In this book, the most relevant machine-learning algorithms are going to be discussed and used in exercises to make you familiar with them. In order to explain these algorithms and to understand the content of this book, there are a few general concepts we need to visit that are going to be described hereafter.

First of all, a good definition of machine learning is the subfield of computer science that has been developed from the fields of pattern recognition, artificial intelligence, and computational learning theory. Machine learning can also be seen as a data-mining tool, which focuses more on the data analysis aspects to understand the data provided. The purpose of this discipline is the development of programs, which are able to learn from previously seen data, through tunable parameters (usually arrays of double precision values), that are designed to be adjusted automatically to improve the resulting predictions. In this way, computers can predict a behavior, generalizing the underlying structure of the data, instead of just storing (or retrieving) the values like usual database systems. For this reason, machine learning is associated with computational statics, which also attempt to predict a behavior based on previous data. Common industrial applications of machine-learning algorithms are spam filtering, search engines, optical character recognition, and computer vision. Now that we have defined the discipline, we can describe the terminology used in each machine-learning problem, in more detail.

Any learning problem starts with a data set of n samples, which are used to predict the properties of the future unknown data. Each sample is typically composed of more than a single value so it is a vector. The components of this vector are called features. For example, imagine predicting the price of a second-hand car based on its characteristics: year of fabrication, color, engine size, and so on. Each car i in the dataset will be a vector of features x(i) that corresponds to its color, engine size, and many others. In this case, there is also a target (or label) variable associated with each car i, y(i) which is the second-hand car price. A training example is formed by a pair (x(i), y(i)) and therefore the complete set of N data points used to learn is called a training dataset {(x(i), y(i));i=1,…,N}. The symbol x will denote the space of feature (input) values, and y the space of target (output) values. The machine-learning algorithm chosen to solve the problem will be described by a mathematical model, with some parameters to tune in the training set. After the training phase is completed, the performance of the prediction is evaluated using another two sets: validation and testing sets. The validation set is used to choose, among multiple models, the one that returns the best results, while the testing set is usually used to determine the actual precision of the chosen model. Typically the dataset is divided into 50% training set, 25% validation set, and 25% testing set.

The learning problems can be divided in two main categories (both of which are extensively covered in this book):

We are going to focus on unsupervised learning methods in Chapter 2, Machine Learning Techniques: Unsupervised Learning, while the most relevant supervised learning algorithms are discussed in Chapter 3, Supervised Machine Learning. Chapter 4, Web Mining Techniques will approach the field of web-mining techniques that can also be considered as both supervised and unsupervised methods. The recommendation systems, which are again part of the supervised learning category, are described in Chapter 5, Recommendation Systems. The Django web framework is then introduced in Chapter 6, Getting Started with Django, and then an example of the recommendation system (using both the Django framework and the algorithms explained in Chapter 5, Recommendation Systems) is detailed in Chapter 7, Movie Recommendation System Web Application. We finish the book with an example of a Django web-mining application, using some of the techniques learned in Chapter 4, Web Mining Techniques. By the end of the book you should be able to understand the different machine-learning methods and be able to deploy them in a real working web application using Django.

We continue the chapter by giving an example of how machine learning can be used in real business problems and in tutorials for Python libraries (NumPy, pandas, and matplotlib), which are essential for putting the algorithms learned in each of the following chapters into practice.

import pandas as pd
df = pd.read_csv('ad-dataset/ad.data',header=None)
df=df.replace({'?': np.nan})
df=df.replace({'  ?': np.nan})
df=df.replace({'   ?': np.nan})
df=df.replace({'    ?': np.nan})
df=df.replace({'     ?': np.nan})
df=df.fillna(-1)

adindices = df[df.columns[-1]]== 'ad.'
df.loc[adindices,df.columns[-1]]=1
nonadindices = df[df.columns[-1]]=='nonad.'
df.loc[nonadindices,df.columns[-1]]=0
df[df.columns[-1]]=df[df.columns[-1]].astype(float)
df.apply(lambda x: pd.to_numeric(x))

import numpy as np
dataset = df.values[:,:]
np.random.shuffle(dataset)
data = dataset[:,:-1]
labels = dataset[:,-1].astype(float)
ntrainrows = int(len(data)*.8)
train = data[:ntrainrows,:]
trainlabels = labels[:ntrainrows]
test = data[ntrainrows:,:]
testlabels = labels[ntrainrows:]

from sklearn.svm import SVC
clf = SVC(gamma=0.001, C=100.)
clf.fit(train, trainlabels)

score=clf.score(test,testlabels)
print 'score:',score

>>> sudo pip install modulename

import numpy as np