# Notes On Machine Learning

## I.R.A.S.: Identify … Recognize … Analyze … Synthesize …

14 min readMar 2, 2023

`1. Neural Network`

2. Logistic Regression

3. Linear Regression

4. Random Forest

5. Support Vector Machines (SVM)

6. Nearest Neighbors

7. K-means Clustering

8. Markov Chain

9. Principal Component Analysis (PCA)

10. Stochastic Gradient Descent

11. Decision Trees

12. AdaBoost

13. Genetic Algorithm

# Neural Network

This example defines a simple neural network with one input and one output neuron, and trains it to predict the values of the output given the input. After training, the model is used to make predictions for the input values, and the resulting line fit is plotted along with the data points. The resulting plot shows the line fit to the data, demonstrating the ability of the neural network to learn the underlying relationship between the input and output.

`# generates some data points along a straight line with some added noise. `

import numpy as np

import matplotlib.pyplot as plt

from…