A.I, Data and Software Engineering

# Search results formachine learning

## Sparse Matrices for Machine Learning quick note

In machine learning, many matrices are sparse. It is essential to know how to handle this kind of matrix. Sparse vs Dense Matrix First, it is good to know that sparse matrix looks similar to a normal matrix, with rows, columns or other indexes. But a sparse matrix is comprised of mostly zero (0s) values. They are distinct from dense matrices with mostly non-zero values. A matrix is sparse if many...

## Machine learning quick note

Machine learning is a terminology to describe the uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. You can think of machine learning as the brains behind AI technologies, and AI technologies do the actions. More technically, machine learning...

## Top programming languages to learn 2020-2021

“What is the best programming language to learn” is generally not a good question. A language should bind to a purpose, developing environment. Set it aside, popular languages often come with trending jobs. Here is the list of top 7 programming languages to consider in 2020 and 2021. 1.  Python python trademark The programming language continues to be one of the best programming...

## Metrics for regression and classification – quick note

In this article, we review some common metrics and their uses for two main ML problems, i.e. regression and classification. Regression Metrics Most of the blogs have focussed on classification metrics like precision, recall, AUC etc. For a change, I wanted to explore all kinds of metrics including those used in regression as well. MAE and RMSE are the two most popular metrics for continuous...

## Common Loss functions and their uses – quick note

Machines learn by means of a loss function which reflects how well a specific model performs with the given data. If predictions deviate too much from actual results, loss function would yield a very large value. Gradually, with $$optimization$$ function, parameters are modified accordingly to reduce the error in prediction. In this article, we will quickly review some common loss functions and...

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