Nowadays, machine learning, a subfield of artificial intelligence, has become an important aspect in the world of science. Machine learning is used in many different areas and, specially in recent years, it is being taught in many universities at undergraduate, graduate and doctoral levels.
Carrying out a machine learning project requires different 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.
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 a great deal of time and money depending on the scale of the project. So we decided to tackle this problem by implementing tools to do machine learning projects online in Bigpro1 and to shoulder this burden instead of the users.
Bigpro1 has made it possible for the user to complete the project in the shortest time possible and get the best results just by using the online machine learning project tools we provide, which require no special skills in machine learning and modeling.
Fast and easy 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
Provides 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 to be done on this data.
The learning process begins with observations or data, such as examples, direct experiences, or instructions, so that based on the given patterns, better decisions are made in the future. 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 processing large amounts of information more efficient.
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 machine learning.
Machine learning algorithms
Machine learning has a wide range of algorithms, each of which is used in different fields. Here we introduce 2 important and widely used algorithms of machine learning algorithms:
Supervised machine learning algorithm:
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 the opposite of 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. Some of the important clustering algorithms are Hierarchical Clustering, K-means, K-NN (Nearest Neighbors), Principal Component Analysis, Exclusive Value Analysis and Independent Component Analysis.
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:
Nowadays, most people 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 the searches done by the users. 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 different fields. Doing machine learning projects requires programming skills, working with datasets is complex and requires a lot of time, and to do the project 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 time consuming, tedious and difficult.
With these being said and considering the multiplicity of tools related to machine learning and the time required to master these tools, Bigpro1 has come to your aid so that you can do your projects with ease.
You can define your machine learning projects by logging in to your user dashboard and wait for the project to be completed in the shortest time possible.
Since all the tools related to machine learning are available in Bigpro1, you can leave your projects to Bigpro1 without mastering this field.
