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Learning from the best Kaggle Blog 20140801
Learning from the best David Kofoed Wind|08.01.2014 Guest contributor David Kofoed Wind is a PhD student in Cognitive Systems at The Technical University of Denmark (DTU): As a part of my master's thesis on competitive machine learning, I talked to a series of Kaggle Masters to try to understand how they were consistently performing well in competitions. What I learned was a mixture of rather well-known tactics, and less obvious tricks-of-the-trade. In this blog post, I have picked some of their answers to my questions in an attempt to outline some of the strategies which are useful for performing well on Kaggle. As the name of this blog suggests, there is no free hunch, and reading this blog post will not make you a Kaggle Master overnight. Yet following the steps described below will most likely help with getting respectable results on the leaderboards. I have partitioned the answers I got into a series of broad topics, together with a list of miscellaneous advice in the end. Feature engineering is often the most important part With the extensive amount of free tools and libraries available for data analysis, everybody has the possibility of trying out advanced statistical models in a competition. As a consequence of this, what gives you most “bang for the buck” is rarely the statistical method you apply, but rather the features you apply it to. By feature engineering, I mean using domain specific knowledge or automatic methods for generating, extracting, removing or altering features in the data set.
#ds  #advice  #dataengineering  #featureengineering  +Kaggle  #A+  >.ac 
yesterday by phil_hendrix
Closing the Results Gap in Advanced Analytics: Lessons from the Front Lines Bain 20170801
Closing the Results Gap in Advanced Analytics: Lessons from the Front Lines Six principles can help companies design advanced analytics approaches that lead to action. By Chris Brahm and Lori Sherer August 01, 2017 6 min read
#analytics  #deployment  #bestpractices  #exemplars  #advice  +Bain  #A+ 
7 days ago by phil_hendrix
Making data analytics work for you – instead of the other way around QuantumBlack 20160930
Making data analytics work for you – instead of the other way around Does your data have a purpose? If not, you’re spinning your wheels. Here’s how to discover one and then translate it into action. Helen Mayhew, Tamim Saleh, and Simon Williams September 30th 2016 Download PDF The data-analytics revolution now under way has the potential to transform how companies organize, operate, manage talent, and create value. That’s starting to happen in a few companies – typically ones that are reaping major rewards from their data – but it’s far from the norm. There’s a simple reason: CEOs and other top executives, the only people who can drive the broader business changes needed to fully exploit advanced analytics, tend to avoid getting dragged into the esoteric “weeds.” On one level, this is understandable. The complexity of the methodologies, the increasing importance of machine learning, and the sheer scale of the data sets make it tempting for senior leaders to “leave it to the experts.” But that’s also a mistake. Advanced data analytics is a quintessential business matter. That means the CEO and other top executives must be able to clearly articulate its purpose and then translate it into action –not just in an analytics department, but throughout the organization where the insights will be used. This article describes eight critical elements contributing to clarity of purpose and an ability to act. We’re convinced that leaders with strong intuition about both don’t just become better equipped to “kick the tires” on their analytics efforts. They can also more capably address many of the critical and com- plementary top-management challenges facing them: the need to ground even the highest analytical aspirations in traditional business principles, the importance of deploying a range of tools and employing the right personnel, and the necessity of applying hard metrics and asking hard questions. (For more on these, see “Straight talk about big data,” on page 42.1) All that, in turn, boosts the odds of improving corporate performance through analytics.
#analytics  #deployment  #advice  #fluency  @McKinsey  @QuantumBlack 
8 days ago by phil_hendrix
5 Steps for Planning a Healthcare Artificial Intelligence Project HealthIT Analytics 201807
5 Steps for Planning a Healthcare Artificial Intelligence Project How can organizations planning a healthcare artificial intelligence project set the stage for a successful pilot or program?
#hc  #ai  #deployment  #usecases  #advice  #A+  +Optum 
13 days ago by phil_hendrix
Sentiment analysis in more than 1,000 web tools MonkeyLearn 20180830
Sentiment analysis in more than 1,000 web tools by Hernán Correa August 30, 2018 · 7 min read Getting your work done involves using different tools and apps. In fact, businesses today use 16 apps on average, up 33% from last year. Say that you work in customer support, most probably your tech stack includes tools for email, support tickets, team communication, NPS surveys, project management, and more. When you have such a diverse set of apps, it can be a struggle to sort through the data; there’s just too much information to process manually. This is why more and more companies are using sentiment analysis in combination with Zapier to automate workflows and get insights from their data. This combo makes it super easy and straightforward to analyze data at scale, no matter what apps you use, with zero lines of code. With this in mind, we’ve created the following step-by-step guide to show how you can use Zapier and MonkeyLearn to do sentiment analysis in more than 1,000 web tools. Let’s get started!
#analytics  #text  #NLP  #sentiment  #applications  #advice  %Zapier  +MonkeyLearn 
19 days ago by phil_hendrix
What Executives Should be Asking about AI Use-Cases in Business Podcast 20180820
What Executives Should be Asking about AI Use-Cases in Business Last updated on August 20, 2018 by Pamela Bump When contemplating a new venture into AI or machine learning, companies need to take on a number of important considerations that relate to talent, existing data, and limitations. One way executives can judge how successful or appropriate and AI project would be for their company is to examine use cases of businesses that have previously done something similar. With AI and machine learning news increasing in tech media, a business leader may find it challenging to cut through the hype and identify valid, useful case studies. We talked to Ben Lorica, PhD,  Chief Data Scientist at O’Reilly Media, to get his insights on what key details executives should be looking for within a case study.
#ai  #enterprise  #deployment  #applications  #advice  #catalysts 
28 days ago by phil_hendrix
The AI-first startup playbook VentureBeat 20180818
The AI-first startup playbook IVY NGUYEN, NEWGEN CAPITAL@ABCDEFGHIVYMARK GORENBERG, ZETTA VENTURE PARTNERS AUGUST 18, 2018 4:41 PM Image Credit: metamorworks / Shutterstock MOST READ Zippin opens cashierless store in San Francisco WaveSense’s ground-penetrating radars could make self-driving cars safer MIT researchers are developing AI for tracking sensors inside the body with wireless signals The homeland-as-a-service model: How blockchain will disrupt the world order How to create a virtuous cycle of data with your customers UPCOMING EVENTS Transform: The AI event of the year for growth marketers. Aug. 21 - 22 VB Summit: The best in AI. An invite-only executive event. Oct. 22 - 23 Iterative Lean Startup principles are so well understood today that an minimum viable product (MVP) is a prerequisite for institutional venture funding, but few startups and investors have extended these principles to their data and AI strategy. They assume that validating their assumptions about data and AI can be done at a future time with people and skills they will recruit later. But the best AI startups we’ve seen figured out as early as possible whether they were collecting the right data, whether there was a market for the AI models they planned to build, and whether the data was being collected appropriately. So we believe firmly that you must try to validate your data and machine learning strategy before your model reaches the minimal algorithmic performance (MAP) required by early customers. Without that validation — the data equivalent of iterative software beta testing — you may find that the model you spend so much time and money building is less valuable than you hoped.
#ai  #startups  #advice  #vc  #valueproposition  #productmarketfit 
29 days ago by phil_hendrix
How-To Guide - Content Marketing Institute
HOW-TO GUIDES
GETTING STARTED Content Marketing 101 begins
PLAN Build fundamentals of your planAUDIENCE Understand your core audiences
STORY Tell your brand story
CHANNELS Determine your distribution channels
PROCESS Manage your team and tools
CONVERSATION Create your content and listen
MEASUREMENT Prove the effectiveness of your program
#socialmedia  #contentmarketing  #explainers  #guides  #advice  #resources  #A+ 
5 weeks ago by phil_hendrix
The Problem With AI Pilots Tom Davenport Randy Bean 20180726
The Problem With AI Pilots Big Idea: Artificial Intelligence and Business StrategyBlog July 26, 2018 Reading Time: 4 min  Thomas H. Davenport and Randy Bean Leadership, Digital, Data & Analytics, Digital Business SUBSCRIBE SHARE Share on Twitter Share on Facebook Share on LinkedIn Share through Email AI technology is not just an experiment. advertisement Over the past year or so we’ve been engaged in an effort to tell the story of how large organizations are deploying artificial intelligence in their businesses. We were encouraged by the response to the 2018 NewVantage Partners executive survey, in which 93% of respondents said their organizations were investing in AI initiatives. Plenty of companies to write about, we thought. These were very large organizations spending goodly sums on AI and with a history of early adoption of other technologies. But when we approached many of these companies to discuss writing some case studies about their work, most of them demurred. Most said the reason wasn’t that they wanted to keep their AI activities secret, but that they weren’t actually very far along and hence their projects were not worth discussing yet. They were doing lots of pilots, proofs of concept, and prototypes, but they had few production deployments. When they did have AI systems in production, most were machine learning-based systems that had been in place for many years. This is particularly true in financial services, where large-scale “scoring” has been used to evaluate customers for credit and potential fraud for well over a decade. Some said to us that they didn’t really consider these projects to be examples of AI — consistent with the common view of AI that it describes technology that is never really here yet. Others say that they have robotic process automation (RPA) implementations in place, but most are relatively small, and there is also debate about whether RPA is really AI or not.
#ai  #pilots  #deployment  #enterprise  #advice  #bestpractices  +TomDavenport 
5 weeks ago by phil_hendrix
Building a great data platform in energy and materials McKinsey 201808
Building a great data platform By Adrian Booth, Rita Chung, Jeff Hart, and Stuart Sim Article Actions Share this article on LinkedIn Share this article on Twitter Share this article on Facebook Email this article Print this article Download this article Five insights into building a great data platform can help energy, chemical, utility, and basic-materials companies get it right. For any sizeable company, a state-of-the-art data and analytics platform is no longer an option but a necessity. Such a platform acts as a central repository for all data, distills them into a single source of truth, and supports the scaling up of sophisticated digital- and advanced-analytics programs that translate data into business value (exhibit). Companies without one risk leaving serious value on the table. For a utility, for instance, a data and analytics platform can cut costs by up to 15 percent in some operational and maintenance areas, while savings in oil and gas companies’ upstream activities can run even higher, at up to 20 percent.
#analytics  #data  #platform  #strategy  #impact  #value  #advice  +McKinsey 
6 weeks ago by phil_hendrix
Rachel Thomas (@math_rachel) | Twitter
Rachel Thomas @math_rachel co-founder ªªhttp://fast.ai ºº + professor @usfca_msds | past: Duke math PhD, quant, early Uber eng | now: making neural nets uncool San Francisco, CA fast.ai Joined May 2013
#datascience  #SME  #tl  #academic  #advice  #A+  >rr 
6 weeks ago by phil_hendrix
Artificial intelligence in business, a strategy and ambitions to strengthen Factonics 20180515
Artificial intelligence in business, a strategy and ambitions to strengthen A large majority of traditional companies have invested heavily, over the last five years to develop their technological capabilities, around Big Data and more recently around cases of use of artificial intelligence. The perfect illustration is the initiative of 'Axa Global to create, five years ago, an entity through its Data Innovation Lab, whose advanced developments are today ahead of schedule in relation to the business needs of the rest of the group. In parallel, the giants of the web, like NATU (Netflix, Amazon, Tesla, Uber) integrated ab initio artificial intelligence at the center of their business model and growth. Let's try to identify the strategic reasons for this dichotomy and the future consequences for competitiveness.
#ai  #deployment  #strategy  #advice  #solutions  +Factonics 
6 weeks ago by phil_hendrix
Morgan Stanley Delivering High-Quality Customized Advice at Scale Jeff McMillan 20180801
MORGAN STANLEY DELIVERING HIGH-QUALITY CUSTOMIZED ADVICE AT SCALE BY JEFF MCMILLAN, AUG 01, 2018 Download the PDF INTRODUCTION Driving growth at Morgan Stanley is about equipping thousands of financial advisors to efficiently deliver personal, customized advice to clients in dynamic markets. Providing personalized financial advice at scale requires more than just leveraging new tools and technologies; it requires creating an analytically enabled, empowered organization. With all the hype in the marketplace around artificial intelligence and big data, it’s important to remember that while algorithms can play an important role in the changing business landscape, they are only one part of a holistic network of capabilities that organizations must leverage to drive the outcomes they want. This means having a clear sense of what they want to achieve, a culture of strong business alignment and collaboration, high quality historical data that is predictive in nature and a leadership team that is open to experimentation. An analytically enabled organization like Morgan Stanley doesn’t rush to create algorithms. It uses analytics and algorithms in support of a clear business strategy and empowers the entire organization to use analytics to make better business decisions.
#analytic  #deployment  #casestudy  #bestpractices  #advice  +IIA  +MorganStanley 
6 weeks ago by phil_hendrix
AI in the Enterprise Susan Etlinger Altimeter 2018
AI in the Enterprise Real-World Strategies for Artificial Intelligence New Research from Altimeter By Susan Etlinger, Industry Analyst, Altimeter For enterprise companies considering investing in AI and implementing AI applications, the current landscape can seem overwhelming. Companies like Amazon, Facebook, Google, Apple, and Microsoft dominate the news, but how applicable are their strategies to companies with vastly different business models? This report examines the real use cases, challenges, and opportunities of AI for organizations. It includes interviews with executives from large, well-known companies and start-up entrepreneurs who are envisioning the many ways that machine intelligence can fuel innovation and growth. Finally, the report offers recommendations for companies thinking about where to focus, how to build their partnership ecosystem, and how to measure value in the short and long term as AI becomes a critical driver of digital transformation. In this new report by Susan Etlinger, you will find:  A framework for understanding how you can apply AI within your organization Interviews and in-depth case studies from organizations using AI to fuel innovation and create tangible business value Five specific guiding recommendations for enterprises planning AI implementations
#ai  #enterprise  #deployment  #advice  #cases  #applications  +Altimeter 
7 weeks ago by phil_hendrix
The five Cs - Five framing guidelines to help you think about building data products O'Reilly 20180724
The five Cs Five framing guidelines to help you think about building data products. By Mike LoukidesHilary MasonDJ Patil July 24, 2018 Trust (source: Terry Johnston on Flickr) Check out the session "An ethical foundation for the AI-driven future" at the Strata Data Conference in New York, September 11-13, 2018. Hurry—early price ends July 27. This post is part of a series on data ethics. What does it take to build a good data product or service? Not just a product or service that’s useful, or one that’s commercially viable, but one that uses data ethically and responsibly. We often talk about a product’s technology or its user experience, but we rarely talk about how to build a data product in a responsible way that puts the user in the center of the conversation. Those products are badly needed. News that people “don’t trust” the data products they use—or that use them—is common. While Facebook has received the most coverage, lack of trust isn’t limited to a single platform. Lack of trust extends to nearly every consumer internet company, to large traditional retailers, and to data collectors and brokers in industry and government. STRATA DATA CONFERENCE Strata Data Conference in New York, September 11-13, 2018 Early price ends July 27 Users lose trust because they feel abused by malicious ads; they feel abused by fake and misleading content, and they feel abused by “act first, and apologize profusely later” cultures at many of the major online companies. And users ought to feel abused by many abuses they don’t even know about. Why was their insurance claim denied? Why weren’t they approved for that loan? Were those decisions made by a system that was trained on biased data? The slogan goes, “Move fast and break things.” But what if what gets broken is society?
#data  #analytics  #advice  #guidelines  #privacy  #consent  #consumer  #protection 
7 weeks ago by phil_hendrix
The five Cs - Five framing guidelines to help you think about building data products O'Reilly 20180724
The five Cs Five framing guidelines to help you think about building data products. By Mike LoukidesHilary MasonDJ Patil July 24, 2018 Trust (source: Terry Johnston on Flickr) Check out the session "An ethical foundation for the AI-driven future" at the Strata Data Conference in New York, September 11-13, 2018. Hurry—early price ends July 27. This post is part of a series on data ethics. What does it take to build a good data product or service? Not just a product or service that’s useful, or one that’s commercially viable, but one that uses data ethically and responsibly. We often talk about a product’s technology or its user experience, but we rarely talk about how to build a data product in a responsible way that puts the user in the center of the conversation. Those products are badly needed. News that people “don’t trust” the data products they use—or that use them—is common. While Facebook has received the most coverage, lack of trust isn’t limited to a single platform. Lack of trust extends to nearly every consumer internet company, to large traditional retailers, and to data collectors and brokers in industry and government. STRATA DATA CONFERENCE Strata Data Conference in New York, September 11-13, 2018 Early price ends July 27 Users lose trust because they feel abused by malicious ads; they feel abused by fake and misleading content, and they feel abused by “act first, and apologize profusely later” cultures at many of the major online companies. And users ought to feel abused by many abuses they don’t even know about. Why was their insurance claim denied? Why weren’t they approved for that loan? Were those decisions made by a system that was trained on biased data? The slogan goes, “Move fast and break things.” But what if what gets broken is society?
#data  #analytics  #advice  #guidelines  #privacy  #consent  #consumer  #protection 
7 weeks ago by phil_hendrix
Machine learning: What developers and business analysts need to know InfoWorld 20180307
Machine learning: What developers and business analysts need to know There is more to a successful application of machine learning than data science.
Machine learning is undergoing a revolution because of new technologies and methods. Machine learning is a process of using a program to develop capabilities—like the ability to tell spam from desirable email—by analyzing data instead of programming the exact steps, freeing the user from needing to make every decision about how the algorithm functions. Machine learning is a powerful tool, not only because over a million people focus on tedious programming steps every day, but also because it sometimes finds better solutions than humans engaged in manual effort.
#ml  #applications  #deployment  #advice  #explainer 
7 weeks ago by phil_hendrix
Machine Learning - Coursera Andrew Ng 201807
Machine Learning by Stanford University Andrew Ng Welcome to Machine Learning! I'm excited to have you in the class and look forward to helping you become an expert in machine learning. After you finish watching the Week 1 lectures, there's also a set of Review Questions to help you check your understanding. You should be able to complete the review questions in a few minutes. You can attempt the review questions as many times as you like, and we will only use your highest score. This machine learning class was the class that had started Coursera. I'm excited to be teaching it again. By the time you finish this class, you'll know how to apply the most advanced machine learning algorithms to such problems as anti-spam, image recognition, clustering, building recommender systems, and many other problems. You'll also know how to select the right algorithm for the right job, as well as become expert at 'debugging' and figuring out how to improve a learning algorithm's performance. I hope you'll have a lot of fun learning about machine learning. Andrew
#ml  #advice  #concept  #methods  #course  +AndrewNg  #A+ 
8 weeks ago by phil_hendrix
Designing with Data 20180716
Designing with Data Interpreting and Analyzing Data as a Designer Photo by Pietro Jeng on Unsplash Statistics help us summarize and understand the hard data we collect, and instincts do the same for all the messy real-world experiences we observe. And that’s why the best products — the ones that people want to use, love to use — are built with a bit of both.  — Braden Kowitz For many tech companies, design and data are intertwined. Companies work amid a constant stream of data detecting the impact of every minute change, and rely on teams of analysts, data scientists or engineers to continuously monitoring hundreds of metrics and multiple iterations. While design instincts are still valuable, data and analytics can help you hone your product understanding and ensure your decisions satisfy stakeholders. Here’s some things to keep in mind as a UX designers working with data:
#data  #analysis  #interpretation  #design  #advice 
9 weeks ago by phil_hendrix

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