Machine Learning: Machine Learning is a method of teaching machines/computers to make a prediction based on some data and experien...

Data Preprocessing: Data Prepossessing is the first stage of building a machine learning model. It involves transforming raw data...

Regression: Basically, Regression is a statistical approach to find the correlations between variables(dependent and independent)....

Simple Linear Regression: Simple Linear Regression is a method used to fit the best straight line between a set of data points. A...

Multiple Linear Regression: Multiple linear regression is the most common form of linear regression analysis. As a predictiv...

Polynomial Regression: Polynomial regression is a form of regression analysis in which the relationship between the independe...

Support Vector Regression: Support Vector machine support linear and nonlinear regression that we can refer to as SVR. Instead of...

Decision tree Intuition: Decision Trees are an important type of algorithm for predictive modeling machine learning. The represen...

Random Forest Regression: A random forest is a version of ensemble learning. Ensemble learning is when we take multiple algorithm...

R-Squared Intuition: R-squared is a very interesting parameter. It means how much of the difference in outcome is explained by the...

Classification: Classification is machine learning task of predicting the value of a categorical variable(target or class). This i...

Logistic Regression Intuition: Logistic regression is used to analyze the relationship between a dependent variable and independen...

K-Nearest Neighbor Intuition: K-nearest neighbor is a non-parametric lazy learning algorithm, used for classification and re...

Support Vector Machine: Support vector machines are supervised learning models with associated learning algorithms that analyze da...

Kernel SVM: In this tutorial, we gonna show you kernel support vector machine. In the previous tutorial, We learned support v...

Bayes Theorem: Bayes Theorem is the fundamental result of probability theory, it puts the posterior probability P(H|D) of a...

Decision Tree Classification: Decision tree builds classification or regression models. Decision trees classify instances by sorti...

Random forest Classification: Random forests are an ensemble learning method for classification, that operates by constructi...

Evaluating Classification Model performance: This section consists of different types of tutorial. Here given below: False posi...

Clustering: Clustering is similar to classification, but the basis is different. In Clustering, you don't know what you are lookin...

Hierarchical Clustering: Hierarchical clustering is where you build a cluster tree (a dendrogram) to represent data, where each gr...

Association Rule Learning: In this part, you will understand and learn how to implement the following Association Rule Learning mo...

Eclat Intuition: Today, we are talking about the Eclat model. It is similar to the a priori algorithm. Here, we actually talking a...

Reinforcement Learning: Reinforcement Learning is a branch of Machine Learning, also called Online Learning. It is used to solve i...

The Multi Armed Bandit problem: The multi-armed bandit problem is a classic reinforcement learning example where we are given...

Thompson Sampling Intuition: Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized...

Natural Language Processing: Natural language processing (NLP) is a field of computer science concerned with the interactions betw...

Deep Learning: Deep Learning is an artificial intelligence function that imitates the workings of the human brain in processing da...

Artificial Neural Networks: An artificial neural network (ANN), usually called Ã¢â‚¬Å“neural...

Dimensionality Reduction: Dimensionality Reduction is the process of reducing the number of random variables under consideration b...

Linear Discriminant Analysis (LDA): LDA is used as a dimensionality reduction technique. It is used in the preprocessing step for...

Kernel PCA in python: Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extract...

Model Selection & Boosting: Model Selection is the undertaking of choosing a statistical model from an arrangement of candidat...

K-fold cross Validation: Cross-validation, sometimes called rotation estimation, or out-of-sample testing is any of various simila...

XGBoost in Python Step 1: First of all, we have to install the XGBoost. Now, we need to implement the classification problem. In t...

Convolution Neural Network: A Convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural ne...