Blog - Page 12 of 14

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...

Machine Learning, TensorFlow

Understanding TensorFlow Architecture

Posted on February 3, 2020 by Sandeep Kumar

This Guide gives you an overview of TensorFlow architecture which helps you in TensorFlow programming and think of what’s going on under the hood. You must have a solid understanding of its architecture to write high computational algorithms using the TensorFlow framework. What is TensorFlow T...

Machine Learning

Consume Azure Trained Model in Real-Time

Posted on January 31, 2020 by Sandeep Kumar

A well-trained machine learning model can make a web or mobile app smart enough to predict future actions and deliver content as per user interest. It can show what a user like to buy or read about. This all needs heavy computation and low latency. So we deploy our trained models as a web service [&...

Machine Learning

Linear Regression vs Logistic Regression – Key Difference

Posted on January 31, 2020 by Sandeep Kumar

Both the Machine Learning Algorithms Linear Regression and Logistic Regression comes under the Supervised Machine Learning Technique. Since both the algorithms are of Supervised nature, therefore they use labeled dataset. The major difference between the two is how they have been used. Linear Regres...

Machine Learning

Building Machine Learning Models in Microsoft Azure

Posted on January 31, 2020 by Sandeep Kumar

This hands-on guide explains how to build a machine learning model using a drag-n-drop interface in Microsoft azure ml studio. Azure Ml is a very intuitive tool to run ml experiments without any programming and it turns out a very fast approach to solve a real-world problem using Machine Learning. M...

Machine Learning

Supervised Machine Learning Algorithms You Should Know

Posted on January 31, 2020 by Sandeep Kumar

Introduction Imagine yourself as a first standard student sitting in a classroom wherein your teacher is supervising you, for instance, “this is a dog and this is a cat”. Similarly, in Supervised Machine Learning input is provided to a machine as a labeled dataset. Later, a model can learn from ...