AI News, Learn and practice Machine Learning with BigML

Learn and practice Machine Learning with BigML

The world and the global workforce cannot afford to stay behind the curve on this key technology enabler, so we urgently need to produce a much larger group of ML-literate professionals such as developers, analysts, managers, and subject matter experts.

To meaningfully contribute on this matter, the BigML Team holds Machine Learning crash courses throughout the year, ideal for advanced undergraduates as well as graduate students and industry practitioners seeking a quick, practical, and hands-on introduction to Machine Learning.

19 Data Science and Machine Learning Tools for people who Don’t Know Programming

This article was originally published on 5 May, 2016 and updated with the latest tools on May 16, 2018.

Among other things, it is acknowledged that a person who understands programming logic, loops and functions has a higher chance of becoming a successful data scientist.

There are tools that typically obviate the programming aspect and provide user-friendly GUI (Graphical User Interface) so that anyone with minimal knowledge of algorithms can simply use them to build high quality machine learning models.

The tool is open-source for old version (below v6) but the latest versions come in a 14-day trial period and licensed after that.

RM covers the entire life-cycle of prediction modeling, starting from data preparation to model building and finally validation and deployment.

You just have to connect them in the right manner and a large variety of algorithms can be run without a single line of code.

There current product offerings include the following: RM is currently being used in various industries including automotive, banking, insurance, life Sciences, manufacturing, oil and gas, retail, telecommunication and utilities.

BigML provides a good GUI which takes the user through 6 steps as following: These processes will obviously iterate in different orders. The BigML platform provides nice visualizations of results and has algorithms for solving classification, regression, clustering, anomaly detection and association discovery problems.

Cloud AutoML is part of Google’s Machine Learning suite offerings that enables people with limited ML expertise to build high quality models. The first product, as part of the Cloud AutoML portfolio, is Cloud AutoML Vision.

This service makes it simpler to train image recognition models. It has a drag-and-drop interface that let’s the user upload images, train the model, and then deploy those models directly on Google Cloud.

It also provides visual guidance making it easy to bring together data, find and fix dirty or missing data, and share and re-use data projects across teams.

Also, for each column it automatically recommends some transformations which can be selected using a single click. Various transformations can be performed on the data using some pre-defined functions which can be called easily in the interface.

Trifacta platform uses the following steps of data preparation: Trifacta is primarily used in the financial, life sciences and telecommunication industries.

The core idea behind this is to provide an easy solution for applying machine learning to large scale problems.

All you have to do is using simple dropdowns select the files for train, test and mention the metric using which you want to track model performance.

Sit back and watch as the platform with an intuitive interface trains on your dataset to give excellent results at par with a good solution an experienced data scientist can come up with.

It also comes with built-in integration with the Amazon Web Services (AWS) platform. Amazon Lex is a fully managed service so as your user engagement increases, you don’t need to worry about provisioning hardware and managing infrastructure to improve your bot experience.

You can interactively discover, clean and transform your data, use familiar open source tools with Jupyter notebooks and RStudio, access the most popular libraries, train deep neural networks, among a a vast array of other things.

It can take in various kinds of data and uses natural language processing at it’s core to generate a detailed report.

But these are excellent tools to assist organizations that are looking to start out with machine learning or are looking for alternate options to add to their existing catalogue.

List of Public Data Sources Fit for MachineLearning

Below is a wealth of links pointing out to free and open datasets that can be used to build predictive models.

We hope that our readers will make the best use of these by gaining insights into the way The World and our governments work for the sake of the greater good.

If you have an academic or research project, please keep in mind that BigML offers special discounts and free access for those.

 In fact, you will automatically get a FREE PRO subscription as long as you sign up with your “.Edu”

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Portal de Obligaciones de Transparencia Junta

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de la Información del Sector Público |

Reutilización de la Información de los Servicios Públicos Portal

de Datos Abiertos de JCCM Ayuntamiento

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de la Información del Sector Público en Gijón Open

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de Empresas, Marcas registradas, Normas legales y Teléfonos en Perú

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het open dataportaal van de Nederlandse overheid PortalU

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New Zealand government data online » 국가공유자원포털 中国政府公开信息整合服务平台 Open

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Programming Challenges: What are some good “toy problems”

in data science?

Where can I find large datasets open to the public?

Analysis: What’s your favorite free data source?

are some publicly available market data feeds?

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files for real-time Virginia transportation data. NYPD

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Getting the NDA out of the Way with MachineLearning

Today’s edition of our blog post series, written by the speakers at the upcoming 2ML event, covers how the Dutch company JuriBlox B.V.

NDAs are routinely used to cover confidential exchange of any business information, from prototype designs to customer lists or proposals for new business ventures.

Most NDAs are not reviewed as carefully as attorneys would recommend: it takes a significant amount of time and legal expertise to get an NDA just right and negotiated down to the last issue.

Until the legal world comes up with a standard NDA for the whole world, the best course of action is to review each NDA carefully prior to signing.

And for lawyers, the problem with reviewing an NDA is that it is mostly scanning for deviations from boilerplate text, which is extremely boring even for people whose job it is to review boring prose.

In the first step, a Machine Learning model was developed to identify whether individual sentences in a document belong to one of some twenty-plus legal categories (e.g.

And with those flavors, it becomes possible to judge the NDA: if you are providing information, it’s bad to have a relaxed security clause as that creates a risk the information ends up in the wrong hands without the clause having been violated.

The ensemble and neural network models of BigML proved very flexible and effective, and the easy-to-use interface made it a short point-and-click exercise to turn a training dataset into a complete model, ready to go at

But the effort was worth it: NDA Lynn now performs sentence classification with 94% accuracy, and its flavor models perform on average well over 90% too.

Moreover, you get NDA document management, you can have the reviews sent to your company lawyer for a manual check and there’s even an API to connect Lynn to your e-mail or document management system.

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Modern software architecture emphasizes modularity and composability. While the industry at large is rapidly moving toward approaches such as microservices, ...

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