Supervised Learning

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

Hits: 8 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 …

Machine Learning Mastery: Linear Regression Using Tensorflow

Hits: 11 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 …

Machine Learning Mastery: Multiple Linear Regression using Python

Hits: 41 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 …

Machine Learning Mastery: Linear Regression (Python Implementation)

Hits: 6 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 …

Machine Learning Mastery: Momentum-based Gradient Optimizer introduction

Hits: 4 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 …

Machine Learning Mastery: Mini-Batch Gradient Descent with Python

Hits: 6 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 …

Machine Learning Mastery: Multiclass classification using scikit-learn

Hits: 16 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 …

Machine Learning Mastery: Types of Learning – Supervised Learning

Hits: 12 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 …

Machine Learning Mastery: Types of Regression Techniques

Hits: 17 Types of Regression Techniques   When Regression is chosen? A regression problem is when the output variable is a real or continuous value, such as “salary” or “weight”. Many different models can be used, the simplest is the linear regression. It tries to fit data with the best hyperplane which goes through the …

Machine Learning Mastery: Basic Concept of Classification (Data Mining)

Hits: 3 Basic Concept of Classification (Data Mining)   Data Mining: Data mining in general terms means mining or digging deep into data which is in different forms to gain patterns, and to gain knowledge on that pattern. In the process of data mining, large data sets are first sorted, then patterns are identified and …

Machine Learning Mastery: Getting started with Classification

Hits: 9 Getting started with Classification   Introduction As the name suggests, Classification is the task of “classifying things” into sub-categories.But, by a machine! If that doesn’t sound like much, imagine your computer being able to differentiate between you and a stranger. Between a potato and a tomato. Between an A grade and a F- …

Machine Learning Mastery: Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python

Hits: 19 Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python   In Machine Learning and Data Science we often come across a term called Imbalanced Data Distribution, generally happens when observations in one of the class are much higher or lower than the other classes. As Machine Learning algorithms tend to increase accuracy …

Machine Learning Mastery: One Hot Encoding of datasets in Python

Hits: 14 One Hot Encoding of datasets in Python   Sometimes in datasets, we encounter columns that contain numbers of no specific order of preference. The data in the column usually denotes a category or value of the category and also when the data in the column is label encoded. This confuses the machine learning …

Machine Learning Mastery: Label Encoding of datasets in Python

Hits: 6 Label Encoding of datasets in Python   In machine learning, we usually deal with datasets which contains multiple labels in one or more than one columns. These labels can be in the form of words or numbers. To make the data understandable or in human readable form, the training data is often labeled …

Machine Learning Mastery: Feature Scaling – Part 2

Hits: 5 Feature Scaling – Part 2   Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing to handle highly varying magnitudes or values or units. If feature scaling is not done, then a machine learning algorithm tends to …