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Overview of the event

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn from data, without being explicitly programmed.

The "deep" in "deep learning" refers to the number of layers through which the data is transformed. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to its original goals. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation.

Facts & Figures About The Event

7 Speakers
7 Topics
50 Tickets

Conference Schedule

Machine learning and Deep Learning Day (21th February, 2020)

X Topic Abstract

The talk will focus on how we extract insights (using deep learning and transformer models) from large volume of unstructured data and apply them to a variety of business domains, including: product competitive analysis, supply-chain disruptions, supplier monitoring and financial distress prediction.

X Topic Abstract

As the power of Artificial Intelligence has exploded over the last decade, so have the real-world implications that result from these solutions. At first glance, a data-driven decision process would appear to hold little bias given that the model learns from the data it is trained on. However, examples have shown that bias exists in the data collected and results in unfair decisions that can deprive groups of people. At Accenture, the Enterprise Insights Studio team has taken a strong stance on reducing bias in algorithms, especially when the consequences affect people. This team has created intelligent solutions that look to debias predictions in order to promote and develop responsible and fair AI.

Speaker Profile

John Navarro holds an Analytics degree and an Economics degree, both from the University of Chicago. Currently, he is a Lead Data Scientist on Accenture’s Enterprise Insights Studio team. John has over 18 years of experience in data modeling, risk management and team leadership with expertise in the Financial, Retail and Consulting industries. When he isn’t working with data, John enjoys spending time with his three sons, leading his small meet-up groups and striving to maintain a fit lifestyle.

X Topic Abstract

Machine learning can be generally defined as a method or function which enable us to make decisions or learn unknown facts
based on the experience with respect to some class of tasks. This function enable us to solve problems which are difficult to be solved by mere problem solving or programming methods. Several different machine learning methods are used in different areas or problem sets. Basic methods are categorized as supervised machine learning methods and un-supervised machine learning methods. However the basic principle behind all the methods remain the same, that is to learn the facts and make decisions based on the unknown data. The scope of the machine learning methods is vast and varies with the problem definition and situation. I can try to explain some basic Machine learning methods, some research work done, applications, and my own projects.

X Topic Abstract

In this talk, I will walk through the use cases of Machine Learning through out the full investment management process, and provide live examples of application for trading cost forecasting & stock selection.

Speaker Profile

Patrick Fang is a quantitative researcher & data scientist with IHS Markit's Quantitative Services Group, which includes products of Research Signals & Transaction Cost Analysis (TCA). His researches have helped institutional investment managers improve their process and performance. Patrick is a CFA charter holder and CAIA charter holder.


X Topic Abstract

This talk will acquaint the audience with practical and contemporary applications of machine learning. Starting from a context of human learning, the discussion moves onto how machines learn in similar ways, including models both machine supervised and unsupervised, and covering Bayesian inference. The "So what?" question of data science is addressed, and a description of how the academic discipline of Knowledge Management informs current machine learning and analysis.

Speaker profile

Andrew Paul Acosta earned a Masters of Business Administration with honors from Roosevelt University in Chicago, and is working on a PhD in Knowledge Management.

Andrew is the lead data scientist at Milesius Capital Resources LLC, a statistical consulting firm specializing in foreign currency trading, futures, swaps, fixed-income, and options, also offering expertise in financial risk management, and economic research in computing environments such as: R, SAS, MATLAB, C++, Python, and SQL.

X Topic Abstract

Jagannath Banerjee is working as Data Scientist in MedlineIndustries. Jagannath has 12 yrs. experience in critical data driven environment implementing numerous data science and machine learning projects across Fraud detection, CustomerBilling Prediction, Customer Churn, Inventory Optimization and Inventory Audit.
Jagannath is domain expert in Cards and Payments.

Prior to Medline, Jagannath worked as Senior consultant with Cognizant Technology Solutionin US and India helping companies like Discover and Fiserv implement major initiatives suchas Real Time Credit approvals, Enterprise monitoring and alerting systems, Sensitive Dataencryption, Google Pay, Apple Pay etc.

Jagannath has experience in building data science teams multiple time, build cloud and local infrastructure for data science and machine learning environment. Jagannath is well versed with Python, Pandas, Numpy, Scikit-Learn and all relational databases.

X Topic Abstract

cars.com is a two sided digital platform strives to connect car sellers and buyers. Buyers contacts sellers by filling out a “lead form” on the site; data science team at cars.com solved one of the most pressing business questions, assessing lead quality, with predictive modeling, but struggled to gain business stakeholders’ trust to use the model. This talk will go through steps data science team took, in addition to applying cutting-edge algorithm, to have eventually obtained company-wide acceptance and recognition for lead quality model.

Our Sponsors

Use this opportunity to improve the visibility of your organization

Instant Sponsorship
Avail instant sponsorship at just USD 2000

Instant sponsorship includes
• Branding of your company as Bronze Sponsor – Company's Logo on the event page with cross link to your website.
• One Speaking Slot (45 min -non sales talk).
• 10% discount on registration fee for any delegate from your organization.
• Full day attendance at the event with lunch
• 1 x Roll up stand / Brochure distribution at the event
• Online Interview post of your company's senior executive at our media portal

For Silver, Gold Platinum & Titanium Sponsorship opportunites, please request for Sponsorship Brochure via email at contact@1point21gws.com, naveen@1point21gws.info

Our Sponsors

Media Partners


Association partner

Digital Marketing Partner

Marketing Partner

Promotional & Media Partners

Our Past Sponsors

Media Partner


Our Speakers

Sanket Chobe

Data Scientist

Wipro Ltd.

Nick Kadochnikov

Data science Director


Andrew Paul Acosta

Data Scientist

Milesius Capital Resources, LLC

Jagannath Banerjee

Data Scientist - Machine Learning & Azure Cloud

Medline Industries, Inc

John Navarro

Lead Data Scientist


Michael Nicholas Colella

Director,Applied Data science


Patrick Fang


IHS Markit

Sherry Wang

Data Scientist


Want to become Speaker Please register here Register

Our Pricing

Group of three or more(Early Bird)

USD 300

Till November 30, 2019
Group of three or more(Standard)

USD 349

Till February 21, 2020
Individual(Early Bird)

USD 409

Till November 30, 2019
Individual (Standard)

USD 469

Till February 21, 2020

Our Testimonial


Who can attend Machine learning and Deep Learning Day in Chicago?

• Data Engineers/Developers / Scientists
• Analytics Professionals
• Startup Professionals
• Scientists/Researchers
• Professors
• President/Vice president
• Chairs/Directors

And last but not the least……….
Anyone interested in Machine Learning & thrives to make the future developed and better

Why to attend Machine learning and Deep Learning Day in Chicago?

Understand the state of development of Machine learning by exchanges, clearing houses, central counter parties and payment systems, and what it means for you.

What will you learn about?

Detecting where underlying problems and frictions exist in your organisation that will be alleviated by Machine learning technologies. Using Machine learning as a tool for innovation across your organisation

Are there any prerequisites to attend this program?


Do I need to register for the event?

Yes, all conference attendees must register in advance to attend the event.