Eight advantages of python to help you in machine learning!

Source: Artificial Intelligence on Medium

Eight advantages of python to help you in machine learning!

Artificial intelligence and machine learning are emerging hot areas in the IT industry. Despite increasing discussions about its development security, developers are continuing to expand the capabilities and storage capacity of artificial intelligence. Today, artificial intelligence has gone far beyond the ideas in science fiction and has become a reality. Artificial intelligence technology is widely used to process and analyse large amounts of data. Due to the significantly increased workload and work intensity, these tasks will not require manual operations in the future.

For example, artificial intelligence is used in analytics to build predictions that help people create powerful strategies and more effective solutions. Fintech companies apply artificial intelligence to investment platforms to conduct market research and predict how to maximize investment returns. Tourism uses artificial intelligence to send personalized suggestions, or develop chatbots to optimize the overall user experience. These examples show that the use of artificial intelligence and machine learning to process large amounts of data will provide users with a more personalized, more accurate and superior experience.

How artificial intelligence and machine learning are building future technologies

With the increase in data volume and data complexity, artificial intelligence and machine learning are applied to data processing and analysis. To be fair, the human brain can analyse large amounts of data, but this ability is limited by the amount of data that can be accommodated at any time. However, artificial intelligence is not limited by this. It can provide more accurate forecasts and perspectives, improve business efficiency, productivity, and reduce production costs. For this reason, it is not surprising that many industries have begun to apply artificial intelligence and machine learning to improve product performance and promote product development.

Deloitte research shows that in the technological transformation for the purpose of increasing productivity, the use of artificial intelligence technology to help companies develop into the latest trend. Their prediction also proves that in the next 24 months, more companies will use artificial intelligence in their products and production processes to achieve higher efficiency and achieve strategic goals. In short, artificial intelligence can help companies do their jobs better.

Five advantages of artificial intelligence

Increase current productivity (44%)

· Reasonable planning of internal operations (42%)

· Better decision making (35%)

· Reasonable planning of external operations (31%)

· Freelancers are more creative (31%)

Given the advantages of using artificial intelligence listed above, more and more companies are eager to try it. However, artificial intelligence is mutual-it can help people optimize the analysis process, but it requires difficult development. Due to the need to analyze large amounts of data, artificial intelligence products must efficiently handle high-load work processes in a short period of time. To ensure proper work, you must choose the appropriate language for development. Programming languages ​​require simple syntax, the ability to handle complex processes, and support at all times.

Python is the best programming language for artificial intelligence and machine learning

As artificial intelligence and machine learning are gradually applied to various channels and industries, large companies have invested in these areas, and the demand for machine learning and artificial intelligence experts has increased accordingly. According to Jean Francois Puget of IBM’s machine learning department, Python is the most popular language for artificial intelligence and machine learning, and this conclusion is based on the trend search results of Indeed.com.

According to FrancoisPuget’s diagram, Python is the main programming language for artificial intelligence and machine learning.

After investigating the advantages of Python, I found the following reasons for using Python to put artificial intelligence and machine learning projects into practice.

1. Strong software library ecosystem

Having a large selection of software libraries is one of the main reasons why Python has become the most popular programming language for artificial intelligence. The software library consists of modules or groups of modules released by different sources such as PyPi, which includes pre-written code snippets that allow users to access certain functions or perform different operations. Python libraries provide basic-level projects, so developers don’t have to code from scratch every time.

Machine learning requires continuous data processing, and Python libraries allow data to be accessed, processed, and transformed. Here are the most widely used software libraries in machine learning and artificial intelligence:

· Scikit-learn is suitable for processing basic machine learning algorithms such as clustering, linear and logistic regression, regression and classification.

· Pandas is suitable for advanced data structure and analysis, allowing data to be merged and filtered, and data collected from other external sources such as Excel.

Keras is suitable for deep learning, enabling fast calculations and prototyping. Because the software library uses a GPU in addition to the computer’s CPU.

TensorFlow is suitable for deep learning by setting up, training, and utilizing artificial neural networks with large data sets.

· Matplotlib is suitable for creating 2D plots, histograms, charts, and other forms of visualization operations.

NLTK is suitable for computational linguistics and natural language recognition and processing.

Scikit-image is suitable for image processing.

· PyBrain is suitable for neural networks, unsupervised learning and reinforcement learning.

Caffe is suitable for deep learning, can switch between CPU and GPU, and processes more than 600,000 images per day by using a single NVIDIAK40 GPU.

· StatsModels are suitable for statistical algorithms and data exploration.

In the PyPI repository, you can explore and compare more Python libraries.

2. Low barriers to entry

Working in the fields of machine learning and artificial intelligence means that you need to easily and efficiently process large amounts of data. The lower barriers to entry allow more data scientists to quickly master Python and develop artificial intelligence without learning too much about the language.

The Python programming language is very similar to everyday English, which makes the learning process easier. Its simple syntax makes it easy to work with complex systems and ensures a clear relationship between system elements.

For example, this code is written to figure out if the input number is prime.

code show as below:

test_number = 407 # our example is not a prime number

# prime numbers are greater than 1

if test_number> 1:

# check for factors

number_list = range (2, test_number)

for number in number_list:

number_of_parts = test_number // number

print (f “{test_number} is not a prime number”)

print (f “{number} times {number_of_parts} is {test_number}”)



print (f “{test_number} is a prime number”)


print (f “{test_number} is not a prime number”)

As the last line shows, the code results indicate that the number tested is not a prime number. To put it plainly, English speakers can easily understand the meaning of the code, because all simple English words are used.

In addition, there is a lot of documentation available, and the Python community can help and give advice over the long term.

3. Flexibility

For machine learning, the Python language is flexible and a great choice:

Provide options for OOP or script.

· No need to recompile the source code, developers can make any changes and see the results immediately.

Programmers can combine Python with other languages ​​to achieve their goals.

In addition, flexibility allows developers to choose programming styles that they are particularly familiar with, and even combine different programming styles to solve different types of problems in the most efficient way.

· Command style of command describes how a computer should perform these instructions components. Using this style, you can customize the order of calculations when the program state changes.

· Functional style , also known as declarative style, because it will declare the operation should be performed. In contrast to the imperative style, this style does not consider program states and declares declarations in the form of mathematical equations.

Object — oriented style is based on two concepts: classes and objects, and similar objects form classes. Python does not fully support this style because it cannot fully perform encapsulation, but developers can still use this style to a certain extent.

· Procedural style is most commonly used for beginners, because of its mission in a stepwise format, so commonly used in sorting, iteration, modularity and options.

The factor of flexibility reduces the possibility of making mistakes, because programmers can control any situation and work in a comfortable environment.

4. Platform independence

Python is not only easy to use, but also easy to learn and versatile. Python for machine learning development can run on any platform, including Windows, MacOS, Linux, Unix, and 21 other platforms. Moving a process from one platform to another requires developers to make a few small changes and modify a few lines of code to create an executable form of code for the chosen platform. Developers can use packages like PyInstaller to prepare code to run on different platforms.

Again, this saves time and money for testing on various platforms, and makes the whole process easier and more convenient.

5. Readability

Python is very readable, so every Python developer can understand peer code and make changes, copy or share. Because there are no paradigms that can cause confusion, error, or conflict, the exchange of algorithms, ideas, and tools between artificial intelligence and machine learning professionals is more effective.

Tools such as IPython can also be used. It is an interactive command interpreter that provides additional functions such as testing, debugging, and tab completion to speed up the process.

6. Good visualisation options

As mentioned above, Python provides a variety of libraries, some of which are good visualisation tools. However, for artificial intelligence developers, the importance of these tools in artificial intelligence, deep learning, and machine learning is important, and more importantly, the ability to represent data in human-readable formats.

Data scientists can use databases like Matplotlib to build charts, histograms, and floor plans for better data understanding, efficient expression, and visualisation. Different application programming interfaces also simplify the visualisation process and make it easier to create clear reports.

7. Community support

The strong community support built around programming languages ​​is very useful. Python is an open source language, which means that for programmers, whether they are beginners or professionals, there are plenty of open resources available.

Many Python documents are available online or in the Python community and forums. Both programmers and machine learning developers can discuss errors, solve problems, and help each other in communities and forums.

The Python programming language is completely free and has a variety of useful libraries and tools.

8. Rising popularity

Given the advantages mentioned above, Python is becoming more and more popular among data scientists. Stack Over flow surveys show that the popularity of Python is expected to continue to grow until at least 2020.

This means that developers can more easily search and replace team members if necessary. Moreover, the cost of working with Python can be as high as using a less popular programming language.

Python examples for artificial intelligence and machine learning

Python provides many features for artificial intelligence and machine learning, which makes it the best language in the field. No wonder industries use Python for prediction and other machine learning tasks.

Take a closer look at examples in the following areas:

· Travel

For example, tourism giant Sky scanner uses Python’s unsupervised machine learning algorithms to predict new routes. Compare thousands of departures and destinations and use 30 different criteria to evaluate each departure and destination to determine passenger needs. The results are displayed on the dashboard, where you can choose your departure city at will, and view destination groups numbered 0 to 9 and their characteristics.

Such an example of applying artificial intelligence in the tourism industry is very helpful in recommending destinations to users, assisting in creating marketing budgets, and setting initial prices for new routes.

· Fintech

The application of artificial intelligence in financial services helps solve problems such as risk management, fraud prevention, personalized banking, and automation, as well as tools to provide users with high-quality financial services. It is predicted that by 2030, due to the application of artificial intelligence in the field of fintech, operating costs can be reduced by 22%, saving US $ 1 trillion.

Among the online banking software built using Python, Venmo, Affirm, and Robinhood are all successful examples. Not only do they allow users to make payments and purchases, they also support the creation of social networks within the software so people can stay connected.

On the cryptocurrency side, Python is used to build platforms like Anaconda to effectively analyze markets, make predictions, and visualize data.

· Transportation

Uber has developed a machine learning platform MichelangeloPyML using Python. Uber uses it for online and offline forecasting, solving everyday tasks. MichelangeloPyML is an upgraded version of Michelangelo’s original product, which is extensible but not flexible enough. Currently, users can use PyML to validate models and then replicate them in Michelangelo for best efficiency and scalability.

· Health care

Artificial intelligence is reshaping the healthcare industry to help predict and scan for diseases, check wounds, and help people stay healthy with easy-to-use mobile apps.

There are many excellent AI-based projects in the industry. For example, Fathom is a natural language processing system for analyzing electronic health records. Its mission is to “automate medical coding.” The company’s leaders are mostly from Google, Amazon, Facebook, Stanford University, and Harvard University.

AiCure is a startup that is committed to ensuring that patients are taking the right medicines at the right time. To this end, the company uses technologies such as face recognition, pill recognition and motion recognition. This application can also analyze the patient’s status and see if the treatment is effective. By using the Interactive Medical Assistant IMA, clinically important data can be collected and then analyzed using software.

The increasing popularity of Python has greatly increased the demand for Python programmers in the data science community. Choosing a language with high demand is a wise choice because it will include more features in the future.

Python for machine learning: useful open source projects

The open source nature of Python makes it possible for all artificial intelligence development companies to share results within the community. If you have decided to learn Python, or want to apply this language to artificial intelligence projects, the following list of open source projects will be helpful:

OpenCog Foundation

OpenCog creates artificial intelligence (AGI) with human capabilities by “gathering more talent.” The foundation was established in 2011 and is currently used for the SingularityNET project, as well as providing intelligent services for Sophia and other robots at Hanson Robotics.

· Institute of Artificial Intelligence

The Institute of Artificial Intelligence is a branch of the Department of Computer Science at the University of Bremen. The institute conducts artificial intelligence research, holds various seminars and events, helps promote the development of artificial intelligence technology, allows more young people to join the field and train them, and supports existing artificial intelligence projects and related companies.

· Zulip

Zulip is “the world’s most productive team chat software” and can process thousands of real-time messages every day. Fortune 500 companies and other large open source projects use Zulip for its clear organization, asynchronous communication, and other great benefits to the team.

· Magenta

Magenta is a Python database and a research project whose biggest goal is to use artificial intelligence to create music and art. It is suitable for the generation of images, songs and drawings, and helps artists explore new creative forms.


MailPile is an innovative email client focused on secure and private communications. This project attempts to answer the question “How to protect the privacy of the Internet?” The software is fast, ad-free, and has powerful search, privacy, and encryption.

to sum up

As a rapidly developing universal technology, artificial intelligence and machine learning can help scientists solve real-life dilemmas and propose clever solutions. The reason why Python is considered the most suitable programming language for artificial intelligence is that it has the following advantages:

1. Provide a rich software library ecosystem

2. Low barriers to entry

3. Extreme flexibility

4. No need to rely on any platform

5. Easy to read

6. Provides a large number of visualization options

7. Strong community support

8. Increasing popularity among scientists, professors, and large companies

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