18 Jul

I got an opportunity to join ODSC third time. I attended it in three different states  in last 2.5 years. This time it was on Immersive AI. The best part was, I joined two days full day training program. The schedule was full power packed trainings with some of the experts in the fields. 

I like the fact that, you get to do some hands on as well for some sessions, who enjoys more interactive platform than only power point presentations. I attended couple of very good and interesting sessions like - 

Machine Learning in Algo Trading

Speaker - Stefan Jansen

The name captured my attention - so I was there.

Highlight - building algorithm and simple reinforcement leaning with Q learning 

People who don’t know what is reinforcement learning  here you go-

Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Q-learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model (hence the connotation "model-free") of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.

Machine learn with R

Speaker : Jared Lander

I always prefer Python over R, but this session made me inclined to R again. It was awesome session with data modeling for NY data.

I remember when I was learning it in my masters, I chosen python over R after exploring it for couple of months as I found it difficult to debug the R code when I build my machine learning model for big data. But still I feel statistics is good in R. The Alteryx tool uses R programming for statistics. May be sometimes you might need to change the ways you learn any programming language. Loved this session

Data science career keynotes - SPEAKER : Triveni Gandhi, PhD

This was a quite good keynote session to give an overview of how it goes with in different data science roles in the market and finding best fit for yourself.x

Recommendation systems

Speaker : Vinny Senguttuvan

Was quite elaborative session explaining examples of YouTube , Amazon and Netflix 

Couple more sessions I managed to attend were - Scikit learn with python, Time series analysis

My takeaways from the conferences are always - to know what’s happening around the field you are working, where did it reach so far, networking, and of course learning more. These kind of platform might not give you immediate opportunity to get into the field but definitely fulfills your needs and opens more doors for you. It certainly upto you how you utilize that time.