Let's explore these factors in detail:

1. Simplicity and Readability:

Python's simplicity and readability make it an accessible language for beginners and experienced developers alike. Its clean and intuitive syntax allows for easier understanding and maintenance of code, promoting faster development and collaboration within the machine learning community.

2. Extensive Library Support:

Python offers a vast array of libraries specifically designed for machine learning and data analysis. Libraries such as NumPy, Pandas, and Matplotlib provide fundamental data manipulation and visualization capabilities, while libraries like TensorFlow, PyTorch, and scikit-learn offer powerful tools for building and training machine learning models. The availability of these comprehensive libraries has significantly accelerated development in the field.

3. Strong Ecosystem of Frameworks:

Python boasts a robust ecosystem of machine learning frameworks that simplify the implementation of complex algorithms and models. TensorFlow, PyTorch, Keras, and scikit-learn are some of the widely adopted frameworks that provide high-level abstractions, efficient computation, automatic differentiation, and GPU acceleration. These frameworks have played a crucial role in democratizing machine learning and enabling researchers and practitioners to focus on higher-level concepts rather than low-level implementation details.

4. Community Support and Collaboration:

Python has a vibrant and supportive community dedicated to machine learning and data science. The community actively contributes to the development of open-source libraries, shares knowledge through forums, mailing lists, and online platforms, and collaborates on research and projects. The availability of resources, tutorials, and code examples has made it easier for newcomers to learn, experiment, and contribute to the machine learning ecosystem.

5. Integration and Interoperability:

Python's versatility and ease of integration with other programming languages and tools have been instrumental in its dominance in machine learning. Python can seamlessly interface with C/C++, enabling efficient computation and integration with existing libraries and systems. It also integrates well with popular data storage and processing frameworks, such as Apache Hadoop and Apache Spark, facilitating scalable and distributed machine learning workflows.

6. Adoption by Industry Leaders:

Python's popularity and versatility have led to widespread adoption by industry leaders and tech giants. Companies like Google, Facebook, Amazon, and Microsoft heavily utilize Python for machine learning and data science applications. This endorsement by industry leaders has further solidified Python's position as the go-to language for machine learning and has contributed to the development of cutting-edge tools and technologies in the ecosystem.

7. Educational Resources and Support:

Python's role as a beginner-friendly language and its extensive educational resources have contributed to its dominance in machine learning. Many universities, online learning platforms, and tutorials focus on Python as the language of choice for teaching machine learning concepts. This educational emphasis has resulted in a large pool of practitioners and researchers proficient in Python, further fueling the growth of the ecosystem.

These factors have fostered a thriving ecosystem of libraries, frameworks, and community support, making Python the preferred programming language for machine learning practitioners and researchers worldwide.

Hope it helps

Python for machine learning is a great choice, as this language is very flexible: It offers an option to choose either to use OOPs or scripting. There's also no need to recompile the source code, developers can implement any changes and quickly see the results.۸

Python has a rich bank of libraries for machine learning, some of them are- TensorFlow, Keras, and Scikit-learn, Numpy, Scipy, Pandas, Seaborn, etc. In these NumPy and Scipy are specifically for scientific and advance computing respectively.

Everybody else uses it so the key libraries are implemented in Python or have a good Python API. Once enough people use a technology, in this case Python, the technology adapts to that use, in this case data science, then this technology becomes optimised to and the most suited to that application.

Some things that make it nice to use now: 1,) easy to read and write data files, 2,) it is easy to run in a virtual environment so you can isolate it from other implementations and your core Python installation so it is easy to fix config errors, easy to port, and through anaconda easy to install as a user rather than as sys admin 3,) easy to package and distribute 4), lots of examples and tutorials online and many courses at universities 5,) easy to use with key AI packages eg PyTorch and TensorFlow 6,) More will follow becasue so many people use it.

I actually like R programming better along with JASP. it's just my opinion

Well - simple answer is cause there is nothing better (maybe R in some aspects). More detailed - I was trying to use in scientific research C/C++, Go, Perl, LabView and other ideas. LabView idea is briliant - but most of IT environment does not get it as known pattern. Go langague is great for system programming but - you are not able to define your own types and data structures. Express your idea - expecially when your data is not obious gets complicated. Moreover Python langage is some kind of linga franca between IT and Scientific people. First Python was great fast prototyping tool - but now - with the existing toolset - it became a monster of big data processing tool. So - there is nothing better than this on plate.

python deal with purely data science and artificial intelligence including vast libraries and enrich compiling features so that's why python is popular.

Python's dominance in the field of machine learning can be attributed to several key factors that have made it the preferred programming language for this domain:

In summary, Python's dominance in machine learning can be attributed to its simplicity, rich ecosystem, community support, and versatility. These factors have contributed to its widespread adoption and flourishing ecosystem, making it the top choice for machine learning practitioners and researchers worldwide.

Python's dominance in machine learning is due to:

If you are interested in delving further into this subject, I recommend explore this site https://researchbrains.com/deep-learning-programming-tools/ to gain better understandings.

May I play 'devil's advocate'? Just because something is popular doesn't necessarily make it 'right'. The well worn path may simply lead us to the same destination.

I have started a speech understanding project to create computational capability in natural language, Enguage. This produces succinct solutions because it closes the gap between the problem and solution domains.

I'm not sure whether it would be 'right' for you, but it represents AGI as language forms the glue between all the specialised AI techniques that transcend the phenomenological barrier, much like HTML forms the glue between non-textual content.