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Artificial Intelligence and Machine Learning have been making our lives easier for quite some time. Today, we’re going to talk about Python For AI & Machine Learning.
AI is used in analytics for data-based predictions that enable people to come up with more effective strategies and strong solutions.
FinTech applies AI in investment platforms to conduct market research and make predictions about where to invest funds for greater profits. The travel industry utilises AI to launch chatbots and make the user journey better. Python Web App Examples are proof of that.
Due to such high processing power, AI and ML are absolutely capable of providing a better user experience, that is not only more apt but also more personal, making it more effective than ever.
Artificial Intelligence Overview
In basic terms, the goal of using AI is to make computers think as humans do. This may seem like something new, but the field was born in the 1950s.
Imagine that you need to write a Python program that uses AI to solve a sudoku problem. A way to accomplish that is to write conditional statements and check the constraints to see if you can place a number in each position. Well, this Python script is already an application of AI because you programmed a computer to solve a problem!
Machine learning (ML) and deep learning (DL) are also approaches to solving problems. The difference between these techniques and a Python script is that ML and DL use training data instead of hard-coded rules, but all of them can be used to solve problems using AI. In the next sections, you’ll learn more about what differentiates these two techniques.
How AI & ML Are Technologies Of The Future?
AI and ML are extremely useful when it comes to processing and analysing large and complicated data. It not limited like the human brain, which can handle data until a certain point only.
They are competent to provide precise predictions and insights that can contribute to boosting your business, decreasing product costs, and increasing productivity. Any Premier Python Web Development Company can help you to develop such solutions.
These multiskilled aspects of AI and ML are the reasons various industries have started applying them in their processes. Python For Machine Learning will be the future, for sure.
Based on research by Deloitte, companies that apply AI are going through a technological transformation that is leading them to increase their productivity.
The report also predicts that in the upcoming 18 to 24 months, the total number of companies using AI in their processes and products to attain higher efficiency and strategic goals will mostly go up. In a nutshell, with lesser efforts, AI can deliver better output.
Reasons to Choose Python for AI and Machine Learning
While AI and ML are reshaping the world with its wonderful contributions to the technological world, it is important for developers and programmers to use the right programming language to make the most of AI and ML.
Python offers some great reasons to pick it as the programming language for AI and ML.
A Great Library Ecosystem
Python is the most in-demand programming language used for AI as it offers a significant choice in libraries.
A library is a module or a group of modules that are published by various sources such as PyPi which comes with a pre-written portion of code that helps users to attain some functionality and carry out different actions.
Python libraries offer base-level items. This saves developers’ time as they do not have to code them from the start every time.
ML needs regular data processing and Python For Machine Learning libraries allow developers to access, manage, and transform data.
Here are some common libraries that you can use for AI and ML:
- Scikit-learn is used to manage fundamental ML algorithms such as clustering, linear and logistic regressions, classification, and regression among others.
- Pandas is used for high-level data structures and analysis. With it, developers can merge and filter data and also collect data from other external sources like Excel.
- Keras is used for deep learning. As it accesses the GPU in addition to the CPU of the computer, it allows you to make fast calculations and prototyping.
- TensorFlow is used to work with deep learning by arranging, instructing, and utilising artificial neural networks with enormous datasets.
- Matplotib is used to develop 2D plots, charts, histograms, and other formats of data visualisation.
- NLTK is used for working with processing, computational linguistics, and natural language recognition.
- Scikit-image is used for image processing.
- PyBrain is used for neural networks, unsupervised, and reinforcement learning.
- Caffe is used for deep learning that lets you switch between the CPU and the GPU and processes 60+ mln images a day using a single NVIDIA K40 GPU.
- StatsModels is used for statistical algorithms and data exploration.
In the PyPI repository, you can find and use more Python libraries.
Low Barrier To Entry
When you work in the ML and AI industry, you need to handle data that needs to be processed in the most efficient and effective way.
With a low entry barrier, data scientists can easily start using Python for AI development without spending much time and energy in learning the language.
This gives an answer to the question – Is Python Good for AI?
Python programming language happens to be similar to the English language that we use daily, hence, the learning process is simpler.
As its syntax is easy, you can simply work with complicated systems, ensuring clear relations between the system elements.
For example, here is the code that is written to find out if an input number is prime:
The last row shows if the result of this code is that the test number is not a prime number. It is written in simple English words which makes the code easy to understand.
Also, you can access the detailed documentation available, and take help from the Python Performance Tips which can help you solve issues.
Flexibility
Python’s flexibility makes it a prominent choice for machine learning.
- Developers get a choice between using OOPs or scripting.
- You do not have to recompile the source code as you can apply any changes and quickly go through the results.
- To achieve the set goals, you can integrate Python with other languages.
Also, with this flexibility, the developers get to pick the programming styles with which they are comfortable or they can even merge these styles to come with effective solutions for various types of problems.
- The imperative style includes commands that detail how a computer should execute these commands. This style allows you to define the order of computations that occur such as a change of the program state.
- The functional style is also known as declarative as it declares what operations need to be executed.
- The object-oriented style is established on two concepts: class and object, where similar objects form classes. Python does not completely back up this style but developers can still implement this style to a certain limit.
- The procedural style proceeds tasks in a bit-by-bit style, hence, it happens to be very common among beginners. This style is usually used in sequencing, iteration, modularization, and selection.
With flexibility, programmers get to handle the situation better and operate in a safe environment. This reduces the chances of mistakes.
Platform Independence
Python’s adaptability adds to its features of being simple to learn and easy to use. When you use Python for machine learning development, you can run it on any operating system such as Windows, Linux, Unix, and macOS among others.
If the developer wants to shift the process from one platform to another, they have to execute many small changes and alter a few lines of code so that this code can be executed on the next platform.
Packages like PyInstaller come in handy when the developer is making ready the code to run on different platforms.
This helps developers not only save time but also money when it comes to testing on different platforms, making the complete process simpler and convenient.
Readability
As Python is simple to read and make out, Python developers do not face troubles in understanding, changing, copying, and pasting their peers’ code.
When using Python, there is no confusion, mistakes, or inconsistent paradigms. This makes the exchange of algorithms, tools, and ideas between AI and ML professionals productive.
Tools like IPython, an interactive shell, offer additional features like debugging, testing, and tab-completion among others, and eases the work process.
That’s why the Python Machine Learning combo is going to be the future of programming.
Good Visualization Options
As mentioned in the earlier parts of this article, Python comes with various libraries and few of them happen to be great visualisation tools.
For developers, the right representation of data in a creative and understandable format happens to be important in artificial intelligence, deep learning, and machine learning.
Libraries like Matplotib helps data scientists make charts, histograms, and plots for effective presentation, visualization, and data understanding.
With various application programming interfaces, the process of making clear reports and visualization also becomes simpler.
Community Support
Having strong community support with any programming language is a win-win situation for developers and programmers.
As Python is an open-source language, programmers at all levels get to access many resources. Also, the language is free and comes with numerous useful libraries and tools.
Significant Python documentation is accessible online and also in Python communities and forums.
Here programmers and machine learning developers have conversations about errors, how to solve problems, and they help each other out.
This kind of approach helps you to stay away from Common Mistakes By Python Programmers.
Growing Popularity
As Python offers many advantages to developers and programmers, it gets more in demand. It becomes easy to find developers proficient in Python and get quality work output from them. So, you should use Python For Machine Learning & AI.
Less Coding
Applying AI includes a massive amount of algorithms. With Python, you get the support from pre-defined packages, so you do not have to code algorithms.
To make this further simpler, Python offers a ‘check your code’ methodology with which you are relieved from the responsibility of testing the code.
Ease of Learning
Python is effortless and so is its syntax. You can use it to apply simple computations such as the sum of two strings to complicated processes such as building a Machine Learning model.
That’s why Artificial Intelligence with Python is a great choice.
Simple and Consistent
Python has a straightforward and readable code. Although AI and ML supports complex algorithms and multipurpose workflows, Python’s ease makes the developers create reliable systems.
Instead of spending effort and time working on the technicalities of the language, developers can put their energy into finding a solution to an ML problem.
Another reason that attracts developers to Python is its simplicity and ease of learning. Written in simple code, Python is easier to create models for Machine Learning.
For some programmers, Python’s higher intuitiveness than other programming languages happens to be a great advantage.
For others, the various python web frameworks, libraries, and extensions that simplify the application of different functionalities is a plus point.
Python also happens to be more apt for collaborative efforts when various developers are part of a project.
Python is a general-purpose language, it can perform many complicated Machine Learning tasks. Developers can quickly create prototypes and test their products for Machine Learning purposes.
Popularity
The many lucrative perks of Python makes it a very popular choice globally. Many beginners in programming are becoming inclined towards learning the language. Also, the smooth learning curve makes it more popular.
You can see Python’s popularity in the CVs of many developers. This makes the hiring process of a Python developer simpler than a multiprogramming developer.
The strong and active community, multiple libraries with improving source code make AI in Python one of the most trending languages.
Versatility
Python has been in business for more than two decades and with time it has become more versatile. It has the potential for implementation in operations, software development, and working and handling cloud infrastructures among others.
To manage large volumes of data, it also works as a data handling backend tool with Spark support.
The versatility of this language makes it support object-oriented programming, structured programming, and functional programming patterns among others.
Along with its common application in Machine Learning and data science, you can also use it in frameworks, development, gaming, and networking among others.
Speed of Execution
Python is readable and one can also execute its formulas in shorter durations. Machine Learning, especially Deep Learning, a subset of Machine Learning using Deep Neural Nets is popular for its long model training sessions. These sessions can last from hours to days.
This is the answer to the question – Why Is Python Used for Machine Learning? It’s due to execution speed that matters the most in today’s time.
Gentle Learning Curve
In AI applications, developers handle a lot of data that has to be processed effectively. As Python is a low entry barrier, data scientists can easily understand the language and start applying it in development. This saves their time in understanding the technicalities of the language first.
The gentle learning curve also implies that substituting or looking for a new member on the team will be quicker and simpler. Employers can easily find somebody, who has recommendable skills in AI, from the large Python community.
So, Python and AI become a great combination, without a pinch of a doubt.!
Python Use Cases For AI & ML
Many industries use Python to implement AI and ML in their products and services. Take a look:
Travel
The well-known travel company. Skyscanner predicted the behavior of new airplane routes by using a Python unsupervised ML algorithm.
To define the demand of passengers, they equated thousands of origins and destinations and assessed every origin and destination with 30 different criteria.
On a dashboard, they showed the results, where you could opt for any origin city to check the groups of destinations numbered from 0 to 9 and their characteristics.
When AI applied in this manner in the travelling industry, companies experience many benefits. They can make helpful destination suggestions to the users, create a strategic marketing budget, and also set a starting price for new routes.
Fintech
When AI applied in financial services, it can find solutions to problems related to risk management, personalised banking, automation, fraud prevention, and other tools that can offer premium financial services to the users.
Industry experts predict that AI in fintech is capable of decreasing costs by 22% by 2030 and it could result in a grand $1 trillion.
Few thriving online banking software companies built on Python include Venmo, Affirm, and Robinhood.
Apart from letting users make and manage their purchases and payments, these companies created a social network inside their software to ensure user connectivity.
In cryptocurrency, Python has been applied to create solutions like Anaconda that effectively analyses the market, visualises data, and makes predictions.
Transportation
Using Python, Uber created an ML platform called Michelangelo PyML. One can use it online and offline predictions that solve daily tasks.
They had earlier also created a Michelangelo product that scaled but it lacked flexibility. Michelangelo PyML is its extension.
Now, users are able to validate models with PyML and then duplicate them in Michelangelo for complete efficiency and expandability.
Healthcare
Predicting and scanning illnesses, detecting injuries, and assisting people in staying healthy on a daily basis with simple mobile applications is taking place with the help of AI and it is transforming the healthcare industry.
The industry is seeing many AI-based projects. Consider Fathom, it is a natural language processing system to examine electronic health records and aims to automate medical coding.
The leaders at Fathom are ex-Google, ex-Facebook, ex-Amazon, and graduates from esteemed universities such as Stanford and Harvard.
Another example is AiCure. It is a startup that ensures that the patients are timely taking the right medications. To achieve this, they use technologies like face recognition, action recognition, and pill recognition.
The application is also capable of analysing the patient’s health and understanding how the treatment is working.
Companies Which Are Using Python For AI
Here is the list of some renowned names that use Python for AI and providing A-grade services to their end consumers:
Volkswagen
The automobile industry is displacing human drivers with the technology of self-drive cars using AI algorithms. Volkswagen is the leader in this step.
JP Morgan Chase
In the financial sector, there is massive quality data on which AI works effectively. JP Morgan Chase is highly competent in finding and preventing fraud, algorithm trading, superior customer service, and customer assistance.
Apple
The well-known Siri in Apple products is a brilliant result of AI. Siri uses voice recognition tools and natural language processing when it answers what users are asking, makes suggestions while executing different actions.
Alphabet Inc’s Google DeepMind created AlphaGo which is a computer program that plays board game Go. It defeated the world champion Ke Jie in the first game of a best of three-match series that took place in China.
Using AI for years, Facebook is becoming an expert at it. They use it in the Facebook app, maps, getting hold of fake accounts, and inappropriate content among others.
Microsoft
Using AI, Microsoft has created its assistant called Cortana. Windows 10 has that facility.
Microsoft Azure and Office 365 and is apt to use in the form of a mobile application with Android and iOS.
It assists the users in finding content present in the computer and focuses on offering a personalised experience. It also focuses on C, C++, and Java.
Read also: A Detailed Guide To Find and Hire Python Developers (Skills, Mistakes, Cost & Salary)
Python For Machine Learning: Useful Open-Source Projects
When companies use Python for AI development, they usually share their accomplishments in the community. This is mostly possible because Python is open-source.
If you are planning to use Python for AI project, you can go through these handy open-source projects to start the project:
OpenCog Foundation
OpenCog, founded in 2011, is “building better minds together” by investing time and energy into developing Artificial General Intelligence (AGI) with human capacities.
Now, SingularityNET project is using it along with Hanson Robotics to deliver intelligence to Sofia and other robots. That’s Why Is Python Used for AI and ML.
Institute for Artificial Intelligence
The Institute of Artificial Intelligence is a part of the Faculty of Computer Science at the University of Bremen.
It runs research on AI and conducts various workshops and events that aimed at advancing AI technologies, involving more youth in this space and training them, and also supporting the active AI companies and projects.
Zulip
Zulip can process thousands of real-time messages in a day. Many large and open-source projects, and Fortune 500 companies uses it. To its users, it provides clear organisation, asynchronous communication, and many benefits to the teams.
Magenta
Magenta is a Python library and research project that aims to use AI to make music and art. It operates on image, songs, drawings creation and helps artists in finding new ways of creating.
MailPile
MailPile is a creative email client that aims to provide private and secure communication. It is quick, displays no ads, and is available with a robust search function along with privacy and encryption.
Conclusion
AI and ML are revamping some of the biggest industries. With its predictions and machine learning tasks, there is an implementation taking place to make companies more effective and productive.
These technologies are helping scientists find intelligent solutions for real-life issues that will add more simplicity to end-users’ life and a personalised experience also.
We hope you had a great experience reading this article and it proves to be of great benefit for any Python Developer in the near future. Thank You.!
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