In recent years, there have been huge advances in the field of neural networks specifically to train models for image recognition and classification. To design and implement an efficient and powerful image classification solution. You need to know how such image data can be read into your program, and how you can represent images as tensors to feed into your model to get the best out of ML algorithms.

Image representation:

All images can be represented using pixels. images are essentially just matrices where the individual cells of the matrix contain pixel data. An image can be divided into a grid of…

Azure Synapse Analytics,is a new analytics Microsoft engine, formerly Azure Data Warehouse. The new service represents not only a name change, but also an evolution of the way of doing analytics within Azure. We’ll look into the details in this article.

what is Azure Synapse Analytics ?

Azure synapse Analytics is a limitless analytics service platform that brings together data integration, enterprise data warehousing, big data processing into a single managed environment with no systems integration required.This has combined a couple different technologies:

  • Azure Data Lake Storage allows you to store massive amounts of different data types inside of one location or lake.
  • Azure SQL Data…

This article Introduces concepts of unsupervised learning with a focus on implemeting some Clustering techniques using Scikit-Learn

Unsupervised learning :

Unsupervised learning is often the case in the real world, that data is unlabeled. You might apply an unsupervised learning technique to make unlabeled data self sufficient. For example, if you want to identify photos of a specific individual, you might feed a model lots of different photographs, millions of them until it starts identifying similar features. Unsupervised learning techniques are also used for latent factor analysis, anomaly detection, quantization, especially with colors. or as pre-training for supervised learning problems, such as classification and regression. …

Machine Learning models cannot work directly with text data. you need to encode your text data in some numeric form.

Any text document is essentially just a sequence of words which you can tokenize into individual words, After transforming your document into a sequence or list of words, you can encode and represent each word in a numeric form using somekind of numeric encoding.

Once you get the numeric representation for each word in your document, you aggregate your data into a tensor, Now the question is how do you transform individual words into numeric form.

there are diffrent techniques…


Data Engineer and Machine learning enthusiast with a great intrest in cloud technologies

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