AI News, Self-Service Machine Learning

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.

Machine Learning

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.

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