KDnuggets

OCTOBER 28, 2019

Learn how Bayes Theorem is in Machine Learning for classification and regression!

Machine Learning 306

Machine Learning Machine Learning 306

Analytics Vidhya

OCTOBER 27, 2019

Overview Big Data is becoming bigger by the day, and at an unprecedented pace How do you store, process and use this amount of. The post PySpark for Beginners – Take your First Steps into Big Data Analytics (with Code) appeared first on Analytics Vidhya.

Big Data Analytics 284

Big Data Analytics Big Data Analytics Big Data Big Data 284

Dataconomy

OCTOBER 30, 2019

Aleksandar Kova?evi?, Sales Engineer at InterSystems, shares how companies use MLOps combined with a central multi-model database to get the most out of their machine learning initiatives. Artificial Intelligence (AI) and Machine Learning (ML) are hot topics at the moment. But when it comes to producing quantifiable results, there is. The post MLOps can help overcome risk in AI and ML projects appeared first on Dataconomy.

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ML ML Machine Learning Machine Learning 183

Smart Data Collective

OCTOBER 30, 2019

Machine learning is changing the future of marketing in many beneficial ways. The Digital Marketing Institute reports that 97% of decision makers believe it is the future of marketing. There are a number of tactics that marketers can pursue to optimize campaigns with machine learning algorithms. However, some of these strategies are more limited then marketers would like to think.

Machine Learning 93

Machine Learning Machine Learning Big Data Big Data 93

Speaker: Tamara Fingerlin, Developer Advocate

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

Analytics

KDnuggets

OCTOBER 30, 2019

Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid.

Data Scientist 304

Data Scientist 304

Analytics Vidhya

OCTOBER 30, 2019

Overview Gaussian Mixture Models are a powerful clustering algorithm Understand how Gaussian Mixture Models work and how to implement them in Python We’ll also. The post Build Better and Accurate Clusters with Gaussian Mixture Models appeared first on Analytics Vidhya.

Clustering 268

Clustering Python Algorithm Analytics 268

Smart Data Collective

NOVEMBER 1, 2019

Machine learning is offering businesses a new opportunity to translate documents. They can use machine learning to translate marketing materials and other literature. However, these AI solutions may not always be the best. Brief Overview of Neural Machine Learning. Towards Data Science has discussed this development. The term is called neural machine translation.

Machine Learning 91

Machine Learning Machine Learning Data Science AI 91

KDnuggets

OCTOBER 31, 2019

A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer.

Data Scientist 300

Data Scientist Machine Learning Machine Learning Python 300

Analytics Vidhya

OCTOBER 29, 2019

Do you want to build your own smart city? Picture it – self-driving cars strolling around, traffic lights optimised to maintain a smooth flow, The post Here are 8 Powerful Sessions to Learn the Latest Computer Vision Techniques appeared first on Analytics Vidhya.

Analytics 264

Analytics Analytics Deep Learning Deep Learning 264

Data Science 101

OCTOBER 31, 2019

Today we are generating data more than ever before. Over the last two years, 90 percent of the data in the world was generated. This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD).

Data Mining 77

Data Mining Data Mining Data Mining Data Science 77

Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage

There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.

AI

Smart Data Collective

OCTOBER 30, 2019

Many of our colleagues have written extensively about the impact big data is having on the gaming industry. They have said that big data technology is probably the biggest game changer in the industry. Kevin Rands of CIO is one of the experts to discuss the state of big data in the gaming industry. He said big data analysis gives companies a long-term strategy that helps them gain the strongest competitive edge.

Big Data 86

Big Data Big Data Machine Learning Machine Learning 86

KDnuggets

NOVEMBER 1, 2019

This article focused on building an Artificial Neural Network using the Numpy Python library.

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Python 265

DataRobot

NOVEMBER 1, 2019

There is a saying, “Be careful what you wish for; you might just receive it.” One of the first-ever stories to exemplify this saying comes from Greek mythology. The story goes that Midas earned the favor of Silenus, a satyr. Silenus offered Midas a wish, and Midas wished that everything he touched would turn to gold. Immediately, Midas put the power to the test, touching a rose and turning it into solid gold.

Data Science 14

Data Science AI AI 14

KDnuggets

NOVEMBER 1, 2019

As a developer who is excited about leveraging machine learning for faster and more effective development, these software tools are worth trying out.

Machine Learning 246

Machine Learning Machine Learning 246

Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives

Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri

AI

KDnuggets

OCTOBER 28, 2019

Data collection is one of the first steps of the data lifecycle — you need to get all the data you require in the first place. To collect the right data, you need to know where to find it and determine the effort involved in collecting it. This article answers the most basic question: where does all the data you need (or might need) come from?

Data Science 200

Data Science 200

KDnuggets

OCTOBER 31, 2019

Learn how to approach the challenges when merging an agile methodology into a data science team to bring out the best value your Big Data products.

Big Data 179

Big Data Big Data Big Data Analytics Big Data Analytics 179

KDnuggets

OCTOBER 29, 2019

Google claimed quantum supremacy, IBM challenged it… but the development is really important for the future of AI.

Artificial Intelligence 172

Artificial Intelligence Artificial Intelligence AI AI 172

KDnuggets

OCTOBER 29, 2019

Developing an excellent machine learning model is one thing. Deploying it to production is another. Consider these lessons learned and recommendations for approaching this important challenge to help ensure value from your AI work.

Machine Learning 170

Machine Learning Machine Learning AI AI 170

Speaker: Frank Taliano

Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.

AI

KDnuggets

NOVEMBER 1, 2019

This live webinar, Nov 14 @ 12pm EST, on MLOps for production-level machine learning, will detail MLOps, a compound of “machine learning” and “operations”, a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Register now.

Machine Learning 169

Machine Learning Machine Learning Data Scientist 169

KDnuggets

OCTOBER 30, 2019

The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning.

Deep Learning 168

Deep Learning Deep Learning 168

KDnuggets

OCTOBER 29, 2019

In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.

Machine Learning 153

Machine Learning Machine Learning Python 153

KDnuggets

OCTOBER 31, 2019

AI-based models are highly dependent on accurate, clean, well-labeled, and prepared data in order to produce the desired output and cognition. These models are fed with bulky datasets covering an array of probabilities and computations to make its functioning as smart and gifted as human intelligence.

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AI AI Data Preparation 151

Speaker: Chris Townsend, VP of Product Marketing, Wellspring

Over the past decade, companies have embraced innovation with enthusiasm—Chief Innovation Officers have been hired, and in-house incubators, accelerators, and co-creation labs have been launched. CEOs have spoken with passion about “making everyone an innovator” and the need “to disrupt our own business.” But after years of experimentation, senior leaders are asking: Is this still just an experiment, or are we in it for the long haul?

KDnuggets

OCTOBER 28, 2019

Also: Introduction to Natural Language Processing (NLP); Anomaly Detection, A Key Task for AI and Machine Learning, Explained; How to Become a (Good) Data Scientist — Beginner Guide.

Data Scientist 139

Data Scientist Natural Language Processing Machine Learning Machine Learning 139

KDnuggets

OCTOBER 28, 2019

Visualizing the datasets is an essential component to identify potential sources of bias and unfairness. DeepMind relied on a method called Causal Bayesian networks (CBNs) to represent and estimate unfairness in a dataset.

Machine Learning 133

Machine Learning Machine Learning 133

KDnuggets

NOVEMBER 1, 2019

Not only can MLonCode help companies streamline their codebase and software delivery processes, but it also helps organizations better understand and manage their engineering talents.

Machine Learning 132

Machine Learning Machine Learning 132

KDnuggets

OCTOBER 30, 2019

While AutoML started out as an automation approach to develop optimal machine learning pipelines, extensions of AutoML to Data Science embedded products can now enable the processing of much more, including temporal relational data.

Machine Learning 131

Machine Learning Machine Learning Data Science 131

Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage

When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m

KDnuggets

OCTOBER 31, 2019

These results will go into each each region and employment type to find out the differences and similarities especially between people from Industry and Students.

Data Science 125

Data Science Data Visualization Deep Learning Deep Learning 125

KDnuggets

OCTOBER 30, 2019

This week in KDnuggets: Feature Selection: Beyond feature importance?; Time Series Analysis: A Simple Example with KNIME and Spark; 5 Advanced Features of Pandas and How to Use Them; How to Measure Foot Traffic Using Data Analytics; Introduction to Natural Language Processing (NLP); and much, much more!

Natural Language Processing 125

Natural Language Processing Analytics Analytics Python 125

KDnuggets

OCTOBER 30, 2019

Also: Highest paid positions in 2019 are DevOps, Data Scientist, Data Engineer (all over $100K) - Stack Overflow Salary Calculator, Updated; A neural net solves the three-body problem 100 million times faster; The Last SQL Guide for Data Analysis You’ll Ever Need; How YouTube is Recommending Your Next Video.

Machine Learning 123

Machine Learning Machine Learning Data Scientist SQL 123

KDnuggets

OCTOBER 28, 2019

DataTech is a one-day conference on 16 Mar 2020, at the Technology and Innovation Centre in Glasgow, focusing on key topics in data science, and welcoming members of industry, academia, and the public sector alike. DataTech provides a forum for these different communities to meet, share knowledge and expertise, and forge new collaborations. We are currently welcoming workshop, talk and poster proposals for the DataTech20 conference.

Data Science 123

Data Science 123

Speaker: Tamara Fingerlin, Developer Advocate

In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!