on a small dataset for detecting seven intents. The results might surprise you!
The curiosity and buzz around the most talked-about technology -- Artificial Intelligence -- have experts and technophiles busy decoding its exciting future applications. Of course, the use of AI and machine learning is already pervasive in our daily lives, as we review many of these popular features in this article.
The data science puzzle is once again re-examined through the relationship between several key concepts of the landscape, incorporating updates and observations since last time. Check out the results here.
HDBSCAN is a robust clustering algorithm that is very useful for data exploration, and this comprehensive introduction provides an overview of its fundamental ideas from a high-level view above the trees to down in the weeds.
This tutorial covers how to download and install Anaconda on Windows; how to test your installation; how to fix common installation issues; and what to do after installing Anaconda.
Machine learning information is becoming pervasive in the media as well as a core skill in new, important job sectors. Getting started in the field can require learning complex concepts, and this article outlines an approach on how to begin learning about these exciting topics based on high school knowledge.
Expedite the deployment of your machine models using serverless cloud infrastructure. In this tutorial, we explore creating and deploying a model which scraps real time Twitter data and returns interactive visualization using R.
What makes deploying a machine learning project so difficult? Is it the expectations? The people? The tech? There are common threads to these challenges, and best practices exist to deal with them.
The Big Bad NLP Database: Access Nearly 300 Datasets
Free Mathematics Courses for Data Science & Machine Learning
The Death of Data Scientists – will AutoML replace them?
Audio Data Analysis Using Deep Learning with Python (Part 1)
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