AI News, Self-Service Machine Learning
- On Thursday, March 8, 2018
- By Read More
Self-Service Machine Learning
specific challenges of integrating ML capabilities in a self-service BI platform include the supply of ML algorithms that do not rely on specific data and can be easily applied and fit to customer specific use cases (and data). In
this Data Science Central Webinar we will demonstrate how to implement a classification model in a generic manner that can be used by many customers, without relying on specific data, and by automating the validation process ensuring minimum overfit introduced.
Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.
The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.
Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.
Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.
Data Scientist vs Data Engineer, What’s the difference?
They have a strong understanding of how to leverage existing tools and methods to solve a problem, and help people from across the company understand specific queries with ad-hoc reports and charts.
However, they are not expected to deal with analyzing big data, nor are they typically expected to have the mathematical or research background to develop new algorithms for specific problems.
Indeed, data science is not necessarily a new field per se, but it can be considered as an advanced level of data analysis that is driven and automated by machine learning and computer science.
In another word, in comparison with ‘data analysts’, in addition to data analytical skills, Data Scientists are expected to have strong programming skills, an ability to design new algorithms, handle big data, with some expertise in the domain knowledge.
Moreover, Data Scientists are also expected to interpret and eloquently deliver the results of their findings, by visualization techniques, building data science apps, or narrating interesting stories about the solutions to their data (business) problems.
The problem-solving skills of a data scientist requires an understanding of traditional and new data analysis methods to build statistical models or discover patterns in data.
This is tricky because, in order to analyze the data, a strong Data Scientists should have a very broad knowledge of different techniques in machine learning, data mining, statistics and big data infrastructures.
They should have experience working with different datasets of different sizes and shapes, and be able to run his algorithms on large size data effectively and efficiently, which typically means staying up-to-date with all the latest cutting-edge technologies.
- On Monday, December 9, 2019
Webinar: Self Service Machine Learning
In today's demanding market, Machine Learning capabilities have become a basic requirement you need to support. Self-service BI solutions are no different. Users need machine learning capabilitie...
11. Introduction to Machine Learning
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: Instructor: Eric Grimson In this lecture, Prof. Grimson..
Introduction to Google Cloud Machine Learning (Google Cloud Next '17)
Google Cloud is at the forefront of developing cutting-edge machine learning technology. Computer vision, predictive modeling, natural language understanding, and speech recognition are among...
The Microsoft AI platform - GS07
Join Joseph Sirosh, Corporate Vice President of the Cloud AI Platform, as he dives deep into the latest additions to the Microsoft AI platform and capabilities. Innovations in AI let any developer...
Jennifer Listgarten: CRISPR Bioinformatics - Machine learning predictive models for guide design
Jennifer Listgarten (Microsoft) explains how machine learning can be utilized for guide RNA design. [2017 CRISPR Workshop]
BI in the age of artificial intelligence
Equip your organization today for the future of data analytics. See how users of Microsoft Power BI, for example, can experience their data in a natural way by simply asking questions and getting...
Lecture 01 - The Learning Problem
The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156...
DataScience@NIH: Current State, Future Directions
Note: Video may display green background for about 1 minute and disappears for the remainder of the video. We apologize for any inconvenience. Dr. Patricia Flatley Brennan, National Library...
How great leaders inspire action | Simon Sinek
Simon Sinek presents a simple but powerful model for how leaders inspire action, starting with a golden circle and the question "Why?" His examples include Apple, Martin..
Pedro Domingos: "The Master Algorithm" | Talks at Google
Machine learning is the automation of discovery, and it is responsible for making our smartphones work, helping Netflix suggest movies for us to watch, and getting presidents elected. But...