AI/ML and
Deep Learning

Machine learning is the heart of AI. Similar to any species, AI needs continuous learning. So, let’s see how we make AI learn and what types of machine learning are there. In this article, we will understand the three different types of Machine Learning; however, we must first understand Artificial Intelligence. Artificial Intelligence (AI) is the ability of a computer

Q-learning stands out in machine learning as a pivotal technique that helps algorithms make optimal decisions by learning from their experiences. Introduction Imagine training a robot to navigate a maze. Now, think of teaching a computer to master chess. This is the realm where Q-learning becomes crucial. Q-learning doesn’t give machines specific instructions on decision-making. Rather, it lets them learn

Cross-validation is a technique used in machine learning to assess how well a model will generalize to an independent data set. What is Cross-Validation? Imagine you’re a teacher preparing a test for your students. You want to ensure that the test accurately reflects how well your students understand the material and not just how well they memorized specific questions you

Deep learning is attracting rapidly growing research and development in today’s world, where artificial intelligence is exponentially growing with newer breakthroughs by the day. What is Deep Learning? Deep Learning is one of the methodologies that is receiving a lot of attention. Deep Learning (DL) is a subset of Machine Learning that teaches computers to do what humans naturally do.

In machine learning, optimizing the learning models is a critical step. This is where Gradient Descent emerges as a central optimizing algorithm. What is Gradient Descent? Machine learning hinges on creating models that predict outcomes from various inputs. However, these models don’t start perfectly; they initially operate on random parameters, which are not ideal for making accurate predictions. Creating a

Adversaries in Machine Learning? When we talk about new technological developments, sometimes we forget that there is another side for whom it’s an opportunity to find new ways to scam. We are using Machine Learning (ML) models more and more each day. They have become a part of our lives without even realizing it most of the time. It is

Logistic and Linear Regression are two fundamental statistical methods used for predictive modeling within the supervised machine learning framework. Regression analysis and classification are two of the most common approaches in machine learning. Linear regression is one of the primary and most fundamental tools for regression analysis. In contrast, Logistic regression is a fundamental tool for classification tasks in machine

The Random Forest algorithm is a versatile and powerful tool capable of handling various data-driven challenges for machine learning. The concept of Random Forest took birth because of the need for simplicity and ensemble learning. In Layman’s terms, Ensemble Learning is stacking together a lot of classifiers to improve performance. What is a Random Forest? Random Forests is a Supervised

Before we discuss Artificial Narrow Intelligence, let’s dive into this article; let’s quickly recap the definition of Artificial Intelligence: Artificial Intelligence (AI) is the ability of a computer or a computer-controlled robot to perform tasks that humans usually do as they require human intelligence. There are three types of Artificial Intelligence (AI): In this article, we will be going over

The Decision Tree algorithm is a type of tree-based modeling under Supervised Machine Learning. Decision Trees are primarily used to solve classification problems (the algorithm, in this case, is called the Classification Tree), but they can also be used to solve regression problems (the algorithm, in this case, is called the Regression Tree). The concept of trees is found in