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:

1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Naive Bayes
5. Support Vector Machines, SVM
6. K-Nearest Neighbors, KNN
7. K-Means
8. Random Forest
9. Dimensionality Reduction
10. 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:

$$Y ={a*X + b}$$