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Rescue workers tend to the crash victims with the culprit python lying motionless nearby.

Three people were injured when a motorcyclist swerved to avoid hitting a python in the road in Khao Maikaew.

Lamfong Jantawongsri, 49, suffered a broken leg and arm when she lost control while trying to avoid the big snake slithering across Highway 331.

The woman crashed her Honda Wave into another Wave carrying Sangkom Jankesorn, 28, and Rayrai Bukaew, 33 who were driving against traffic on the wrong side of the road. Sangkom suffered broken ribs while the pillion passenger suffered cuts and a possible concussion.

Apparently the snake was run over too as it lay lifeless next to the crashed motorbikes and injured people.

Apparently the snake was run over too as it lay lifeless at the scene of the crash.

The 22nd Edition of the State of the Developer Nation report, which contains trends from a survey conducted in Q1 2022, ranks Python as the second most widely used and adopted programming language, after JavaScript, with 15.7 million users. The figure represents an increase of 4.4 million users from 11.3 million in Q3 2021, the most significant rise of the various languages sampled in the report. This growth is attributed to the rise of data science (DS) and machine learning (ML), as over 70% of DS and ML developers report using Python. In addition to DS and ML, Python is utilized in other use cases that we have detailed herein.

What is Python

First released in the early 1990s, Python is a high-level, general-purpose programming language famed for its easy readability. This language has been used to create popular websites and products, including Instagram’s backend, Google, Spotify, Netflix, and more. But, of course, the language is not only the preserve of products with a user base that goes beyond several hundred million users; it can be deployed to create simple tools.

For instance, you can use Python and the web scraping libraries, such as Python Requests library and Beautiful Soup, to create an in-house web scraping tool. And this is just one example of the myriad uses of Python.

Features of Python Programming Language

Python has the following features:

1. Easy to read and code

Python uses a syntax that has been likened to the English language. This characteristic makes it easy to read and code. In fact, you can easily tell what function the code is designed to perform by simply glancing at the lines of code. Little wonder then that Python is the third most popular language among learners. On the other hand, C++ is the most difficult language to learn.

2. Easy to debug

Python makes it easy to identify and correct errors and mistakes, particularly once you can understand how to interpret the language’s error traces.

3. Open source and free

The Python programming language is freely available for use and download from the official website.

4. Object-oriented language

As an object-oriented programming language, Python uses the concept of objects, which contain fields (data) and behavior/procedures that can modify the data to represent elements of a problem.

5. High-level language

Python is a high-level language. This means it is not tied to a system architecture such as x86 or ARM. As such, you can write Python code as well as run it on any device or computer.

6. Extensive developer community

Python has an extensive developer community with more than 15 million users globally. This community underlines the fact that the language is mature. These users have likely contributed to forums or written resources to help learners understand the language or debug their code. In contrast, Go or Golang, which was publicly released in 2012, is still maturing and does not have an extensive developer community.

7. Large standard library

By some accounts, there are over 137,000 Python libraries. A standard library contains functions, constants, multiple language definitions, and commonly used commands that are designed to serve a specific function. For instance, the Python requests library contains commands and functions that send HTTP/HTTPS requests.

8. Support for multiprocessing

Python has a multiprocessing package that, when included in a code and executed, prompts the system to run multiple processes in parallel. Multiprocessing speeds up processes by utilizing multiple processor cores or threads.

9. Extensible and integrated language

You can write Python code in C or C++ language. In fact, you can use SWIG to build a natural Python interface for your C or C++ code. In addition, you can easily integrate Python with these two languages.

10. Graphical User Interface (GUI) support

Python modules such as Tk in Python, wxPython, PyQt5, and PyQt4 enable you to create GUI-based apps with Python.

11. Portability

You can run Python code on any platform or operating system. For instance, if you had written a script on a Windows computer, you can run it on computers that use other operating systems like macOS and Linux.

Uses of Python

Python is used in the following instances:

1.It is used in web development to create server-side web applications (backend)

2. Python connects database systems and can automatically read and modify files

3. It can be used for production-ready software development and deployment as well as rapid prototyping (system scripting)

4. This programming language can be used to a create machine learning and artificial intelligence solutions

5. It can be used to handle big data and execute complex mathematical calculations

6. Python can be used to create web scrapers

Conclusion

If you are contemplating including Python in your tech stack, it is important to consider whether the features appeal to your needs. Also, it is essential to consider the tools you want to create with Python (look at the use case).

Click here for more information

The 22nd Edition of the State of the Developer Nation report, which contains trends from a survey conducted in Q1 2022, ranks Python as the second most widely used and adopted programming language, after JavaScript, with 15.7 million users. The figure represents an increase of 4.4 million users from 11.3 million in Q3 2021, the most significant rise of the various languages sampled in the report. This growth is attributed to the rise of data science (DS) and machine learning (ML), as over 70% of DS and ML developers report using Python. In addition to DS and ML, Python is utilized in other use cases that we have detailed herein.

What is Python

First released in the early 1990s, Python is a high-level, general-purpose programming language famed for its easy readability. This language has been used to create popular websites and products, including Instagram’s backend, Google, Spotify, Netflix, and more. But, of course, the language is not only the preserve of products with a user base that goes beyond several hundred million users; it can be deployed to create simple tools.

For instance, you can use Python and the web scraping libraries, such as Python Requests library and Beautiful Soup, to create an in-house web scraping tool. And this is just one example of the myriad uses of Python.

Features of Python Programming Language

Python has the following features:

1. Easy to read and code

Python uses a syntax that has been likened to the English language. This characteristic makes it easy to read and code. In fact, you can easily tell what function the code is designed to perform by simply glancing at the lines of code. Little wonder then that Python is the third most popular language among learners. On the other hand, C++ is the most difficult language to learn.

2. Easy to debug

Python makes it easy to identify and correct errors and mistakes, particularly once you can understand how to interpret the language’s error traces.

3. Open source and free

The Python programming language is freely available for use and download from the official website.

4. Object-oriented language

As an object-oriented programming language, Python uses the concept of objects, which contain fields (data) and behavior/procedures that can modify the data to represent elements of a problem.

5. High-level language

Python is a high-level language. This means it is not tied to a system architecture such as x86 or ARM. As such, you can write Python code as well as run it on any device or computer.

6. Extensive developer community

Python has an extensive developer community with more than 15 million users globally. This community underlines the fact that the language is mature. These users have likely contributed to forums or written resources to help learners understand the language or debug their code. In contrast, Go or Golang, which was publicly released in 2012, is still maturing and does not have an extensive developer community.

7. Large standard library

By some accounts, there are over 137,000 Python libraries. A standard library contains functions, constants, multiple language definitions, and commonly used commands that are designed to serve a specific function. For instance, the Python requests library contains commands and functions that send HTTP/HTTPS requests.

8. Support for multiprocessing

Python has a multiprocessing package that, when included in a code and executed, prompts the system to run multiple processes in parallel. Multiprocessing speeds up processes by utilizing multiple processor cores or threads.

9. Extensible and integrated language

You can write Python code in C or C++ language. In fact, you can use SWIG to build a natural Python interface for your C or C++ code. In addition, you can easily integrate Python with these two languages.

10. Graphical User Interface (GUI) support

Python modules such as Tk in Python, wxPython, PyQt5, and PyQt4 enable you to create GUI-based apps with Python.

11. Portability

You can run Python code on any platform or operating system. For instance, if you had written a script on a Windows computer, you can run it on computers that use other operating systems like macOS and Linux.

Uses of Python

Python is used in the following instances:

1.It is used in web development to create server-side web applications (backend)

2. Python connects database systems and can automatically read and modify files

3. It can be used for production-ready software development and deployment as well as rapid prototyping (system scripting)

4. This programming language can be used to a create machine learning and artificial intelligence solutions

5. It can be used to handle big data and execute complex mathematical calculations

6. Python can be used to create web scrapers

Conclusion

If you are contemplating including Python in your tech stack, it is important to consider whether the features appeal to your needs. Also, it is essential to consider the tools you want to create with Python (look at the use case).

Click here for more information

The 22nd Edition of the State of the Developer Nation report, which contains trends from a survey conducted in Q1 2022, ranks Python as the second most widely used and adopted programming language, after JavaScript, with 15.7 million users. The figure represents an increase of 4.4 million users from 11.3 million in Q3 2021, the most significant rise of the various languages sampled in the report. This growth is attributed to the rise of data science (DS) and machine learning (ML), as over 70% of DS and ML developers report using Python. In addition to DS and ML, Python is utilized in other use cases that we have detailed herein.

What is Python

First released in the early 1990s, Python is a high-level, general-purpose programming language famed for its easy readability. This language has been used to create popular websites and products, including Instagram’s backend, Google, Spotify, Netflix, and more. But, of course, the language is not only the preserve of products with a user base that goes beyond several hundred million users; it can be deployed to create simple tools.

For instance, you can use Python and the web scraping libraries, such as Python Requests library and Beautiful Soup, to create an in-house web scraping tool. And this is just one example of the myriad uses of Python.

Features of Python Programming Language

Python has the following features:

1. Easy to read and code

Python uses a syntax that has been likened to the English language. This characteristic makes it easy to read and code. In fact, you can easily tell what function the code is designed to perform by simply glancing at the lines of code. Little wonder then that Python is the third most popular language among learners. On the other hand, C++ is the most difficult language to learn.

2. Easy to debug

Python makes it easy to identify and correct errors and mistakes, particularly once you can understand how to interpret the language’s error traces.

3. Open source and free

The Python programming language is freely available for use and download from the official website.

4. Object-oriented language

As an object-oriented programming language, Python uses the concept of objects, which contain fields (data) and behavior/procedures that can modify the data to represent elements of a problem.

5. High-level language

Python is a high-level language. This means it is not tied to a system architecture such as x86 or ARM. As such, you can write Python code as well as run it on any device or computer.

6. Extensive developer community

Python has an extensive developer community with more than 15 million users globally. This community underlines the fact that the language is mature. These users have likely contributed to forums or written resources to help learners understand the language or debug their code. In contrast, Go or Golang, which was publicly released in 2012, is still maturing and does not have an extensive developer community.

7. Large standard library

By some accounts, there are over 137,000 Python libraries. A standard library contains functions, constants, multiple language definitions, and commonly used commands that are designed to serve a specific function. For instance, the Python requests library contains commands and functions that send HTTP/HTTPS requests.

8. Support for multiprocessing

Python has a multiprocessing package that, when included in a code and executed, prompts the system to run multiple processes in parallel. Multiprocessing speeds up processes by utilizing multiple processor cores or threads.

9. Extensible and integrated language

You can write Python code in C or C++ language. In fact, you can use SWIG to build a natural Python interface for your C or C++ code. In addition, you can easily integrate Python with these two languages.

10. Graphical User Interface (GUI) support

Python modules such as Tk in Python, wxPython, PyQt5, and PyQt4 enable you to create GUI-based apps with Python.

11. Portability

You can run Python code on any platform or operating system. For instance, if you had written a script on a Windows computer, you can run it on computers that use other operating systems like macOS and Linux.

Uses of Python

Python is used in the following instances:

1.It is used in web development to create server-side web applications (backend)

2. Python connects database systems and can automatically read and modify files

3. It can be used for production-ready software development and deployment as well as rapid prototyping (system scripting)

4. This programming language can be used to a create machine learning and artificial intelligence solutions

5. It can be used to handle big data and execute complex mathematical calculations

6. Python can be used to create web scrapers

Conclusion

If you are contemplating including Python in your tech stack, it is important to consider whether the features appeal to your needs. Also, it is essential to consider the tools you want to create with Python (look at the use case).

Click here for more information

This content is made possible by our sponsor; it is not written by and does not necessarily reflect the views of Engadget’s editorial staff.

When choosing your first programming language, one thing you should consider is the type of projects you want to work on, whether that’s web development or automation. But if you’re not sure which to specialize in, picking up a versatile language like Python can give you flexibility when exploring the fields you enjoy most.

However, learning a coding language can be challenging without a structured study plan. The 2023 Premium Python Programming Mega Certification Bundle has 14 courses led by experienced developers that can teach you beginner to advanced skills, and it’s on sale for $40. These teachers include Ardit Sulce, the founder of PythonHow, and best-selling Udemy instructor Joseph Delgadillo.

If you have zero coding experience, Python in a Weekend is an excellent place to start. It’s designed for beginners and can introduce you to the language’s fundamentals, such as variables, strings, loops and functions within four hours. It also includes three practice projects: a pickalator, a word counter and a Twitter bot.

For a more in-depth look, you can take the Python Mega Course, which offers 25 hours of training on how to build production code for real-world applications. These lessons cover concepts such as list comprehension with if and if-else conditional statements, keyword and non-keyword arguments and standard Python and third-party modules.

Once you’ve developed a solid understanding of Python, you can explore different ways to use it. For example, prospective game developers can learn how to create interactive elements in Create an Escape Room With Python. But if you’re interested in machine learning, you might prefer Python and Android TensorFlow Lite, where you’ll discover how to deploy classification and regression models.

Course list:

• Learn to Code with Python 3

• Build a Medical Diagnosis Bot with Python

• Python Programming for Beginners+Python Bootcamp In a Day

• Tkinter Python and Python GUI With Tkinter Desktop Application

• Python Programming: Machine Learning, Deep Learning

• NumPy Python Programming Language Library From Scratch A-Z

• Machine Learning Python With Theoretically for Data Science

• Python in a Weekend: The Easiest Python for Beginners Course

• Python and Android TensorFlow Lite — Machine Learning for App Development

• Django Crash Course with Examples

• Data Visualization on the Browser With Python and Bokeh

• The Python Mega Course: Build 10 Real World Applications

• The Complete Python Hacking Course: Beginner to Advanced

• Create an Escape Room With Python

Learning Python is an excellent way to sharpen your OOP, scripting and app development skills. The 2023 Premium Python Programming Mega Certification Bundle offers training in these areas and more, and you can purchase it today for $40, or under $3 per course.

Prices subject to change. Engadget is teaming up with StackSocial to bring you deals on the latest headphones, gadgets, tech toys and tutorials. This post does not constitute editorial endorsement, and we earn a portion of all sales. If you have any questions about the products you see here or previous purchases, please contact StackSocial support here.

Python is a general-purpose, object-oriented programming language that has several implications across the software, web development, data science and automation environments. The language’s dynamic semantics, high-level built in data structures, dynamic typing and dynamic binding make it one of the most useful languages for rapid application development.

What Is Python Used For?

Python is most commonly used in website development, software development, task automation, data analysis and data visualization.

Python is an incredibly versatile programming language. Often used to develop an application or website’s back end, Python is capable of processing data, communicating with databases, routing URLs and ensuring overall security. 

Python also offers the ability to easily automate processes through scripting, making it key for software testing, troubleshooting and bug tracking.

Finally, Python plays a key role in data science tasks and is used to perform complex statistical calculations, visualize data and create machine learning algorithms.

Python for Beginners – Learn Python in 1 Hour. | Video: Programming With Mosh

More on Python From Built In Experts4 Python Tools to Simplify Your Life

Why Is Python so Powerful?

Python’s power comes in its versatility. The language is easy to use and can be implemented across a variety of technical processes, such as programming, machine learning, data science, automation and more.

While other languages may be capable of handling tasks in all of these environments, Python stands out due to its ability to excel in each. Python is also an interpreted language, meaning that it executes instructions directly without the need for compilation.

Additionally, there are a variety of Python frameworks and libraries, such as Django and Flask, that add additional power and tailored capabilities to specific processes. 

Is Python Easy to Learn?

Python is one of the easiest programming languages to learn, largely due to its object-oriented nature and simple syntax.

Related Reading on Built In5 Ways to Write More Pythonic Code

Once again, Python is at the top of the IEEE’s annual survey of popular programming languages – seemingly decided by a grab bag of metrics – while SQL appears to be a crucial skill.

When it comes to popular languages, the survey found Python topped the list, followed by three flavors of C (original, ++ and #), Java, SQL, and JavaScript. Beyond those seven, and popularity drops quickly: R, the next most popular programming language, more than half as popular as its closest rival.

As we noted last year, Python’s presence atop the list is atypical, as JavaScript regularly leads lists of popular languages from other sources, such as Stack Overflow, whose 2021 and 2022 Developer Survey reports both have JavaScript in pole position, followed by HTML/CSS, SQL, Python and Typescript. 

In 2021, IEEE Spectrum allowed users to apply their own weightings to the report to see different results, but decided not to include such a feature this year. Spectrum said it made the choice because few people were using it, and that the “giant ball of floating-point math” in browsers messed up the figures.

SQL the unsexy star

Loving a language is all well and good but a programmer needs to put food next to the keyboard, and when it comes to getting a job, SQL climbs to the top of the list, followed by Java, Python, JavaScript, the C’s, HTML and TypeScript. That is to say, SQL is a leading desirable skill, at least according to job ads. It’s worth noting that the IEEE said it just recently began considering TypeScript separately from JavaScript; TypeScript doesn’t appear in the 2021 survey.

According to last year’s list, SQL was ranked at number 10: so why the sudden jump to the top of desirable skills?

Having looked through hundreds of job listings in the course of compiling the list, said IEEE special projects editor and report author Stephen Cass, “I can say that the strength of the SQL signal is not because there are a lot of employers looking for just SQL coders … They want a given language plus SQL.” 

Cass described modern apps as often consisting of a front and middleware layer talking to a database “often over a network to eliminate local resource constraints.” With a variety of SQL implementations available, Cass said, “chances are there’s probably already one that fits your use case.”

As we noted, Stack Overflow’s reports seem to agree, placing SQL in third place behind JavaScript and HTML/CSS, one spot higher than its 4th place ranking in 2021 (behind Python, which it swapped spots with). If you’re turning to lists like these to determine where to focus your professional development, the big takeaway might be to polish your SQL skills. ®

Python is a general-purpose, object-oriented programming language that has several implications across the software, web development, data science and automation environments. The language’s dynamic semantics, high-level built in data structures, dynamic typing and dynamic binding make it one of the most useful languages for rapid application development.

What Is Python Used For?

Python is most commonly used in website development, software development, task automation, data analysis and data visualization.

Python is an incredibly versatile programming language. Often used to develop an application or website’s back end, Python is capable of processing data, communicating with databases, routing URLs and ensuring overall security. 

Python also offers the ability to easily automate processes through scripting, making it key for software testing, troubleshooting and bug tracking.

Finally, Python plays a key role in data science tasks and is used to perform complex statistical calculations, visualize data and create machine learning algorithms.

Python for Beginners – Learn Python in 1 Hour. | Video: Programming With Mosh

More on Python From Built In Experts4 Python Tools to Simplify Your Life

Why Is Python so Powerful?

Python’s power comes in its versatility. The language is easy to use and can be implemented across a variety of technical processes, such as programming, machine learning, data science, automation and more.

While other languages may be capable of handling tasks in all of these environments, Python stands out due to its ability to excel in each. Python is also an interpreted language, meaning that it executes instructions directly without the need for compilation.

Additionally, there are a variety of Python frameworks and libraries, such as Django and Flask, that add additional power and tailored capabilities to specific processes. 

Is Python Easy to Learn?

Python is one of the easiest programming languages to learn, largely due to its object-oriented nature and simple syntax.

Related Reading on Built In5 Ways to Write More Pythonic Code

GPUs are crucial in delivering the computational power required for deploying AI models for large-scale pretrained models in various machine learning domains like computer vision, natural language processing, and multimodal learning. Currently, AI practitioners now have a minimal choice in the matter of choosing high-performance GPU inference solutions due to their platform-specific nature. A machine learning system created for one company’s GPU must be entirely reimplemented to run on hardware from a different technology vendor. Because of hardware dependencies in complicated runtime environments, it is challenging to maintain the code that makes up these solutions. Additionally, AI production pipelines frequently need rapid development. Although proprietary software toolkits like TensorRT offer customization options, they frequently fail to meet this demand. Further reducing development agility, the proprietary solution may make it more difficult to debug the code swiftly.

Meta AI has created AITemplate (AIT), a unified open-source inference solution with distinct acceleration back ends for AMD and NVIDIA GPU technology, to address these industry difficulties. On a range of popular AI models, including convolutional neural networks, transformers, and diffusers, it provides performance almost identical to that of hardware-native Tensor Core (NVIDIA GPU) and Matrix Core (AMD GPU) architectures. The team improved performance by up to 12x on NVIDIA GPUs when utilizing AIT and 4x on AMD GPUs when using PyTorch’s eager mode. Currently, AITemplate is enabled on NVIDIA’s A100 and AMD’s MI200 GPU systems, which are both commonly used in data centers of technology businesses, research facilities, and cloud computing service providers.

AITemplate is a Python system that converts AI models into high-performance C++ GPU template code to speed up inference. A front-end layer that performs various graph transformations to optimize the graph and a back-end layer that produces C++ kernel templates for the GPU target make up the system. The vision behind the framework is to support high speed while maintaining simplicity. The project includes several performance advances, such as enhanced kernel fusion, an optimization technique that unifies several kernels into one kernel to operate them more effectively, and advanced transformer block optimizations. These improvements dramatically increase the use of AMD’s Matrix Cores and NVIDIA’s Tensor Cores, resulting in cutting-edge performance. Additionally, AIT keeps its reliance on external libraries to a minimum.

Thanks to its support for three advanced optimizations—vertical, horizontal, and memory fusions—AITemplate boasts one of the business’s most sophisticated kernel fusion systems. Moreover, being easy to deploy makes AITemplate a viable solution. An independent, self-contained binary containing the AI model is created. This binary has good backward compatibility because it can operate in any environment with the same hardware and more recent CUDA 11 / ROCM 5 versions. Additionally, AITemplate offers commonly used pre-built models (e.g., VisionTransformer, BERT, Stable Diffusion, ResNet, and MaskRCNN). This streamlines the deployment procedure and makes it simple for professionals to deploy PyTorch pretrained models. The Python Jinja2 template and the GPU Tensor Core/Matrix Core C++ template are the two layers of template systems that make up the AITemplate. After profiling in Python, the system converts the Jinja2 template into C++ code to determine the optimum kernel setup. The model’s final binary code is created by compiling the generated source code using the GPU C++ compiler. Users can convert their models from a variety of frameworks, including PyTorch, to AITemplate because of its front-end design, which is similar to PyTorch.

In addition to increasing the number of platforms available for AI, Meta AI hopes to develop techniques that can also help solve environmental concerns by lowering carbon emissions. According to studies, the use of GPUs can influence carbon emissions. AITemplate speeds up GPU execution, which can minimize emissions even further. To summarize, AITemplate provides cutting-edge performance for present-generation and upcoming AMD and NVIDIA GPUs with minimal system complexity. Nevertheless, according to the researchers, they are merely at the start of developing a high-performance AI inference engine. They are actively trying to improve AITemplate with new optimizations and complete support for dynamic shapes. Their long-term goals include expanding AITemplate to more hardware platforms from different technology vendors. Meta aims to create an ecosystem for AI inference that is greener and more effective, with more remarkable performance, flexibility, and back-end options and developing AITemplate is a stepping stone in that direction.

This Article is written as a research summary article by Marktechpost Staff based on the research article 'Faster, more flexible inference on GPUs using AITemplate, a revolutionary new inference engine'. All Credit For This Research Goes To Researchers on This Project. Check out the code and reference article.

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Khushboo Gupta is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Goa. She is passionate about the fields of Machine Learning, Natural Language Processing and Web Development. She enjoys learning more about the technical field by participating in several challenges.

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As far as coding languages go, Python is one of the most relevant. That’s because it’s used for many different applications, from app development and data science to cybersecurity. Want to learn Python at home without investing thousands in school tuition? Then the 2023 Premium Python Programming Mega Certification Bundle, offered to readers for just $40, is a great alternative.

This convenient web-based training package is one of the easiest ways for beginners to learn how to code with Python. It features fourteen courses that introduce students to the basics of the language and gradually take them through its varied applications. By the end, students can expect to have an advanced understanding of the platform, so they’ll be able to start developing their own apps from scratch.

What sets this bundle apart from traditional learning opportunities is that it’s so casual. There are no physical class sessions to attend, so you can log in and learn at your convenience. And since you’ll enjoy lifetime access, you can take as long as you want to complete the whole thing. That also means there are no deadlines to contend with, which is ideal if you don’t thrive in a traditional classroom setting.

Even better, these courses are all facilitated by Python coding experts — such as Joseph Delgadillo and Paul Ashun — and each comes highly recommended by past students. Every instructor over the 14 included courses has received a perfect rating of 5 out of 5 stars, which means the quality of instruction is about the best you’re likely to find anywhere. 

When you couple all these benefits along with the low cost of enrollment — just $40 for a limited time — then the 2023 Premium Python Programming Mega Certification Bundle is a low-risk, high-reward option worth checking out.