Perform machine learning project in bigpro1

Easily and with just some simple clicks

machine learning, ensemble learning, Stacking, Supervised learning

Today, machine learning, which is one of the subfields of artificial intelligence, has become an important issue in the world and has affected the life. Machine learning is used in many areas; especially in recent years, it has been taught in various fields and universities at the undergraduate, graduate and doctoral levels.

Carrying out a machine learning project requires some skills in the field of programming, and this requires sufficient mastery of various tools in this field. Due to the great variety of machine learning tools, mastering all of these tools is difficult and time consuming.

Machine learning algorithms, decision tree, regression

Performing a machine learning project

Machine learning is one of the most important topics in today’s world; the importance of this field is so great that data scientists, students and business owners turn to the machine learning project. As mentioned above, doing a machine learning project is difficult and requires plenty of time and money according to the scale of the project. So we decided to tackle this problem by implementing tools for doing machine learning projects online in Bigpro1 and remove the burden from the users. Bigpro1 has made it possible for the user to complete the project in the shortest time and get the best result only by using the online machine learning project tools, without having any special skills in machine learning and modeling.

Easy and fast to use

Access to the complete set of machine algorithms

Proper control of the modeling process to prevent user errors

Complete and understandable output for the user

Provide model evaluation indicators in full

Ability to test the system with a dynamic form builder

What is machine learning?

Machine learning is a subfield of artificial intelligence (AI) that gives the system the ability to automatically learn and improve the experience without explicit planning. Machine learning focuses on the development of computer programs that can access a set of data and use it to learn according to the training given to the machine by this data.

The learning process begins with observations or data, such as examples, direct experience, or instructions, to make better decisions in the future based on the patterns we present. The main goal is to allow computers to learn automatically and adjust actions based on human intervention or assistance. Due to the increasing dependence of humans on machines, in the future we will see a new revolution by artificial intelligence and machine learning.

Machine learning makes it possible to analyze large amounts of data. Although they generally provide faster and more accurate results in identifying profitable opportunities or harmful risks, they may require more time and resources to be trained properly. Combining machine learning with artificial intelligence and cognitive technologies can make it more efficient in processing large amounts of information.

History of machine learning

Machine learning is not really a new topic. Arthur Samuel is one of the pioneers of artificial intelligence who coined the term machine learning in 1959 while working for IBM. He described machine learning as “an area of research in which computers can learn without being programmed”.

Although Arthur Samuel coined the term machine learning in 1959, the idea of machine learning is a little older, dating back to 1950. In 1950, Alan Turing, in his essay, asked the question, “Does the machine think?” And this question marked the beginning of extensive research into artificial intelligence and the machine learning.

Machine learning, Supervised learning, ensemble learning

Machine learning algorithms

Machine learning has a wide range of algorithms, each of which is used in different fields. Here we introduce 3 important and widely used algorithms of machine learning algorithms:

Supervised machine learning algorithm:

In this algorithm, as the name implies, the machine needs an observer or a guide. In machine learning, a set of pre-prepared data is labeled to guide the machine, in other words, the system tries to learn the patterns based on the given samples and use them to predict future events. This system is able to provide the objectives of each new entry after sufficient training. The learning algorithm can also compare its output with the correct and predetermined output and find the existing errors to correct the model accordingly. Regression, Classification, and Decision Tree are three supervised machine learning method

Classification:

A classification algorithm that aims to classify inputs into a certain number of categories or classes, based on the labeled data on which they are taught.  

Regression:

Regression tasks are different because they expect the model to create a numerical relationship between input and output data.  

Decision Tree:

The decision tree is one of the most widely used algorithms for supervised learning. This algorithm specifies methods for dividing a data set based on different conditions.

Unsupervised machine learning algorithm:

Unsupervised machine learning algorithm is opposite to supervised machine learning algorithm and has a much more difficult algorithm. Unsupervised learning is a machine learning method in which users do not need to monitor the model. In this algorithm, the data required for training are neither categorized nor labeled, and the algorithm itself must find a structure in the input and be able to work alone to find information and patterns that did not already exist.

One of the unsupervised learning methods is the clustering method.

 

Clustering:

Clustering is one of the unsupervised machine learning algorithms that allows the system to divide raw and unclassified data into different groups based on their similarities and differences. Also, the number of clusters detected by this algorithm and the details of its groups can be adjusted. Cluster parsing is a poor choice for applications such as customer segmentation and targeting. Important clustering algorithms can be Hierarchical Clustering, K-means, K-NN (Nearest Neighbors), Principal Component Analysis, Exclusive Value Analysis and Independent Component Analysis.

bagging, boosting, ensemble learning

Ensemble learning algorithm:

Ensemble learning algorithm is a meta-general approach in machine learning that pursues better predictive performance by combining multi-model predictions. Ensemble learning methods are popular when the best performance in a modeling project is the most important predictor of results.

Although there seem to be an infinite number of sets that can be made for prediction modeling problem, there are three methods that dominate the field of ensemble learning. So much so that each, instead of the algorithm itself, is a context that has created more specialized methods. The three main classes of ensemble learning methods are: bagging, boosting and stacking.

Bagging:

This algorithm is used to improve the stability and accuracy of machine learning algorithms.

Boosting:

Boosting is another method of ensemble learning algorithms.

Stacking:

Its purpose is to predict the best output.

machine learning, decision tree, Machine learning algorithms

Applications of machine learning in today's world

Machine learning has different uses, some of which are discussed here:

 

Application of machine learning in social networks:

In today’s world, most people spend at least one  hour a day on social networks such as Instagram or Twitter, and use machine learning as easily as possible. For example: The use of machine learning on Instagram is such that the Explore section is adjusted exactly according to the interests of each user. If a person searches and views a topic on Instagram for several consecutive days, in the next few days, he will see more videos and images in that field in the Explore section.

 

Application of machine learning in online stores:

Another application of machine learning is the use in online stores, which try to increase their sales. Many domestic and foreign online stores, such as Amazon, use machine learning algorithms. These stores use machine learning algorithms to obtain information such as: preferences and interests of users in shopping to use this information to seek customer satisfaction and implement a kind of online and smart marketing.

 

Application of machine learning in search engines:

Most people today cannot spend a day without the Internet and Google search. Most people use Google search engines on a daily basis to do school research, college projects, read the latest news, and so on. Google search engines have evolved from the past to present due to the study of keywords and language features used in user searches. The purpose of Google’s algorithm is to find and provide the best response to users, for this purpose Google uses artificial intelligence to try to adapt to the needs of users and increase the quality of response content and the degree of compliance with user requests.

Performing machine learning projects in Bigpro1

In recent years, machine learning has been taught in many university disciplines at various levels. Machine learning has many applications in various fields. Doing machine learning projects requires programming skills, working with datasets is complex and requires a lot of time, to do the project, and you must have sufficient mastery of machine learning tools. Given the variety of tools available for doing machine learning projects, mastering all of these tools is tedious and difficult.

With these explanations, due to the multiplicity of tools related to machine learning and spending a lot of time to master these tools, Bigpro1 has come to your aid to do your projects easily.

You can define your machine learning project by logging in to your user dashboard and wait for the project to be completed in the shortest possible time.

Since all the tools related to machine learning are available in Bigpro1, you can leave your projects to Bigpro1 without mastering this field.