Analytics Vidhya
OCTOBER 14, 2019
Overview Here’s an intuitive and beginner friendly guide to the mathematics behind machine learning Learn the various math concepts required for machine learning, including. The post Mathematics behind Machine Learning – The Core Concepts you Need to Know appeared first on Analytics Vidhya.
KDnuggets
OCTOBER 16, 2019
A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning.
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Dataconomy
OCTOBER 16, 2019
If you’re a Data Scientist, you’ve likely spent months earnestly developing and then deploying a single predictive model. The truth is that once your model is built – that’s only half the battle won. A quarter of a Data Scientist’s working life often goes something like this: You met with. The post A Data Scientist’s relationship with building Predictive Models appeared first on Dataconomy.
Hacker News
OCTOBER 13, 2019
This is my advice to anyone writing something for the public — especially a talk on stage. People listen to a talk, or read an article, because they want to learn something new. They want a little “oh wow” moment. “I never thought of it that way before.”. People only really learn when they’re surprised. If they’re not surprised, then what you told them just fits in with what they already know.
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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
Analytics Vidhya
OCTOBER 13, 2019
Overview Knowledge graphs are one of the most fascinating concepts in data science Learn how to build a knowledge graph using text from Wikipedia. The post Knowledge Graph – A Powerful Data Science Technique to Mine Information from Text (with Python code) appeared first on Analytics Vidhya.
KDnuggets
OCTOBER 14, 2019
As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it’s the right approach for a given problem.
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
Smart Data Collective
OCTOBER 14, 2019
From the tech industry to retail and finance, big data is encompassing the world as we know it. More organizations rely on big data to help with decision making and to analyze and explore future trends. For current and future software development companies that want to be knowledgeable about using data and analysis, a few big data skillsets will help give them leverage in the coming year.
Analytics Vidhya
OCTOBER 16, 2019
Overview Swift is quickly becoming one of the most powerful and effective languages for data science Swift is quite similar to Python so you’ll. The post A Comprehensive Guide to Learn Swift from Scratch for Data Science appeared first on Analytics Vidhya.
KDnuggets
OCTOBER 17, 2019
While the average salary for a Software Engineer is around $100,000 to $150,000, to make the big bucks you want to be an AI or Machine Learning (Specialist/Scientist/Engineer.).
DataRobot
OCTOBER 17, 2019
In the past 12 months, artificial intelligence has become headline news, and not always for the right reasons. We’ve heard stories of sexist AI hiring algorithms , racist face detection algorithms , and AIs using your private social media data to influence elections. Embarrassing AI failures such as these can damage your reputation, incur regulatory penalties, and even negatively impact a company’s stock prices.
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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
Smart Data Collective
OCTOBER 14, 2019
Advances in mass storage and mobile computing brought about the phenomenon we now know as “big data.” These developments then ushered in solutions and tools that can process vast amounts of information — think terabytes of it or more — in real-time. That is how “big” the need for big data analytics came to be. More specifically, big data analytics offers users the ability to generate relevant insights from heaps of data.
Analytics Vidhya
OCTOBER 15, 2019
Overview Qlik is widely associated with powerful dashboards and business intelligence reports Did you know that you can use the power of Qlik to. The post Build your First Linear Regression Model in Qlik Sense appeared first on Analytics Vidhya.
KDnuggets
OCTOBER 17, 2019
This post aims to make you get started with putting your trained machine learning models into production using Flask API.
Depends on the Definition
OCTOBER 14, 2019
Last weekendPyCon DE and PyData Berlin joined in Berlin for a great conference event that I was lucky to attend. The speaker line-up was great and often it was hard to choose which talk or tutorial to attend.
Speaker: Mike Rizzo, Founder & CEO, MarketingOps.com and Darrell Alfonso, Director of Marketing Strategy and Operations, Indeed.com
Though rarely in the spotlight, marketing operations are the backbone of the efficiency, scalability, and alignment that define top-performing marketing teams. In this exclusive webinar led by industry visionaries Mike Rizzo and Darrell Alfonso, we’re giving marketing operations the recognition they deserve! We will dive into the 7 P Model —a powerful framework designed to assess and optimize your marketing operations function.
Smart Data Collective
OCTOBER 14, 2019
Regardless of if you’re a data science professional or an IT department who wants to help your company have more successful data science projects, it’s essential to have some data science tools under your belt to avail of when needed. Here are some open-source options to consider. 1. Ludwig. Ludwig is a tool that allows people to build data-based deep learning models to make predictions.
DataCentric podcast
OCTOBER 14, 2019
As ARM TechCon 2019 wraps up in San Jose, hosts Steve McDowell and Matt Kimball from Moor Insights & Strategy are joined by Patrick Kennedy of Serve the Home in a wide-ranging discussion of the entire microprocessor market, and where ARM fits in. Here's a general timeline for the discussion, though the topics overlap, so you owe it to yourself to listent to the entire thing. 00:00 Intros 02:20 Big Themes & Announcements from TechCon 03:50 Patrick's views of TechCon from a server,
KDnuggets
OCTOBER 18, 2019
If you want to launch your data science skills into freelance work, then check out these important tips to help you kick start your next adventure in data.
Alation
OCTOBER 16, 2019
Whether we’re speaking to data analysts or CDOs, data people almost instantly understand the value of the Alation Data Catalog. Faces light up when we describe how Alation helps enterprises find, understand, trust, use and reuse data. The response is usually some form of, “exactly, that’s the problem my company needs to solve!” At some […]. The post Forrester Does the Math on the ROI of the Alation Data Catalog appeared first on Alation.
Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin
As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.
Twilio Segment
OCTOBER 17, 2019
How we managed to reduce our infrastructure cost by 30%. And how you can too.
DataRobot
OCTOBER 18, 2019
There have been anecdotal complaints that the postseason baseball is deader than its regular-season counterpart, so we used DataRobot to analyze 100k+ batted balls in 2019.
KDnuggets
OCTOBER 14, 2019
Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications.
KDnuggets
OCTOBER 14, 2019
Density estimation is estimating the probability density function of the population from the sample. This post examines and compares a number of approaches to density estimation.
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Cloud Development Environments (CDEs) are changing how software teams work by moving development to the cloud. Our Cloud Development Environment Adoption Report gathers insights from 223 developers and business leaders, uncovering key trends in CDE adoption. With 66% of large organizations already using CDEs, these platforms are quickly becoming essential to modern development practices.
KDnuggets
OCTOBER 16, 2019
This post is about various evaluation metrics and how and when to use them.
KDnuggets
OCTOBER 18, 2019
Read this quick overview of neural networks and learn how to implement your first in very few lines using Keras.
KDnuggets
OCTOBER 15, 2019
In this research guide, we’ll look at deep learning papers aimed at synthesizing video frames within an existing video.
KDnuggets
OCTOBER 15, 2019
The way we control our data isn’t working. Data is as vulnerable as ever. Download this white paper, which outlines lessons about how data science and governance programs can, if implemented properly, reinforce each other’s objective.
Speaker: Nikhil Joshi, Founder & President of Snic Solutions
Is your manufacturing operation reaching its efficiency potential? A Manufacturing Execution System (MES) could be the game-changer, helping you reduce waste, cut costs, and lower your carbon footprint. Join Nikhil Joshi, Founder & President of Snic Solutions, in this value-packed webinar as he breaks down how MES can drive operational excellence and sustainability.
KDnuggets
OCTOBER 17, 2019
While effective anonymization technology remains elusive, understanding the history of this challenge can guide data science practitioners to address these important concerns through ethical and responsible use of sensitive information.
KDnuggets
OCTOBER 18, 2019
In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used.
KDnuggets
OCTOBER 16, 2019
This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.
KDnuggets
OCTOBER 16, 2019
Also: Kannada-MNIST: A new handwritten digits dataset in ML town; Math for Programmers; The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization; The Last SQL Guide for Data Analysis You’ll Ever Need.
Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage
When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.
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