Blog - Page 9 of 11

Understand Confusion Matrix Using Real-life Classification Example

Posted on March 25, 2020 by Sandeep Kumar

First I’d like to explain what is the use of a confusion matrix. Classification Problems are solved using Supervised Machine learning algorithms. In these problems, our goal is to categories an object using its features. For e.g, Identify a fruit using its taste, color and size or check out ...

Machine Learning

Explain Echelon Form of a Matrix

Posted on February 26, 2020 by Sandeep Kumar

Echelon Form of a matrix is used to solve a linear equation by converting a complex matrix to a simple matrix. A matrix is in an Echelon Form if it satisfies some conditions which we’ll discuss in this post. We must know how to convert a matrix into Echelon Form and simplify our matrix for [&h...

Machine Learning

Working of Bagging and Boosting in Ensemble Learning

Posted on February 10, 2020 by Sandeep Kumar

Bagging and Boosting both are Ensemble Learning techniques. Ensemble Methods are an important addition to Data Scientists toolbox. Here, weak learners combined together to become strong learners. And offers better performance than an individual one. What is an Ensemble Method The main logic behind t...

Machine Learning

What is the difference between Variance and Bias in Machine Learning?

Posted on February 8, 2020 by Sandeep Kumar

From the perspective of Supervised Machine Learning, we know all models have errors. We need to minimize the error so as to make the model useful. For this, we need to minimize two major sources of error- Bias and Variance. What is Bias? The tendency of the algorithm to learn wrong details from the ...

Machine Learning

Decision Tree vs Random Forest in Machine Learning

Posted on February 8, 2020 by Sandeep Kumar

Machine Learning is the sub-branch of Artificial Intelligence. It gives a system the ability to learn and become better from past experiences. Decision tree and random forest are two Supervised Machine Learning techniques. A decision tree is a simple and decision-making diagram. Certainly, for a muc...

Machine Learning

Decision Tree in Machine Learning with Example

Posted on February 6, 2020 by Sandeep Kumar

Decision Tree algorithm belongs to the Supervised Machine Learning. It can use to solve Regression and Classification problems. It creates a training model which predicts the value of target variables by learning decision rules inferred from training data. What is Decision Tree? It is easy...

Machine Learning, TensorFlow

Solving Regression Problems Using Neural Network

Posted on February 5, 2020 by Sandeep Kumar

This tutorial explains solving regression problems using a neural network approach instead of using Supervised Machine Learning Algorithm. You’ll be able to solve simple regression problems at the end of this tutorial. We’ll use Jupyter Notebook to write the code and TensorFlow as our ma...

Machine Learning

Setup Kaggle Notebook for Ml Experiments

Posted on February 5, 2020 by Sandeep Kumar

This hands-on guide will teach you to use the Kaggle Platform to start your own machine learning experiments. Kaggle.com is an online community of data scientists and machine learning practitioners and You can learn lots from other publically shared notebooks. We, at aitude.com, highly recommend thi...

Machine Learning

Basics of K-Nearest Neighbor Algorithm

Posted on February 5, 2020 by Sandeep Kumar

K-NN or K-Nearest Neighbor works on the simple principle- “Birds of the same feather flock together.” K-NN is a Supervised Machine Learning Algorithm. It solves both Regression and Classification problems. And mostly used for Classification problems. It classifies the data points based on ho...

Machine Learning

SVM: Difference between Linear and Non-Linear Models

Posted on February 4, 2020 by Sandeep Kumar

What are Support Vector Machines? SVM is a Supervised Machine Learning Algorithm which solves both the Regression problems and Classification problems. SVM finds a hyperplane that segregates the labeled dataset(Supervised Machine Learning) into two classes. Support Vectors These data points are clos...