It seems a bit confusing when we need to decide which activation function will be the best for a particular neural network. Through this post, you will be able to understand all about activation functions as it’s meaning, different types…
Speech is an integral part of communication. The importance of speech recognition has been increasing over the years with the advancement of technology. Through this tutorial, you will be able to get familiar with the Automatic speech recognition system, the…
To solve the mystery of bias-variance trade off, we need to first understand what is meant by bias and variance in data science. Why it is so important for analysis of machine learning model. Does it create any issue while…
As you know, the literal meaning of regularization is to manage or control things. The machine learning model also demands regularization sometimes. Through this post, you will be able to know about what is regularization in machine learning, why does…
In previous post, we learned to design machine learning model on Azure notebook, how to create workspace on Azure and how to register trained model on cloud. “Part 2: Machine learning model deployment on Microsoft Azure ” is continuation of…
You might have worked on various data processing techniques, machine learning algorithms from understanding the business requirement, building the machine learning model to the model’s validation process. Through this series, we are going to learn how to build an ML…
In the following post, we have explained the class imbalance problem in classification models and techniques to handle class imbalance problems using python. Machine Learning suffers from various issues. Class Imbalance is one of them. But do not worry! We’ll…
In previous posts, we have discussed different image pre-processing techniques through different operations using OpenCV. This post includes the next step towards image processing i.e. feature detection algorithm and feature matching using SIFT (Scale-invariant feature transform) and SURF (Speeded-up Robust…
Linear Regression is the most frequently used algorithm in data science. In this post, you will get to know all the details of linear regression, how to use it and when to use it. Table of content What does regression…
Today, in this blog, we will be understanding the error metrics used for Classification and Regression. You have a model but you need to make sure that it also performs well. This is where error metrics come into the picture.…