Analytics Vidhya
MARCH 31, 2019
Introduction What’s the first thing you do when you’re attempting to cross the road? We typically look left and right, take stock of the. The post Computer Vision Tutorial: A Step-by-Step Introduction to Image Segmentation Techniques (Part 1) appeared first on Analytics Vidhya.
Dataconomy
MARCH 14, 2019
Here is how Big Data can help you in your growth strategies in ecommerce Ecommerce is taking a bigger slice of the global retail pie. In the US, for example, ecommerce currently accounts for approximately 10% of all retail sales, a number that’s projected to swell to nearly 18% by. The post Data’s Growing Role in Scalable Ecommerce? appeared first on Dataconomy.
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Eugene Yan
MARCH 5, 2019
How we built an ML system to predict hospitalization costs at admission; sharing at DATAx Conference.
DataRobot
MARCH 29, 2019
AI has tremendous potential for benefiting humanity in every area of how we live and work. While most people realize this fact, their hopes for AI also come with a note of caution. A recent survey reported that 77% of Americans expressed that AI would have a “very positive” or “mostly positive” impact on how people work and live in the next 10 years.
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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?
Data Science 101
MARCH 16, 2019
Recently updated, is the March 2019 Machine Learning Study Path. It contains links and resources to learn Tensorflow and Scikit-Learn. If you are interested in details on the study path and how to best use the resources. There is a livestream on Facebook, Sunday March 17 on the Math for Data Science Facebook page.
Smart Data Collective
MARCH 28, 2019
If someone asked you which industry is the most innovative , you probably wouldn’t say the financial industry. In fact, that would probably be the last industry on your list. Nonetheless, the financial industry is using big data more than ever. The success of both Fintech companies and traditional banks will hinge on their ability to leverage big data to its fullest potential.
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
Dataconomy
MARCH 28, 2019
What do fat fingers, power failures, and Godzilla have in common? Answer: They have all been responsible for IT outages that, even if they aren’t caused by Godzilla, can cause monster-sized budget holes for companies. How monster-sized? A Rand Organization report says that according to 98% of organizations, IT outages. The post Why There Are IT Outages – And What to Do About Them?
Victor Zhou
MARCH 3, 2019
Here’s something that might surprise you: neural networks aren’t that complicated! The term “neural network” gets used as a buzzword a lot, but in reality they’re often much simpler than people imagine. This post is intended for complete beginners and assumes ZERO prior knowledge of machine learning. We’ll understand how neural networks work while implementing one from scratch in Python.
DataRobot
MARCH 20, 2019
The 2019 NCAA March Madness tournament has arrived! This is one of the most famous annual sports events in the United States, bringing together the best Division 1 men’s and women’s college basketball teams from 68 schools to compete against each other for the NCAA Champion title.
Data Science 101
MARCH 7, 2019
Read to the end to learn more about a new study group I will be launching. In Late January 2019, Microsoft launched 3 new certifications aimed at Data Scientists/Engineers. For a while, Microsoft has been toying with different methods for training and credentials. They launched the Microsoft Professional Program in Data Science back in 2017. While it provides great content, it did not result in either a college diploma or an official Microsoft certification.
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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.
Smart Data Collective
MARCH 27, 2019
“Google Search Reveals Community College Student’s Social Security Number.” While this may seem like a headline you would find on sites like The Onion , this is something that actually happened. This situation occurred when staff members at a community college started to test a new type of online application that utilized files full of sensitive and unaltered data on a server that was not secure.
Analytics Vidhya
MARCH 13, 2019
Introduction I have been a programmer since before I can remember. I enjoy writing codes from scratch – this helps me understand that topic. The post 5 Amazing Deep Learning Frameworks Every Data Scientist Must Know! (with Illustrated Infographic) appeared first on Analytics Vidhya.
Dataconomy
MARCH 19, 2019
A few years ago, I combined my day job as a data engineer, helping customers engineer data analytics, with my love for football and set out to beat the bookies. It’s an exciting game to play, and I have learned a lot about the merits of different predictive models that. The post How to apply predictive analytics to Premiership football to beat the bookies?
ML Review
MARCH 31, 2019
Are the machine learning models we use intrinsically flawed? What are adversarial attacks? In 2014, a group of researchers at Google and NYU found that it was far too easy to fool ConvNets with an imperceivable, but carefully constructed nudge in the input. Up to this point, machine learning algorithms simply didn’t work well enough for anyone to be surprised when it failed to do the right thing.
Speaker: Scott Sehlhorst
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
DataRobot
MARCH 25, 2019
Data scientist time is a precious, expensive commodity. Do you truly understand what your data science talent works on all day? Are they spending way too much time researching data science theory, coding the same data preparation tasks over and over again, and maintaining scripts for model factories? Take a serious look at what your data scientists actually do.
Data Science 101
MARCH 9, 2019
Machine Learning for Kids is a site for children and teachers to explore machine learning with the Scratch Programming language. It includes numerous lessons and tutorials for building fun programs which incorporate machine learning.
Smart Data Collective
MARCH 28, 2019
The Internet of Things is one of the most groundbreaking trends affecting consumers and businesses all over the world. According to a report by Gartner, the economic impact of all products connected to the IoT will exceed $300 billion by next year. A number of factors are contributing to the proliferation of the IoT. One of the most influential changes is the increasing capacity of big data.
Analytics Vidhya
MARCH 10, 2019
Introduction I work on different Natural Language Processing (NLP) problems (the perks of being a data scientist!). Each NLP problem is a unique challenge in. The post A Step-by-Step NLP Guide to Learn ELMo for Extracting Features from Text appeared first on Analytics Vidhya.
Speaker: Timothy Chan, PhD., Head of Data Science
Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.
Dataconomy
MARCH 7, 2019
Digital transformation dominates most CIO priority lists pertaining to questions such as: How will digital transformation affect IT infrastructure? Will technology live on-premise or in the cloud? Depending on where that data lives, an organization requires different skill sets. If you’re building these resources in-house, then you need an infrastructure.
Victor Zhou
MARCH 29, 2019
If you look at the documentation for the DecisionTreeClassifier class in scikit-learn , you’ll see something like this for the criterion parameter: The RandomForestClassifier documentation says the same thing. Both mention that the default criterion is “gini” for the Gini Impurity. What is that?! TLDR: Read the Recap. Decision Trees ? Training a decision tree consists of iteratively splitting the current data into two branches.
DataRobot
MARCH 14, 2019
For the Philadelphia 76ers, data is an integral part of how they work, helping them make strategic decisions on both the sports and corporate sides of the organization. On the business side, they knew that taking a data-driven approach could help them become much more efficient in how they approach their ticket sales process. Enter automated machine learning with the layup.
Data Science 101
MARCH 6, 2019
University can be a great way to learn data science. However, many universities are very expensive, difficult to get admitted, or not geographically feasible. Luckily, a few of them are willing to share data science, machine learning and deep learning materials online for everyone. Here is just I small list I have come across lately. MIT Deep Learning – Lecture notes, slides and guest talks about deep learning and self driving cars Introduction to Artificial Intelligence from UC Berkeley &
Speaker: Kevin Kai Wong, President of Emergent Energy Solutions
In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.
Smart Data Collective
MARCH 26, 2019
Forbes contributor Kalev Leetaru recently wrote a fantastic article about the intersection of big data and website hosting. Leetaru notes that big data and cloud technology have led to the evolution of web hosting services. Cloud technology is changing the logistics of many traditional hosting plans. WordPress hosting is a prime example. How Big Data is Changing the Future of WordPress.
Analytics Vidhya
MARCH 24, 2019
Introduction Computer vision is among the hottest fields in any industry right now. It is thriving thanks to the rapid advances in technology and. The post 16 OpenCV Functions to Start your Computer Vision journey (with Python code) appeared first on Analytics Vidhya.
Dataconomy
MARCH 15, 2019
Moving IT to the cloud is one of the main objectives for many companies. But once achieved, they find managing data in a hybrid environment a challenge. This is when metadata management becomes more important than ever. Cloud vs. on-premises? It’s one of those topics that you hear broached at. The post How Agile Cloud Solutions Can Upgrade On-Prem Performance: The BI Business Case?
Victor Zhou
MARCH 25, 2019
Why? I started this blog at the beginning of last month (February 2019) because I wanted to become a better writer. I felt like I had some interesting/useful things to say. I’d sold both of my main side projects and wanted a new challenge. I didn’t like the old victorzhou.com and had been itching to rewrite it for a while. I wanted to learn more about and get firsthand experience with SEO.
Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage
Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.
DataRobot
MARCH 5, 2019
In a previous blog post , we introduced you to Zach Deane-Mayer, a data scientist who runs our core modeling team. One of the most important tools in his team’s arsenal is a data science performance evaluation system created and maintained by our QA team. This system is at the core of our comprehensive testing philosophy that we believe is crucial to delivering a platform that our customers can trust, no matter what DataRobot features they’re using or how they’ve chosen to deploy them.
Data Science 101
MARCH 13, 2019
1. Be Honest. Try not to exaggerate your skills. If the job sounds more engineering focused than you are wanting, be honest and say that. Data Science is getting very broad and you don’t want to get in a position that is a bad fit. You often sound worse when you try to explain something you do not understand. Just be honest and say, “I have not needed to use that yet, can you explain to me when you have done that?
Smart Data Collective
MARCH 28, 2019
The life insurance industry will soon undergo a dramatic transformation in response to advances in big data. A growing number of digital startups are starting to emphasize the impact of big data in this antiquated business. A number of insurance executives have been reluctant to embrace the changes of big data. One study found that 74% of respondents felt that the insurance industry had done an inadequate job addressing the need for big data.
Analytics Vidhya
MARCH 17, 2019
Introduction Natural Language Processing (NLP) applications have become ubiquitous these days. I seem to stumble across websites and applications regularly that are leveraging NLP. The post 8 Excellent Pretrained Models to get you Started with Natural Language Processing (NLP) appeared first on Analytics Vidhya.
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Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.
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