Kumar Vishwesh

Hello, I am a seasoned AI/ML Product Management professional and Data Science Development Lead with ~9 years of progressive experience in the field. My passion for AI and its potential to transform the world has led me to work on cutting-edge projects that leverage machine learning and artificial intelligence to solve complex problems. As an AI/ML Product Manager, I have a proven track record of successfully launching and managing AI/ML-based products, ensuring that they meet both customer and business requirements. My ability to understand customer needs and translate them into product features, coupled with my technical expertise, has allowed me to deliver impactful solutions that meet business objectives. As an AI/ML Data Science Development Lead, I have led teams of talented data scientists, overseeing the development and deployment of machine learning models and algorithms. I have a strong background in data science and statistics, and machine learning. Throughout my career, I have had the opportunity to work with leading companies in the tech industry, helping them leverage the power of AI to drive innovation and growth. I am passionate about staying up-to-date with the latest trends in AI and using my expertise to create products that make a difference. Thank you for taking the time to read my introduction.

Categorical features with high cardinality: Dealing with Feature Hashing

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Firstly, “Dealing with categorical features with high cardinality: Feature Hashing”, is an interesting question. So, this post will be interesting and will help a lot of learners. Introduction to Feature Hashing Generally, many machine learning algorithms are not able to use non-numeric data. So, we represent these features using strings. And we need some way … Read more

Deal with class imbalance (without generating synthetic samples): Clustering Based Bagging Algorithm (CBBA)

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to deal with class imbalance

To deal with class imbalance, take a look at “In classification, how do you handle an unbalanced training set?”. Definitely, the answers were very creative. The rookie way to deal with class imbalance The rookie’s way: The approach of under-sampling the majority class is an effective method in dealing with classifying imbalanced data sets. But … Read more

Understanding Heteroskedasticity and Transformations in Linear Regression Analysis

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Linear regression is a widely used statistical method for predicting outcomes based on input variables. However, analyzing the results of a linear regression model can be complicated, particularly when there is heteroskedasticity or a violation of the assumption of homoscedasticity. This can lead to incorrect or unreliable predictions and can be challenging to diagnose and … Read more

Curse of Dimensionality: An intuitive and practical explanation with examples

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“Curse of Dimensionality: An Intuitive and practical explanation with Examples”, this article will definitely consolidate your concept. “As the number of features or dimensions grows, the amount of data we need to generalize accurately grows exponentially.” Charles Isbell, Professor and Senior Associate Dean, School of Interactive Computing, Georgia Tech Curse of dimensionality The common theme … Read more