Walmart Labs, the technology unit of retail firm Walmart, has acqui-hired its third Indian startup, Dataturks, a Bengaluru-based Machine Learning (ML) -based Data Annotation Platform that do image bounding, document annotation, NLP and text annotations. The acquisition will help Walmart to use Dataturk's machine learning capabilities for content enrichment. Post acquisition, the 5-member team of Dataturks will be part of Wamart's merchant technology team for catalogue enrichment. Founded in last year, by Gajendra Dadheech and Mohan Gupta, Dataturks makes ML data annotations super easy by allowing teams to upload data, invite other team member to upload and build training/evaluation dataset in hours and find labeled datasets. In December 2018, Walmart Labs had acqui-hired Int.AI, a Bangalore-based mobile analytics aka MicroApps startup and in September last year it acquired Appsfly, a Bangalore-based micro-app startup.

In machine learning, the dataset entirely decides the fate of the algorithms. SVM being a supervised learning algorithm requires clean, annotated data. So do we have to depend on others to provide datasets? Creating dataset using Bing/ Google Image search APIS and then labelling them using Dataturks tool simplifies the entire process, and adds flexibility to the process of machine learning. Here is various image classification datasets.

Whenever we look at any online course, they take off with linear regression and this is a concept that most of us study write from our 8th grades, that is, an equation of a line initially and then gradually fitting of the best fit line. The application of this algorithm is used in machine learning as a way to predict results in the future given the feature vectors, x. So, why is the cost function a squared cost function? Why not have an absolute cost function? Well, there are plenty of reasons as to why we consider this, but when we derive this mathematically, we come across the concept of exponential families under general linear models, which generalize the notion of loss functions for any given model, and thus the square function is actually an exponential family curve.