DataHour: Model Parameters vs Hyperparameters - Techniques in ML Engineering

DataHour Recording

Find the resources used in the DataHour HERE.

About the DataHour:

Understanding the significance of Parameter Tuning is a must for every ML Engineer while selecting the right machine learning model and improving the performance of the model. Make sure that those parameters selection are influenced and learnt from the data, and it needs to be accessed before the model gets into the train. Ultimately the performance of the machine learning model improves with a more acceptable choice of hyperparameter tuning and selection techniques.

In this DataHour, Shanthababu will cover the following topics:

1. Differentiating the Model and Hyperparameter - What are Hyperparameters? How to differ from a Model Parameter?
2. Importance of Hyperparameter
3. Steps to Perform Hyperparameter Tuning.
4. Influencing on Models with respect to various aspects.
5. Hyperparameter Optimization Techniques and simple use cases.

Prerequisites: Enthusiasm of learning Data Science,  Python Programming, Machine Learning Life cycle, Basic Algorithms understanding and an Idea about Model performance

Who is this DataHour for?

• Students & Freshers who want to build a career in the Data-tech domain.
• Working professionals who want to transition to the Data-tech domain.
• Data science professionals who want to accelerate their career growth

Speaker:

Shanthababu Pandian

AI & Data Analytics Lead at Cognizant

Shanthababu has 20+ years of experience in Information Technology (IT). Expertise in ML & Data Architect and Program Delivery including liaising with clients, gathering, eliciting requirements, architecting, and devising cost-effective solutions as per delivery frameworks and mitigating project/delivery risks. Expertise in providing insights into obtaining, describing, visualizing, and using data for making right business decision through quantitative research, advance data sourcing and data profiling for Cloud, Analytics MSBI and AIML Solutions; Adoption Skilled Data Architecting, Modeling, Data Quality, Data Governance, Data Privacy and Data Integration, Data Platform, Data analysis and Data Science program.He has done his PG Program in Artificial Intelligence & Machine Learning from Great Learning & University of Texas and is currently working as AIA Architect and Delivery with Cognizant. 

You can connect with him on Linkedin