Category: Data Science
Nuances of Defining the Goal in Machine Learning Life Cycle
Since the emergence of the age of Data Science, Machine Learning has become a full-fledged discipline. Similar to Software Development, even Machine Learning has...
Overview of the exam AI-900 : Azure AI Fundamentals
Motivating Azure AI Fundamentals With the explosion of data and computing, Machine Learning and Deep Learning have become the cornerstone of modern information systems....
Machine Learning and Human Learning
The funny thing about machine learning is that it closely seems to mimic human-learning algorithms which when studied closely(by a human:), could help him to learn and...
The Azure Machine Learning Designer
At MS Ignite this year, Azure Machine Learning Designer went GA, which is the next generation of the classic Azure ML Studio. Although it is quite similar to its...
Understanding Precision and Recall
Classification is one of the most widely used supervised Machine Learning technique. In classification, the target variable takes a limited set of values. For this...
Cumulative Distribution in Azure Databricks
Imagine that you receive a requirement to calculate the aggregations like average on a range of percentiles and quartiles, for a given dataset. There are two ways to...
Knowing when to consider Machine Learning
A Data Scientist is the one who not only knows how to use machine learning but the one who knows when to avoid it. Today, I will stand on the shoulder of a giant i.e....
Nuances of identifying Palindrome with Python Strings
One of the introductory examples in the world of programming the algorithm to identify Palindrome strings. Being the most widely used language, python is no exception to...
Is Artificial Intelligence a threat to Software Engineers?
The fear of the singularity Every threat is an opportunity The Forbes magazine article Software Ate The World, Now AI Is Eating Software reminded me of the movie...
Azure Machine Learning Visual Interface
Prologue: The Azure Machine Learning family Data Science process entails data extraction, data preparation, modeling, training, testing and evaluation, deployment etc....