About Vidhi Chugh

Vidhi Chugh is an award-winning AI/ML innovation leader and an AI Ethicist. She works at the intersection of data science, product, and research to deliver business value and insights. She is an advocate for data-centric science and a leading expert in data governance with a vision to build trustworthy AI solutions.

Vidhi Chugh Posts (23)

• Which Metric Should I Use? Accuracy vs. AUC - 04 Oct 2022
  
  Depending on the problem you’re trying to solve, one metric may be more insightful than another.
• An Intuitive Explanation of Collaborative Filtering - 15 Sep 2022
  
  The post introduces one of the most popular recommendation algorithms, i.e., collaborative filtering. It focuses on building an intuitive understanding of the algorithm illustrated with the help of an example.
• Machine Learning Algorithms – What, Why, and How? - 07 Sep 2022
  
  This post explains why and when you need machine learning and concludes by listing the key considerations for choosing the correct machine learning algorithm.
• Visualizing Your Confusion Matrix in Scikit-learn - 06 Sep 2022
  
  Defining model evaluation metrics is crucial in ensuring that the model performs precisely for the purpose it is built. Confusion Matrix is one of the most popular and effective tools to evaluate the performance of the trained ML model. In this post, you will learn how to visualize the confusion matrix and interpret its output.
• Support Vector Machines: An Intuitive Approach - 23 Aug 2022
  
  This post focuses on building an intuition of the Support Vector Machine algorithm in a classification context and an in-depth understanding of how that graphical intuition can be mathematically represented in the form of a loss function. We will also discuss kernel tricks and a more useful variant of SVM with a soft margin.
• Why is Data Management so Important to Data Science? - 16 Aug 2022
  
  High data availability may help power digital transformation, but data management systems are needed to keep that data organized and make it accessible. Read this article to see why data management is important to data science.
• The Importance of Experiment Design in Data Science - 12 Aug 2022
  
  Do you feel overwhelmed by the sheer number of ideas that you could try while building a machine learning pipeline? You can not take the liberty of trying all possible ways to arrive at a solution - hence we discuss the importance of experiment design in data science projects.
• Benefits Of Becoming A Data-First Enterprise - 22 Jul 2022
  
  Data is everywhere but only data is not sufficient to reap the benefits that come with it. It needs to be organized to enable the organizations to make more informed business decisions. In this article, we will learn what are the various benefits of being a data-first enterprise and using the data in developing a business intelligence solution.
• Hidden Technical Debts Every AI Practitioner Should be Aware of - 07 Jul 2022
  
  Coming to think of technical debt in ML systems leads to the additional overhead of ML-related issues on top of typical software engineering issues.
• A Structured Approach To Building a Machine Learning Model - 10 Jun 2022
  
  This article gives you a glimpse of how to approach a machine learning project with a clear outline of an easy-to-implement 5-step process.