Learning is a vital part of an intelligent system whether it takes place in human beings, animals or machines. Machine learning is a system of learning in which machines are enabled to learn without any explicit programming. Intelligent machines were introduced in the middle of the twentieth century which gave birth to a new branch of computer science that deals with artificial intelligence (AI). Artificial intelligence enables machines to execute their tasks efficiently by using different kinds of software. Statistical methods establish the basis of intelligent software that is used in machine learning. There are four major machine learning techniques viz. supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. Machine learning paves the way to machine learning systems. Computer scientists today are more and more interested in building machine learning systems. Python is considered, among these scientists, as one of the most beneficial programming languages for this task.
Goals and Accomplishments of Machine Learning
Machine learning revolves around a basic concern of learning. There are four basic aims of machine learning which differ from one another regarding their success stories, methodological perspectives and approaches to evaluation.
A first group of researchers describes a goal that contains the modeling of all those mechanisms that underlie human learning. This psychological framework establishes learning algorithms that are normally considered in line with knowledge of human cognitive architecture. Observed learning behaviors are also focused. A large range of computational models is produced by using this approach. These models work in various domains including perception, natural language, problem-solving and motor control.
A second group of researchers is in favor of an empirical approach in machine learning. Their aim is to establish general principles which create a link between the learning behavior and the characteristics of learning algorithms. This approach includes the variation of either the algorithm or the domain in running experiments and later on observing the influence of this manipulation on learning. Experiments have developed generalizations that recommend alternative methods suggesting ideas for improved algorithms, areas of weakness and sources of task difficulty.
A third group of scholars considers machine learning as an area of mathematical study. The goal of this school of thought is to formulate and prove theorems about entire classes of learning problems and design algorithms that can solve these problems. This theory of computational learning has developed many insightful and useful theorems keeping in view the difficulty level of the learning tasks and methods for their solution.
A fourth group of scholars develops an application approach towards machine learning. This approach considers real-world problems as the main concern of machine learning. These applications of artificial intelligence mostly rely on expert systems which need years to develop because machine learning transforms data into knowledge and automates the process of knowledge acquisition.
Machine Learning Systems
Machine learning (ML) can be described as an algorithm or technique that provides computers with the ability to reason and learn from the available data. Machine learning systems contain various components in their development. These systems are quite different from other computer systems when it comes to building, evolving, delivering, deploying and testing.
ML systems always face different challenges when the behavior or architecture of the system is to change. While building real machine learning systems, it is important to learn how to face these challenges which often occur during production. Machine learning systems are basically developed on intelligent systems.
Building an Intelligent System and Its Elements
An intelligent system provides a platform for the intelligence to improve and evolve especially when the users interact with the system. An intelligent system normally contains the following features in it.
A meaningful objective. An intelligent system always keeps a meaningful objective that justifies its existence. On the one hand, it is useful for the users so that users can complete their tasks on time. On the other hand, it is always achievable for a system that can be easily built and run. Developing a meaningful objective is a critical task because it ultimately leads to success. It is difficult to develop a meaningful objective for an intelligent system.
The intelligent experience. An intelligent experience is mostly concerned with the system’s intelligence (predictions made by the machine learning) which comes out as its output and introduces it to users in order to get the defined outcomes. To go through all this, it must keep its own user interface that can be adjusted on the basis of predictions and help in establishing the way for the intelligence to shine when it is needed. When it is wrong, it minimizes cost of mistakes. Explicit and implicit feedback of the users must be made transparent by the intelligent experience in order to improve the intelligence of the system.
Implementation of the intelligence. This includes everything that is required in the execution of intelligence, movement of intelligence where it is needed, and intelligent management, to highlight the experiences of intelligence, collection of telemetry to get an idea whether the system is functioning and to collect the feedback of the users that will help improving the intelligence in future
Intelligence creation. Intelligence creation is the main objective of intelligent systems for achieving success in the desired field and tasks. This intelligence evolves from various places and sources ranging from basic heuristic learning to complicated machine learning. This creation of intelligence in its organization must handle the problem in an appropriate manner with a relevant type of intelligence for the problem. This intelligence can be developed effectively with the help of a team of people with strenuous efforts and over an extended time.
The orchestration. The Life of an intelligent system depends on the organization of its various components. The balance of these elements must be maintained in order to attain its desired and defined objectives. An orchestration process contains the control of the intelligent system regarding its changes, keeping the quality of the intelligence in sync with the experience, finalizing what telemetry collects to remove problems and deciding what amount of budget is to be consumed in building and utilizing the new intelligence.
Machine Learning Systems: Python Language
This is an open source, object-oriented programming language. It was developed by Guido Von Rossum. It is simple compared to other computer languages and easy to learn. It is a blend of dynamic semantics which is very useful and efficient for app and web development. It is very popular for its dynamic binding and typing options along with rapid application development. Python code is easily readable and developers feel more comfortable in translating it than other computer languages ( such as c ++ and Java).
Further, it permits different members of the team to work in collaboration without having different experiences and language barriers which ultimately reduces the cost of program development and maintenance. Different packages and modules are supported by Python which means that a modular style of the program can be designed easily.
If the required module or package is developed once, it can be used or reused in various projects including import and export of these modules with easy access. Another benefit of Python is the availability of the interpreter and the standard library free of cost in both source and binary form. All the tools used for Python are available on all platforms. It is an attractive option for developers who do not want to pay a high development cost.
Uses of Python Language and Its Role in Building Systems
Python in its use is a programming language for general purposes. This means it can be used almost for every purpose. In fact, it is an interpreted language. This means that during the run time of the computer, its written code is not translated to a readable computer format. This feature is not present in most of the computer languages. This kind of language is even named “scripting language” because in its beginning it was basically developed for trivial projects.
This concept of “scripting language” has been changed since its beginning because Python can be used now in writing various commercial applications on a large scale and in different styles instead of relying only on banal ones. With the increased popularity of the internet, this reliance on Python has been increased more than ever. Most platforms and web applications depend on Python, including YouTube, Google search engine and the transaction system of the New York Stock Exchange (NYSE).
A programming language must be very trustworthy if it is to be used in the system of a stock exchange. In fact, NASA also uses Python when it is programming its space machinery and other equipment. Python is widely used to show images or numbers, process text, solve scientific equations and save data. Basically, Python is used in systems to process many of the elements required for efficient use in completing tasks.
In a Nutshell
Machine learning is a sub-field of the larger domains of artificial intelligence and cognitive science. In this new era of innovations and advance, learning is the main goal of machine learning. Statistical and mathematical methods used in developing software focus on skillful completion of tasks. Elements of the system are binding forces which ultimately develop a system of learning. Python language is easy to handle and useful for all applications.