# Machine Learning Algorithms I

## Introduction

In this lesson, we are going to learn about the most popular machine learning algorithms. Note that we are not going to do a technical deep-dive as it would be out of our scope. The goal is to cover details sufficiently enough so that you can navigate through them when needed. * The key is to know about the different possibilities so that you can then go deeper on a need’s basis*.

In this algorithm tour we are going to learn about:

- Linear Regression
- Logistic Regression
- Decision Trees
- Naive Bayes
- Support Vector Machines, SVM
- K-Nearest Neighbors, KNN
- K-Means
- Random Forest
- Dimensionality Reduction
- Artificial Neural Networks, ANN

## 1. Linear Regression

Linear Regression is probably *the* most popular machine learning algorithm.

Remember in high school when you had to plot data points on a graph with an *X*-axis and a *Y*-axis and then find the line of best fit? That was a very simple machine learning algorithm, linear regression. In more technical terms, linear regression attempts to represent the relationship between one or more independent variables (points on *X* axis) and a numeric outcome or dependent variable (value on *Y* axis) by fitting the equation of a line to the data:

$Y ={a*X + b}$

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