 An Introduction to Machine Learning  The Complete Guide
 Data Preprocessing for Machine Learning  Apply All the Steps in Python
 Regression
 Learn Simple Linear Regression in the Hard Way(with Python Code)
 Multiple Linear Regression in Python (The Ultimate Guide)
 Polynomial Regression in Two Minutes (with Python Code)
 Support Vector Regression Made Easy(with Python Code)
 Decision Tree Regression Made Easy (with Python Code)
 Random Forest Regression in 4 Steps(with Python Code)
 4 Best Metrics for Evaluating Regression Model Performance
 Classification
 A Beginners Guide to Logistic Regression(with Example Python Code)
 KNearest Neighbor in 4 Steps(Code with Python & R)
 Support Vector Machine(SVM) Made Easy with Python
 Kernel SVM for Dummies(with Python Code)
 Naive Bayes Classification Just in 3 Steps(with Python Code)
 Decision Tree Classification for Dummies(with Python Code)
 Random forest Classification
 Evaluating Classification Model performance
 A Simple Explanation of Kmeans Clustering in Python
 Hierarchical Clustering
 Association Rule Learning  Apriori
 Eclat Intuition
 Reinforcement Learning in Machine Learning
 Upper Confidence Bound (UCB) Algorithm: Solving the MultiArmed Bandit Problem
 Thompson Sampling Intuition
 Artificial Neural Networks
 Natural Language Processing
 Deep Learning
 Principal Component Analysis
 Linear Discriminant Analysis (LDA)
 Kernel PCA
 Model Selection & Boosting
 Kfold Cross Validation in Python  Master this State of the Art Model Evaluation Technique
 XGBoost
 Convolution Neural Network
 Dimensionality Reduction
Classification  Machine Learning
Classification: Classification is a machine learning task of predicting the value of a categorical variable(target or class). This is done by building a modal based on one or more numerical and categorical variables( predictors, attributes or features). It is considered an instance of supervised learning.
Classification is the problem of identifying to which of a set of categories a new observation belongs, on the basis of training data containing observations. Classification models include linear models like Logistic Regression, SVM, and nonlinear ones like KNN, Kernel SVM, and Random Forests.
Now, we will learn how to implement the following Machine Learning Classification models:

Logistic Regression

KNearest Neighbors (KNN)

Support Vector Machine (SVM)

Kernel SVM