Dimensionality Reduction using t-SNE Algorithm in python

deepak9001


0

Votes

Description:

Dimensionality reduction or Dimension reduction is a technique used to reduce number of random variables under consideration, via obtaining a set of principal variables. There are a variety of techniques for doing this including but not limited to PCA, ICA, and Matrix Feature Factorization but our focus will be more on t-SNE which used mostly on non parametric and non-linear data.

Following things will be covered in this:- 1. What is Dimensionality Reduction? 2. What are different Dimensionality Reduction techniques? 3. What is t-SNE? 4. How is it different from other dimensionality Reduction Technique? 5. When to use it and how to use it in python?

Prerequisites:

Participants should have basic python programming knowledge .

Content URLs:

https://www.linkedin.com/pulse/dimensionality-reduction-using-tsne-python-deepak-kumar

Speaker Info:

I am Deepak Kumar and did my Engineering in Computer Science and working in Analytics domain from last 3.5 years. I had started my career as Solution Designing in BI and Analytics specific to Manufacturing , Retail and Insurance Domain in Indian Market. I am using python from last 2.5 years for Data Analytics, Predictive and Machine Learning Model Building.

Speaker Links:

https://www.linkedin.com/in/deepak-kumar-2a5a5620

https://www.linkedin.com/pulse/how-banks-use-transnational-customer-data-increase-revenue-kumar

https://www.linkedin.com/pulse/marketing-mix-modeling-analytical-approach-deepak-kumar

Section: Core Python
Type: Talks
Target Audience: Intermediate
Last Updated:

Hi can you please upload the slides for the talk so that your proposal can be reviewed.

Pradhvan Bisht (~cyber_freak)

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