Supervised Learning

Machine Learning for Beginners – A Guide to compare different ensemble techniques with scikit-learn in Python

Machine Learning for Beginners – A Guide to compare different ensemble techniques with scikit-learn in Python.

Machine Learning Mastery: A Practical approach to Simple Linear Regression using R

A Practical approach to Simple Linear Regression using R   Simple Linear Regression is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables. One variable denoted x is regarded as an independent variable and other one denoted y is regarded as a dependent variable. It is assumed that …

Machine Learning Mastery: Linear Regression Using Tensorflow

Linear Regression Using Tensorflow   Brief Summary of Linear Regression Linear Regression is a very common statistical method that allows us to learn a function or relationship from a given set of continuous data. For example, we are given some data points of x and corresponding y and we need to learn the relationship between them that is called …

Machine Learning Mastery: Multiple Linear Regression using Python

Multiple Linear Regression using Python   Linear Regression: It is the basic and commonly used type for predictive analysis. It is a statistical approach to modelling the relationship between a dependent variable and a given set of independent variables. These are of two types: Simple linear Regression Multiple Linear Regression Let’s Discuss Multiple Linear Regression …

Machine Learning Mastery: Linear Regression (Python Implementation)

Linear Regression (Python Implementation)   This article discusses the basics of linear regression and its implementation in Python programming language. Linear regression is a statistical approach for modelling relationship between a dependent variable with a given set of independent variables. Note: In this article, we refer dependent variables as response and independent variables as features for simplicity. In order to …

Machine Learning Mastery: Momentum-based Gradient Optimizer introduction

Momentum-based Gradient Optimizer introduction   Gradient Descent is an optimization technique used in Machine Learning frameworks to train different models. The training process consists of an objective function (or the error function), which determines the error a Machine Learning model has on a given dataset. While training, the parameters of this algorithm are initialized to random …

Machine Learning Mastery: Mini-Batch Gradient Descent with Python

Mini-Batch Gradient Descent with Python   In machine learning, gradient descent is an optimization technique used for computing the model parameters (coefficients and bias) for algorithms like linear regression, logistic regression, neural networks, etc. In this technique, we repeatedly iterate through the training set and update the model parameters in accordance with the gradient of …

Machine Learning Mastery: Multiclass classification using scikit-learn

Multiclass classification using scikit-learn   Multiclass classification is a popular problem in supervised machine learning. Problem – Given a dataset of m training examples, each of which contains information in the form of various features and a label. Each label corresponds to a class, to which the training example belongs to. In multiclass classification, we have a finite …

Machine Learning Mastery: Types of Learning – Supervised Learning

Types of Learning – Supervised Learning What is Learning for a machine? A machine is said to be learning from past Experiences(data feed in) with respect to some class of Tasks, if it’s Performance in a given Task improves with the Experience.For example, assume that a machine has to predict whether a customer will buy a specific product lets …