JupyterLab & Jupyter Notebook Cheat Sheet Commands
Are you starting to create machine learning models (using python programming) using JupyterLab or Jupyter Notebook? This post list down some commands which…
Missing Data Imputation Techniques in Machine Learning
Have you come across the problem of handling missing data/values for respective features in machine learning (ML) models during prediction time? This is different from…
Code of Ethics in Artificial Intelligence (AI) – Key Traits
Do you know that organizations have started paying attention to whether AI/machine learning (ML) models are doing unbiased, safe and…
Ethical AI – Lessons from Google AI Principles
Is your organization using AI/machine learning for many of its products or planning to use AI models extensively for upcoming…
Why take Google Machine Learning Crash Course?
This post represents my thoughts on why you should take the Google Machine Learning (ML) Crash Course. Most importantly, this course would…
How to Choose Right Machine Learning Algorithms?
In this post, you will learn about tips and techniques which could be used for selecting or choosing the right…
QA – How Reliable are your Machine Learning Systems?
In this post, you will learn about different aspects of creating a machine learning system with high reliability. It should…
Configure Nexus Repository for Docker Registry (Windows)
In this post, you will learn about how to configure Nexus Repository OSS on Windows as a Docker Private Registry. The goal of doing this can…
ML Metrics – Sensitivity vs Specificity Difference
In this post, we will try and understand the concepts behind evaluation metrics such as sensitivity and specificity which is used to…
Why is QA needed for Machine Learning Models?
Given that the machine learning models are also a kind of conventional software application, the quality assurance principles applied to…
Testing Machine Learning Models on Dual Coding Principles
This post intends to propose a technique termed as Dual Coding for testing or performing quality control checks on machine…
QA – Blackbox Testing for Machine Learning Models
Data science/Machine learning career has primarily been associated with building models which could do numerical or class-related predictions. This is…
Assessing Quality of AI Models from QA Standpoint
In this post, you will learn about the definition of quality of AI / machine learning (ML) models. Getting a good understanding of what is…
QA – Metamorphic Testing for Machine Learning Models
In this post, you will learn about how metamorphic testing could be used for performing quality control checks/testing on machine learning models. The post is…
QA – Why Machine Learning Systems are Non-testable
This post represents views on why machine learning systems or models are termed as non-testable from quality control/quality assurance perspectives. Before I proceed ahead, let…
QA – Testing Features of Machine Learning Models
In this post, you will learn about different types of test cases which you could come up for testing features of the data…