Machine Learning, Distributed and Parallel Computing, and High-performance Computing are the themes for this year’s edition of Fifth Elephant.
The deadline for submitting a proposal is 15th June 2015
We are looking for talks and workshops from academics and practitioners who are in the business of making sense of data, big and small.
This track is about general, novel, fundamental, and advanced techniques for making sense of data and driving decisions from data. This could encompass applications of the following ML paradigms:
- Statistical Visualizations
- Unsupervised Learning
- Supervised Learning
- Semi-Supervised Learning
- Active Learning
- Reinforcement Learning
- Monte-carlo techniques and probabilistic programming
- Deep Learning
Across various data modalities including multi-variate, text, speech, time series, images, video, transactions, etc.
This track is about tools and processes for collecting, indexing, and processing vast amounts of data. The theme includes:
- Distributed and Parallel Computing
- Real Time Analytics and Stream Processing
- MapReduce and Graph Computing frameworks
- Kafka, Spark, Hadoop, MPI
- Stories of parallelizing sequential programs
- Cost/Security/Disaster Management of Data
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source license. If your software is commercially licensed or available under a combination of commercial and restrictive open source licenses (such as the various forms of the GPL), please consider picking up a sponsorship. We recognize that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
If you are interested in conducting a hands-on session on any of the topics falling under the themes of the two tracks described above, please submit a proposal under the workshops section. We also need you to tell us about your past experience in teaching and/or conducting workshops.
Practical Approach to Python based Supervised Machine Learning: User Generated Text Classification Techniques
In e-Commerce, we handle large volume of user genearted content in the forms of Reviews, Ratings, Question/Answer, Chat etc. These user generated content has lot of values in terms of taking right organization-wide business decission. This large volume of user generated text also imposes problem of classificaiton and moderation because the data is mostly unstructured. Combination of various Machine Learning techniques can be a convenient tool to handle the problems in this space. In this session we want to explore how we can bring the Python NLTK and other Machine learning components together to solve this problem and provide the solution as service (Python Flask) to integrate the intelligence with other parts of e-Commerce platform.
We will be covering the following topics in the session:-
- Introduction to Supervised ML text classification. How we can model the text classification problem to a supervised ML problem. What are the traditional ways to combine various ML models.
- What are the challanges to handle the User generated content/text. Why we can not just shoe-horn the established ML models in solving moderation problem in user generated content.
- How we can integrate Python NLTK and other ML Components for text classification.
- What are the best practices of using Python based ML models: Training and testing models
- How we can combine multiple ML models and RegEx logic.
- Python Flask for exposing the ML logic as service.
Basic Computer science knowledge. Knowledge in Python will be helpful.
Kausik is Senior Manager(/Architect) in Snapdeal, managing the Machine Learning component for User Generated Content(Reviews, Ratings, Question/Answer, Chat). Kausik is MBA from IIM Bangalore and BE from Jadavpur University, Kolkata. Kausik has previously worked in Amazon(Product Aggregation Technologies) and Goldman Sachs(Hedge Fund Risk Management).