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.


Outline/Structure of the Talk

1. Machine and Deep Learning Introduction

2. Why Automated Machine learning

3. Auto ML libraries (H2O, Auto-Keras, T-POT) and algorithms in R and Python

4. Industry usage and integration with Cloud (GCP)

5. Auto ML demo using Banking industry case study in R

Learning Outcome

Participants will take back good fundamental concepts of Deep Learning and Automated Machine Learning using different libraries in R and Python.

Beginners to Machine learning will learn how to develop model in 15 mins and experts in ML will learn about how to leverage Auto ML to develop model for complex use cases.

Cloud integration using GCP will also demoed.

Target Audience

Machine Learning enthusiast, Beginners and Data scientist

Prerequisites for Attendees

1. Basics of Machine Learning and Deep Learning

2. Supervised and Unsupervised Learning

3. Cloud (GCP, AWS) knowledge - Good to have (not must to have)

schedule Submitted 3 years ago

  • Dr. Saptarsi Goswami

    Dr. Saptarsi Goswami - Mastering feature selection: basics for developing your own algorithm

    45 Mins

    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

  • Dr. Rahee Walambe

    Dr. Rahee Walambe / Vishal Gokhale - Processing Sequential Data using RNNs

    480 Mins

    Data that forms the basis of many of our daily activities like speech, text, videos has sequential/temporal dependencies. Traditional deep learning models, being inadequate to model this connectivity needed to be made recurrent before they brought technologies such as voice assistants (Alexa, Siri) or video based speech translation (Google Translate) to a practically usable form by reducing the Word Error Rate (WER) significantly. RNNs solve this problem by adding internal memory. The capacities of traditional neural networks are bolstered with this addition and the results outperform the conventional ML techniques wherever the temporal dynamics are more important.
    In this full-day immersive workshop, participants will develop an intuition for sequence models through hands-on learning along with the mathematical premise of RNNs.


    JAYA SUSAN MATHEW - Breaking the language barrier: how do we quickly add multilanguage support in our AI application?

    Sr. Data Scientist
    schedule 4 years ago
    Sold Out!
    20 Mins

    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.

  • Parul pandey

    Parul pandey - Jupyter Ascending : The journey from Jupyter Notebook to Jupyter Lab

    Parul pandey
    Parul pandey
    Data Science Communicator
    schedule 3 years ago
    Sold Out!
    45 Mins

    For many of the researchers and data scientists, Jupyter Notebooks are the de-facto platform when it comes to quick prototyping and exploratory analysis. Right from Paul Romer- the Ex-World bank chief Economist and also the co-winner 2018 Nobel prize in Economics to Netflix, Jupyter Notebooks are used almost everywhere. The browser-based computing environment, coupled with a reproducible document format has made them the choice of tool for millions of data scientists and researchers around the globe. But have we fully exploited the benefits of Jupyter Notebooks and do we know all about the best practises of using it? if not, then this talk is just for you.

    Through this talk/demo, I'll like to discuss three main points:

    1. Best Practises for Jupyter Notebooks since a lot of Jupyter functionalities sometimes lies under the hood and is not adequately explored. We will try and explore Jupyter Notebooks’ features which can enhance our productivity while working with them.
    2. In this part, we get acquainted with Jupyter Lab, the next-generation UI developed by the Project Jupyter team, and its emerging ecosystem of extensions. JupyterLab differs from Jupyter Notebook in the fact that it provides a set of core building blocks for interactive computing (e.g. notebook, terminal, file browser, console) and well-designed interfaces for them that allow users to combine them in novel ways. The new interface enables users to do new things in their interactive computing environment, like tiled layouts for their activities, dragging cells between notebooks, and executing markdown code blocks in a console and many more cool things.
    3. Every tool/features come with their set of pros and cons and so does Jupyter Notebooks/Lab and it is equally important to discuss the pain areas along with the good ones.