Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
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Building a single-variable regressor

Let's see how to build a single-variable regression model. Create a new Python file and import the following packages:

import pickle

import numpy as np
from sklearn import linear_model 
import sklearn.metrics as sm 
import matplotlib.pyplot as plt

We will use the file data_singlevar_regr.txt provided to you. This is our source of data:

# Input file containing data 
input_file = 'data_singlevar_regr.txt'

It's a comma-separated file, so we can easily load it using a one-line function call:

# Read data
data = np.loadtxt(input_file, delimiter=',') 
X, y = data[:, :-1], data[:, -1]

Split it into training and testing:

# Train and test split 
num_training = int(0.8 * len(X)) 
num_test = len(X) - num_training

# Training data
X_train, y_train = X[:num_training], y[:num_training]

# Test data
X_test, y_test = X[num_training:], y[num_trai...