Machine Learning Tutorials

An Introduction to Machine Learning | The Complete Guide

Machine learning is a subfield of Artificial Intelligence. It is the study of computer algorithms that can learn from data and make predictions. This article will give a thorough introduction to machine learning and its applications.

Data Preprocessing for Machine Learning | Apply All the Steps in Python

Data preprocessing is an important step of data mining in which raw data get into a clean and understandable format.

Regression

Regression models are used to predict a continuous outcome variable (y) based on the value of one or multiple predictor variables (x).

Learn Simple Linear Regression in the Hard Way(with Python Code)

Simple linear regression is the prediction of a single dependent variable based on a single independent variable. It tries to find a simple linear function that represents the relationship between the independent and the dependent variable.

Multiple Linear Regression in Python (The Ultimate Guide)

Multiple Linear Regression is a regression technique used for predicting values with multiple independent variables. In this tutorial, the basic concepts of multiple linear regression are discussed and implemented in Python.

Polynomial Regression in Two Minutes (with Python Code)

Polynomial regression is a special version of linear regression. It uses a polynomial equation to build model around nonlinear data. This tutorial explains the concepts and implements it in Python.

Support Vector Regression Made Easy(with Python Code)

Support Vector regression implements a support vector machine to perform regression. In this tutorial, you'll get a clear understanding of Support Vector Regression in Python.

Decision Tree Regression Made Easy (with Python Code)

Decision tree is a supervised machine learning algorithm that breaks the data and builds a tree-like structure. The leaf nodes are used for making decisions. This tutorial will explain decision tree regression and show implementation in python.

Random Forest Regression in 4 Steps(with Python Code)

Random forest is an ensemble machine learning algorithm. It uses randomized decision trees to make predictive models. This tutorial explains the concepts of random forest and how to implement it in Python.

4 Best Metrics for Evaluating Regression Model Performance

There are many regression metrics to evaluate the performance of a regression model. Four of the best metrics are-Mean Absolute Error(MAE)m Root Mean Square Error(RMSE), Coefficient of determination or R2, and Adjusted R2.

Classification

The Classification algorithm is a task of predicting the category of new observations on the basis of training data.

A Beginners Guide to Logistic Regression(with Example Python Code)

Logistic regression is the appropriate regression analysis to conduct when the dependent variable is binary.

K-Nearest Neighbor in 4 Steps(Code with Python & R)

K nearest neighbor is a nonparametric learning algorithm used for both regression and classification. This tutorial will help you understand KNN algorithm and implement it in R and Python.

Support Vector Machine(SVM) Made Easy with Python

Support Vector Machine(SVM) is a supervised learning algorithm. It tries to find the optimal hyperplane to divide the data into two classes. This tutorial discusses about SVM and how it works.

Kernel SVM for Dummies(with Python Code)

Support vector machine (SVM) is a powerful supervised machine learning algorithm that is used both for classification and regression.

Naive Bayes Classification Just in 3 Steps(with Python Code)

Naïve Bayes is one of the simple and most effective probabilistic supervised algorithms which is used for building fast machine learning models that can make quick predictions.

Decision Tree Classification for Dummies(with Python Code)

Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks that creates a model that predicts the value of a target variable by learning simple decision rules from the features of a dataset.

Random forest Classification

Random Forest is an ensemble learning technique that builds a number of decision trees and selects the decision which gets the majority vote.

Evaluating Classification Model performance

Evaluating classification model performance is a method to find the best method for your problem by analyzing the number of correct and incorrect predictions made by a model compared to the actual outcomes(target value).

A Simple Explanation of K-means Clustering in Python

Clustering is an unsupervised learning algorithm that divides data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups.

Hierarchical Clustering

Hierarchical clustering is an unsupervised learning algorithm that is used to group together the unlabeled data points having similar characteristics.

Association Rule Learning | Apriori

Association rule is a procedure that aims to observe frequently occurring patterns, correlations, or associations from datasets.

Eclat Intuition

ECLAT algorithm aims to observe frequently occurring patterns, correlations, or associations from datasets. It works in a vertical manner so ECLAT algorithm is a faster algorithm than the Apriori algorithm.

Reinforcement Learning in Machine Learning

Upper Confidence Bound (UCB) Algorithm: Solving the Multi-Armed Bandit Problem

The Upper Confidence Bound algorithm is the kind of algorithm that helps us to perform exploitation and exploration together.

Thompson Sampling Intuition

Thompson Sampling is an algorithm that follows exploration and exploitation to maximize the cumulative rewards obtained by performing an action.

Artificial Neural Networks

Natural Language Processing

Natural Language Processing shows how machines interact with human language.

Deep Learning

Deep Learning is a field that is concerned with algorithms inspired by the structure and function of the human brain

Principal Component Analysis

Principal Component Analysis is an unsupervised, non-parametric statistical technique primarily used for dimensionality reduction in machine learning.

Linear Discriminant Analysis (LDA)

Linear discriminant analysis is a supervised classification method that is used to create machine learning models based on the dimensionality reduction method.

Kernel PCA

Principal component analysis (PCA) is a tool for dimensionality reduction and feature extraction for a linearly separable dataset.

Model Selection & Boosting

Model Selection is the undertaking of choosing a statistical model from an arrangement of candidate models, given information.

K-fold Cross Validation in Python | Master this State of the Art Model Evaluation Technique

k fold cross-validation is a model evaluation technique. It splits the data set into multiple trains and test sets known as folds. Where all folds except one are used in training and the rest one is used in validating the model.

XGBoost

XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework.

Convolution Neural Network

A Convolutional neural network is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery.

Dimensionality Reduction

Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset.