We live in the age of data, which benefits from a better computing power of computers and the vastness of storage resources.
That’s what we’re referring to when we talk about Big Data. This data or information is growing daily, but the real challenge is to make sense of it.
Companies and organisations are trying to cope by building intelligent systems using the concepts and methodologies of data science, data mining and machine learning. Among these, machine learning, especially with Python, is the most exciting area. It would not be wrong to call machine learning the application and science of algorithms that give meaning to data.
In this article, we will talk about what machine learning is, its applications in the industry, its major concepts and we will put its concepts into practice with the Python programming language.
Where does this need for machine learning come from?
At present, humans are the most intelligent and advanced species on Earth because they can think, evaluate and solve complex problems. Artificial Intelligence is still in its initial stage and has not surpassed human intelligence in many aspects. The question is therefore why it is necessary to teach the machines. The most appropriate response is to make decisions, on the basis of data, efficiently and on a large scale.
In recent times, organizations are investing heavily in new technologies such as artificial intelligence, machine learning and deep learning to obtain key information from data to perform multiple tasks and solve problems.
We can call this machine-made decisions, especially to automate the process.
These decisions, guided by data, can be used, instead of programming logic, in problems that cannot be programmed inherently.
Currently, machine learning is used in self-driving cars, cyber fraud detection, face recognition, friend suggestion by Facebook, etc. Several large companies like Netflix and Amazon have built machine learning models that use a large amount of data to analyze user interests and recommend products accordingly.
What is machine learning? Machine learning is considered a subset of artificial intelligence that focuses on the development of algorithms that allow a computer to learn for itself from past data and experiences.
We can summarize it as follows:
Machine learning allows a machine to learn automatically from data, improve its performance from experiences, and predict facts without being explicitly programmed.
What are the applications of machine learning?
We use machine learning without knowing it in our daily life using the following tools: Google Maps, Google Assistant, Alexa, etc. We will detail in this section the most common applications of machine learning.
The recognition of images:
voice recognition:
Recommendation of products:
The different machine learning techniques with Python:
There are different algorithms, techniques and methods of ML that can be used to build models in order to solve real-life problems using data. In this section, we will discuss these different types of methods.
Supervised learning:
Machine learning algorithms are currently the most commonly used as supervised algorithms or curricula.
This learning method or algorithm takes the sample of data, that is, the learning data, and the output, that is, the labels or responses, associated with each sample of data during the learning process.
The main purpose of supervised learning algorithms is to learn an association between input data samples and corresponding outputs after performing multiple instances of training data. For example, we have x as input variable and y as output variable.
The objective of a supervised learning algorithm is to find an f function for matching the input variable (x) with the output variable (Y), that is, an expression of the type Y=f(x). In order to obtain new input data (x), we can easily predict the output variable (Y) for these new input data.
The functioning of supervised learning can be easily understood through the example and diagram below:
Based on the tasks to be performed, supervised learning algorithms can be divided into two broad categories:
• The Classification • And the Regression
Classification: The main purpose of classification-based tasks is to predict categorical output labels or responses for the given input data. The output will be based on what the model learned during the training phase. Since we know that output categorical responses mean discrete and unordered values, each output response will belong to a specific class or category. The classification and associated algorithms will be discussed in detail in the following sections.
Regression:
The main purpose of regression algorithms is to predict output labels or responses that are continuous numerical values, for the given input data. The output will be based on what the model learned in its training phase. Basically, regression models use input data characteristics (independent variables) and their corresponding continuous numerical output values (outcome or dependent variables) to learn a specific association between the corresponding inputs and outputs.
Unsupervised learning:
There is a type of machine learning, called unsupervised learning, in which we train models using a variety of unexpired data, and allow them to create these data without being subjected to an official.
The purpose of unsupervised learning is to find the underlying structure of a data set, to group that data according to its similarities, and to represent that data set in a compressed format.
The operation of unsupervised learning can be understood by the diagram below: Here, we have taken unlabelled input data, which means that it is not categorized and the corresponding output is not given either. Now, these unlabeled inputs are sent to the machine learning model to train it. First, it will interpret the raw data to find hidden models of the data and then apply appropriate algorithms such as k-means clustering, decision tree, etc.
After applying the appropriate algorithm, the algorithm divides the data into groups based on similarities and differences between the data.
Based on the tasks to be performed, unsupervised learning algorithms can be divided into two broad categories:
The clustering:
Clustering is a method of grouping objects into clusters so that objects with the most similarities remain in a group. Objects in one group must have less, if any, similarity to objects in another group. Cluster analysis finds commonalities between the data and categorizes them according to the presence or absence of these commonalities.
The association:
An association rule is an unsupervised learning method that is used to find relationships between variables in a large database. It determines which elements appear together in a database. The association rule makes the marketing strategy more effective. For example, people who buy an item X (such as a loaf of bread) also tend to buy an item Y (butter/jam). A typical example of an association rule is the analysis of the household basket. We are now going to put machine learning into practice in Python. But before we tackle it, we invite you to download this book on the initiation to the Hadoop ecosystem. In this part of the article, we will see how to carry out a machine learning project step by step. We will study in particular the method of classification of supervised learning. For a better understanding of this tutorial you must have a basic knowledge of the python programming language and a global knowledge of the Scikit-Learn library. Our articles on Python programming for data and on Scikit-Learn will give you all the necessary basics.
What is machine learning classification with Python?
The classification algorithm is a supervised learning technique that is used to identify the category of new observations on the learning database. In classification, a program learns from a given set of data or observations and then classifies new observations into a number of classes or groups. For example, Yes or No, 0 or 1, Spam or No Spam, cat or dog, etc. Classes can be called targets/labels or categories.
We bring out the classification variables that are a category, the opposite of what is done and has no value, we set examples: yellow and red colors, fruits and animals to other examples.
As the classification algorithm is a supervised learning technique, it takes labeled input data, which means it contains inputs with the corresponding output. The best example of an ML classification algorithm is the email spam detector.
We can divide the Classification algorithms into two main categories:: Linear models: • Logistic regression • Support Vector Machines – SVM Non-linear models: • K-Nearest Neighbors (K closest neighbours) • SVM Kernel • Naïve Bayes (Bayesian networks) • Classification by decision tree • Classification Random Forest In this part, we will try to solve a Python machine learning problem with the K-Nearest Neighbors model (K closest neighbors). The KNN (K-Nearest Neighbors) algorithm is a simple, non-parametric supervised machine learning algorithm that can be used to solve classification and regression problems.
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