Member-only story
Artificial Intelligence Bootcamp
Introduction, Applications, and Examples in Python
Artificial Intelligence
Artificial intelligence (AI) is the overarching term used to describe a system that is capable of learning about data for a purpose. The way in which an artificial intelligence system goes about learning determines whether it is a machine learning system or a deep learning system.
It is important to understand the difference between each of these terms so that when artificial intelligence is referenced in different contexts we can determine what algorithm is — better yet, what algorithm should be used to solve the problem at hand. In this article, I will run you through artificial intelligence Bootcamp. After reading this you will understand artificial intelligence, machine learning, deep learning, and the process to create data-driven solutions to complex real-world problems.
To follow along with the code in this article you will need to install Python and its accompanying libraries. If you aren’t adept at managing virtual environments, or just want a prepackaged data science platform you may want to check out Anaconda. If you wish to follow along with the dataset that I will be using throughout this article it can be found here.
Exploratory Data Analysis
The first step in any data-driven solution is exploratory data analysis. This can be done by implementing basic statistics or visualization tools to get a better idea of the kind of data we are working with.
Let’s get started by creating histogram plots for our data…
import pandas as pd
import numpy as np
import matplotlib.pyplot as pltdata = pd.read_excel('Data Science/banknote.xlsx')data.hist()plt.show()
