Machine learning

RajeevGM sur Twitter : "#APIconUK by @ProgrammableWeb - @LouisDorard talking about #MachineLearning & #predictiveAPIs... PAPIs.io — The 1st Predictive APIs and Apps Conference. Slavi Marinov sur Twitter : "Awesome list of #machinelearning as a service providers by @louisdorard at #APIconUK... 6 Questions You Should Ask About Prediction APIs. In an upcoming talk at APIcon UK, I'll introduce you to Machine Learning through the use of prediction APIs.

As machine learning and predictive analytics services become more widely embraced in the business world, predictive APIs are starting to open up. But are predictive APIs right for your organization? And, if so, which ones? In a previous post on ProgrammableWeb, we saw how Machine Learning and Predictive Analytics services were growing, and we saw a sample of APIs in that space. More and more of these Prediction APIs are opening up. When evaluating this class of API, it is useful to have a common set of questions—the answers to which will help determine whether prediction APIs are a good fit for your needs and to steer you toward the best product for your organization. 1.

The answer to this question will determine how fast you can get up and running with an API. 2. Does the API provider display pricing on its website? 3. 4. 5. 6. Building a business around Machine Learning APIs. I got a variety of reactions on Twitter following my GigaOM piece on how Data Scientists work at automating themselves.

One of them I want to discuss today is about building businesses on top / around Prediction APIs such as Google's or BigML's (a.k.a. machine learning APIs). Anthony Nyström, who is Principal for Artificial Intelligence at Mashable, raises a very interesting point: is this even possible, and if you do build your business around these APIs, how are you going to differentiate yourself from others who could use the same APIs?

Here's one such business The first thing that comes to my mind is Pondera Solutions, which is a company that was founded in November 2011 and that provides Fraud Detection as a Service. Their product is built on top of Google Prediction API. Make your apps and your business smarter. Discover opportunities "While interest in all things relating to Big Data is high, most businesses are still in the phase of trying to figure out what to actually do with it.” — Michael Vizard, IT Business Edge Be a step ahead of the competition and figure out how to exploit the value of data in your business or in your app.

A whole chapter of the book is dedicated to concrete examples so you’ll understand why learning from data is so important and what are the opportunities. The book also teaches you what makes ML work and what are the limitations, so you’ll be able to develop your own original ideas of ML applications. How to predict abstention rates with open data. How to read the SunBurst visualization: Each arc corresponds to a set of townships that satisfy certain conditions — hover your mouse over a few of these arcs to see examples.The color of the arc reflects the average predicted abstention rate for the corresponding set of townships.Arcs are split into sub-arcs, representing subsets, as you move away from the center.The number of degrees spanned by an arc is proportional to the number of townships found in the data that belong to the corresponding set.

Note that this model is just one way to interpret the data and to present the correlations that were found. Even though we are dealing with a rule-based model, bear in mind that the "features" used in the rules to characterize townships are not necessarily causes of abstention. Correlation does not imply causation — if you take the example of sunglasses and ice-cream, their sales are correlated but one does not cause the other.

Everyone can do Data Science, Part 3 — BigML. The evaluation of the model is shown as a benchmark between the model and two baselines: mean-value predictions and random predictions.

When in green we know that BigML outperformed the baselines. You can compare the results to see by how much. The R-squared method shows how much better the model performs compared to the mean. We are told that the average error is $58,275.83 (Mean Absolute Error). It would be interesting in this case to also have the average error relative to the true value we tried to predict (the Mean Absolute Percentage Error). One way to decrease the error is by adding even more attributes like the year of construction/renovation, presence of a swimming pool, proximity to schools, public transports, etc. Another way to increase the accuracy of the model would be by increasing the volume of data we used. How to predict abstention rates with open data. Predicting Startup Success. My name is Rahul Desai and I’m the CEO and co-founder of Trendify, a meta-startup that uses machine learning and big data to more reliably determine whether any given startup will succeed or not.

I’d like to re-count the Trendify story, and elaborate on where BigML fits in. Introducción a Machine Learning - GPMESS. The Missing V in Big Data for Healthcare. My name is Dean Hudson and I’m the President of EngageHi², a healthcare IT solutions provider and service delivery partner to BigML.

The phrase “Transforming Healthcare” looms as large as “Big Data” or “Business Intelligence” in the healthcare industry. For years the industry has been throwing around jargon and buzzwords to drive awareness, marketing and sales. 3 exemplaires de Bootstrapping Machine Learning à gagner ! Training, interview and books to win with HumanCoders. How to Improve Your Subscription Based Business by Predicting Churn. Churn prediction is one of the most popular Big Data use cases in business.

It consists of detecting customers who are likely to cancel a subscription to a service. Although originally a telco giant thing, this concerns businesses of all sizes, including startups. Louisdorard : Automate as much as possible ... Language Principles and Concepts. 3 exemplaires de Bootstrapping Machine Learning à gagner ! Predict—Wolfram Language Documentation. Get Started in Machine Learning with Python... Registration, London. Invalid quantity.

Please enter a quantity of 1 or more. The quantity you chose exceeds the quantity available. Please enter your name. Please enter an email address. Please enter a valid email address. Please enter your message or comments. Please enter the code as shown on the image. Please select the date you would like to attend. Please enter a valid email address in the To: field. Please enter a subject for your message. USI 2014 – Rencontre avec Olivier Grisel – Predictive Analytics. [Interview] Louis Dorard, formateur Machine Learning - Human Coders Blog. Louis Dorard dispense chez nous une formation Machine Learning.

Big Data made easy. Best Programming Language for Machine Learning. A question I get asked a lot on my email list is: what is the best programming language for machine learning? I’ve replied to this question many times now it’s about time to explore this further in a blog post. Ultimately, the programming language you use for machine learning should consider your own requirements and predilections. No one can meaningfully address those concerns for you. Data Night Billets, Bordeaux - Eventbrite. Quantité non valide. Veuillez saisir une quantité de 1 ou plus. La quantité choisie excède la quantité disponible. Veuillez saisir votre nom. Veuillez saisir une adresse e-mail. Veuillez saisir une adresse e-mail valide. Data Night Billets, Bordeaux - Eventbrite. 'Jao' pretende abrir el 'Machine Learning' a las pymes.

Su pulso late por bienios. Desde que terminase su tesis de Física a finales de los 90, José Antonio Ortega (prefieren que le llamen 'Jao') no ha durado más de tres años en el mismo puesto de trabajo. Indra, Google, Oblong o iSOCO son algunas de las empresas por las que ha pasado. Hasta ahora, que se ha animado a crear su nueva empresa en un ámbito que pretende «democratizar», la inteligencia artificial. Al terminar su tesis, 'Jao' se dio cuenta de que el mundo de la Física Teórica era muy «endogámico».

Aquello no encajaba con él. Pasaron dos años y medio. «En Google te dan confianza como ingeniero, al contrario que en España» Automating the Data Scientist. Talking about automating one's job is a difficult exercise. People who need to have the job done but don't know how (or don't have enough resources) are bound to like the message. People who work on this automation can only approve. But many of those who do that job for a living will disagree. As I said in a previous post, my GigaOM piece on Data Scientists putting themselves out of business received mixed reactions. This is a complex topic, so let me add some more details to explain the perspective and context.

Bootstrapping Machine Learning: Book Review. Louis Dorard has released his book titled Bootstrapping Machine Learning. It’s a book that provides a gentle introduction to the field of machine learning targeted at developers and start-ups with a focus on prediction APIs. I just finished reading this book and I want to share some my thoughts. If you are interested, I have already reviewed the sample Louis provides on his webpage that covers the first two chapters. Bootstrapping Machine Learning Overview The book is broken down into eight chapters, as follows. Building a business around Machine Learning APIs. Building a business around Machine Learning APIs.

Cluster 5372457bd99497128e00e25e. Building a business around Machine Learning APIs. When Machine Learning fails. We previously assumed that there was a natural separation between classes. We started with a linear example and admitted we could deal with boundaries of any shape, but we still assumed that there was a boundary, which means that one class is in one region of the space and the other class is in another region. Why should things be this way, actually?

When using Machine Learning, we implicitly assume that there exists a relationship between inputs and outputs. If this is indeed the case, then similar inputs should be associated to similar outputs. When visualizing the objects that we aim to classify in a space (for instance a 2D space in the previous illustrations), similarity is equivalent to proximity in that space. The machine relies on that same assumption that similar inputs should be associated to the same outputs, at least most of the time. New Links. Make your apps and your business smarter. Three Workshops on Predictive Applications. This is a guest post by Louis Dorard, author of Bootstrapping Machine Learning. When it comes to learn about Machine Learning APIs from experts, webinars are a great place to start. However, there’s nothing like in-person workshops. Today, BigML is announcing three exceptional workshops on Predictive Applications taking place in Spain in May. I will have the honour to speak at these workshops alongside Francisco and jao, who are the CEO and CTO of BigML.

Here are the three dates: Bellaterra (Barcelona): May 7 at 12pm, IIIA (Artificial Intelligence Research Institute) at the Campus of the Universitat Autonòma de Barcelona.Madrid: May 8 at 5pm, Wayra/Telefonica.Valencia: May 13 at 4pm, Universitat Politècnica de València. If you’re anywhere near these cities at that time, you should definitely come! Make your apps and your business smarter. Building predictive apps and predictive businesses is a very hot topic. But don’t just take my word for it. Here is what the experts are saying: For apps "Predictive apps are the next big thing in app development” — Mike Gualtieri, Principal Analyst at Forrester"There’s no doubt that developers are going to be increasingly asked to embed [predictive] analytics capabilities within their applications.” — Michael Vizard, IT Business Edge For businesses.

Louisdorard : Bootstrapping #MachineLearning... Louisdorard : Bootstrapping #MachineLearning... Create smarter apps and businesses. PreciBake: Bakery ovens that use algorithms. Baking with algorithmic software can help minimize mistakes as busy bakers work to broaden portfolios, according to developer PreciBake. The creators of the PreciBake artificial intelligence baking system said it worked by ‘getting to know’ products as it baked, which allowed bakers to see the oven’s content from wherever they were in the world.

The algorithm system learned what product it was baking by collecting data and adjusted to account for any discrepancies from one oven to another without requiring human intervention, Thomas Bone, managing director for PreciBake, told BakeryandSnacks.com. The firm, which launched its systems alongside WP Bakery machines, has also developed a way in which bakery managers can track oven production through data and images of inside the machine via their smart phone or tablet. Data Privacy, Machine Learning, and the Destruction of Mysterious Humanity - John Foreman, Data Scientist. Big Data and Machine Learning: The Dehumanization of Everything. There is a lot of talk recently about the consequences of Big Data, machine learning, and the lack of data privacy.

Facebook, for example, has a lot of data regarding a lot of people, and Mark Zuckerberg attended the annual NIPS conference and panels on deep learning, so what does it all mean? Louis @ Dojo Crea. 2014 will be the year you'll learn Machine Learning. A plain English guide to how natural language processing will transform computing. Buzz phrases such as “artificial intelligence,” “machine learning” and “natural language processing” are becoming increasingly commonplace within the tech industry. There is a lot of ambiguity around these phrases, so I’ll explain the substance behind the technologies and why I believe they’re transforming the way we live, work and play.

HipsterDataSci : drew conway breaks it down... 2014 will be the year you'll learn Machine Learning. Url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CDkQFjAA&url=http%3A%2F%2Fwww.mckinsey.com%2F~%2Fmedia%2FMcKinsey%2Fdotcom%2FInsights%2520and%2520pubs%2FMGI%2FResearch%2FTechnology%2520and%2520Innovation%2FBig%2520Data%2FMGI_big_data_exec_summary.as. Bootstrapping Machine Learning: overview and case study. Bootstrapping Machine Learning: overview and case study.

Reimplement Priority Inbox. Louis @ Dojo Crea. Create smarter apps and businesses. Machine Learning Kit - Get  Automate Analysis Through Patterns in Data You alone are no match for the vast amounts of data in existence. Create smarter apps. Create smarter apps. Registration. Why Machine Learning fails. Welcome to BigML. Why Machine Learning fails. Daily learnings. Create smarter apps. Create smarter apps. Machine Learning on Big Data for Predictive Analytics. [humantalks][Paris][juin 2013] Baptême en Machine Learning. Create smarter apps. Baptême en Machine Learning.

Writing an ebook on Machine Learning for bootstrappers - feedback welcome! Writing an ebook on Machine Learning for bootstrappers - feedback welcome! Inside the Secret World of Quants and Data Crunchers Who Helped Obama Win. ICML 2011 Conference Talks. Bayesian optimization, experimental design and bandits NIPS 2011 Workshop. Machine Learning for Website Optimisation. Carnegie Mellon University. Webscope from Yahoo! Labs. Explorations in Computer Go, Web Search, and Online Advertising.

Presentation of Adobe Omniture, the Challenge, and announcement of the final results. Exploration & Exploitation Challenge 2011.