Feature selection is one of the most important processes for pattern recognition, machine learning and data mining problems. A successful feature selection method facilitates improvement of learning model performance and interpretability as well as reduces computational cost of the classifier by dimensionality reduction of the data. Feature selection is computationally expensive and becomes intractable even for few 100 features. This is a relevant problem because text, image and next generation sequence data all are inherently high dimensional. In this talk, I will discuss about few algorithms we have developed in last 5/6 years. Firstly, we will set the context of feature selection ,with some open issues , followed by definition and taxonomy. Which will take about 20 odd minutes. Then in next 20 minutes we will discuss couple of research efforts where we have improved feature selection for textual data and proposed a graph based mechanism to view the feature interaction. After the talk, participants will be appreciate the need of feature selection, the basic principles of feature selection algorithm and finally how they can start developing their own models

With the need to cater to a global audience, there is a growing demand for applications to support speech identification/translation/transliteration from one language to another. This session aims at introducing the audience to the topic, learn the inner working of the AI/ML models and eventually how to quickly use some of the readily available APIs to identify, translate or even transliterate speech/text within their application.

The fusion of blockchain and machine learning is an ultimate game changer. Machine learning relies on high volume of data to build models for accurate prediction. A lot of the challenges incurred in getting this data lies in collecting, organizing and auditing the data for accuracy. This is an area that can significantly be improved by using blockchain technology. By using smart contracts, data can be directly and reliably transferred straight from its place of origin. Smart contracts could, however, improve the whole process significantly by using digital signatures.

Blockchain is a good candidate to store sensitive information that should not be modified in any way. Machine learning works on the principle of “Garbage In, Garbage Out,” which means that if the data that was used to build a prediction model was corrupted in any way, the resultant model would not be of much use either. Combining both these technologies creates an industry disruptor which leverages the power of both Blockchain and Machine learning.

Automated Machine Learning (AutoML) provides methods and processes to automate the end-to-end machine learning workflow towards solving real-world business problems.

In traditional machine learning world, data scientist used to spend a considerable amount of time in Data wrangling, model selection and tuning, now with the advances of AutoML it provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.

Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand.

Language Wars have always been there for ages and it's got a new candidate with Data science booming - R vs Python. While the fans are fighting R vs Python, the creators (Hadley Wickham (Chief DS @ RStudio) and Wes McKinney (Creator of Pandas Project)) are working together as Ursa Labs team to create open source data science tools. A similar effort by RStudio has given birth to Reticulate (R Interface to Python) that helps programmers combine R and Python in the same code, session and project and create a new kind of super hero.

R programming is one of the most popular programming languages used in Data Science. Known for its simplicity and easy to take off working environment, R has been the language of choice of many non-programmers and its Rich ecosystem enables it to perform variety of Data Science related tasks. The objective of this workshop is to help you get started with R for you to move forward with your Data Science journey. As we are moving into the world of language-agnostic developers, Even if you know a language already, knowing another extra programming language like R would add an extra feather to your cap.

This talk contains two sections predominantly - 1st explaining what’s all (non-obvious) that are possible with R and 2nd, How well-known organizations are using R in their company. R is one of the most popular programming languages preferred in Data Science / Analytics.