Unfolding Logistic Regression. Don’t get confused with the name as it says regression but Logistic Regression is a supervised learning algorithm which is used for carrying out classification tasks.

Supervised Learning vs Unsupervised Learning. This article will introduce us to the tools and techniques developed to make sense of unstructured data and discover hidden patterns.

This is how decision trees are combined to make a random forest. In this article, I describe how this can be used for a classification task with the popular Iris dataset.

How to do bias-variance tradeoff the right way in Machine Learning. One of the most common decisions that data scientists and machine learning experts have to face daily is how to go about validating their models.

The Magic of Reactive Supervision. High Quality NLP Data Labeling Using Social Media Interactions

This article is about an introduction to SVMs, understanding the mathematical intuition, Regularization, implementing the concept in code, and then knowing the fields of its applications.

In this blog, I’m going to talk about how I have gotten an accuracy greater than 88% (92% epoch 22) with Cifar-10 using transfer learning, I used VGG16 and I applied a very low constant learning rate and I implemented the function upsampling to get more data points for processing

Deploy your first end-to-end ML model using Streamlit. I am going to deploy a Supervised machine learning model to predict the age of a Abalone and in the next part of the tutorial we will host this web app on Heroku.

In this post, we are going to dive into the concepts of Supervised Learning or rather known as Classification in the domain of Machine Learning. We will discuss the definitions, components, examples of classification.

Naive Bayes Classification. Probability basics and Bayes theorem. Naive Bayes Classification is a supervised machine learning algorithm. It is one of the many algorithms that are derived from the Bayes’ theorem.

How to do the feature selection in Machine Learning. An important question for your first data science-related job

Understanding LeNet: A Detailed Walkthrough. In this article, we explore LeNet, a group of CNNs developed by Yann Le-Cun and others in the late 1990s.

What deep learning needs for better COVID-19 detection. The world probably doesn’t need another neural network, but it needs a coffee chat with those on the front lines.

Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people. But what exactly is machine learning and what is making the current boom in machine learning possible?

In this article, we will use logistic regression to perform binary classification. Binary classification is named this way because it classifies the data into two results.

Various Types Training a Machine to become intelligence. In this phase we teach or train the machine using data ie: information which is well labeled that means some data is already have with the correct answer.

We construct multiple datasets using the same set of the original dataset but with combinations of records where the duplicate record entry is allowed.

The Pyramid Principle applied to Classification Algorithms. How to better remember and understand machine learning classifiers.

A Brief Survey of Time Series Classification Algorithms. In this article, I will introduce five categories of time series classification algorithms with details of specific algorithms.

A Euclidean journey of KNN. KNN or in long-form so-called K-Nearest neighbors. A very famous algorithm for used for classification.