Here's how you can choose the best programming languages for a career in Machine Learning.

Python is a good programming language for machine learning and deep learning. It is highly recommended to use Python for these purposes. Python has simple syntax, is readable, and easy to learn. Python has too many free libraries and packages for ML, DL, data visualization, and wrangling such as Numpy, Pandas, Sickit-learn, Pytorch, Tensorflow, and .... Its documentations, resources, and community is too vast and you can easily and quickly find the answers of your questions. But Python has low performance. It is low speed, high memory consumption, high energy efficiency. Most of ML algorithms are computational expensive. So the core of more Python's ML packages is written by high performance languages such as C , C++, Fortran, and ....

I am a non technical person who transitioned into data science 4 years ago and thanks to python for making this transition easy. The extensive libraries, open source resources, easy help on stackoverflow, chat bots of today make it easy to advance pretty quickly. Pandas was my favourite library as it made my transition easier from Excel and performed the same functions I would on Excel in a much more structured and dynamic way. For ML tasks start with scikit learn, spacy and then progress to tensorflow (keras), pytorch, gensim based on your needs and if deep learning is needed. The libraries you should start depends on your role profile, like for analysts good to master pandas and then move to ML libraries.

1. Python's simplicity streamlines learning and application in machine learning. 2. Its clear syntax lets developers focus more on problem-solving than code complexity. 3. Extensive libraries like TensorFlow and scikit-learn enhance ML capabilities. 4. These libraries provide tools for efficient data manipulation and algorithm implementation. 5. Python's popularity ensures a vast community for support and collaboration. 6. Community resources are crucial for troubleshooting and learning new techniques. 7. Regular updates and contributions keep Python at the forefront of ML technology. 8. Python fosters a collaborative environment beneficial for beginners and experts alike.

Python is the most widely used programming language in the machine learning community due to its simplicity, versatility, and extensive library support, particularly with libraries like TensorFlow, PyTorch, and scikit-learn. Its readability and ease of use make it ideal for beginners and experienced practitioners alike, allowing for rapid prototyping, experimentation, and deployment of machine learning models. Python's strong ecosystem of data science libraries, frameworks, and tools provides comprehensive support for tasks such as data manipulation, visualization, and model evaluation.

Python is the best programming language to learn for machine learning. It is simple and has great libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. These libraries make it easy to work with data, build models, and do deep learning. Python has a large community that creates many helpful tools specifically for machine learning tasks. While other languages like R, Java, and C++ have their niche use cases, Python's dominance in machine learning is unparalleled, making it the ideal starting point for anyone looking to kickstart their career in this rapidly growing domain.

8-9 years ago R would be a reasonable choice for stats-focused professionals since it has a lower learning-curve and is slightly better suited for this purpose than Python. But at this point, learning R over Python is (IMO) always a mistake. Here's why. Career paths are ever-changing, and the likelihood of transitioning into a field where Python dominates is significant—I see it happen all the time with job seekers where they want to make a slight pivot in functionality or industry, and their lack of Python skills hold them back. My advice? Avoid boxing yourself in by picking a language based on its current ease of use, which might restrict your opportunities down the line.

R is a programming language commonly used in statistical computing and data analysis, making it well-suited for tasks involving statistical modeling, hypothesis testing, and exploratory data analysis. Its rich collection of packages and libraries, such as ggplot2 and caret, cater specifically to statistical analysis and visualization, offering powerful tools for data scientists and statisticians. R's syntax and functionality are optimized for statistical tasks, making it a preferred choice for researchers and practitioners working in fields where statistical methods are paramount.

R's specialized syntax, while distinct from more general-purpose languages like Python, is optimally structured for statistical programming. This makes it highly efficient for data scientists and statisticians who require a toolset finely tuned to the nuances of statistical analysis. Additionally, the language's focus on statistical techniques makes it an indispensable tool in fields that rely heavily on thorough data exploration and interpretation, such as biostatistics, epidemiology, and econometrics.

Developers look at their project needs, team’s domain specific expertise & current trends for selecting a language. R is well designed for high-level statistical analysis, visualizations & modeling- be it time series, panel, pool, bioinformatics, genomics or geopolitical structure. They aid in data manipulation, multivariate & survival analysis, ML, simulations, predictions, etc. Python is versatile general-purpose programming for AI, NN, NLP, API integration & deployment. I learnt R in 2018 for econometrics. Learning curve is steeper than Python. After 2020, I don't see many giving emphasis to learning R. It doesn’t have efficient compilers, memory managers & garbage collection. Although it is good to have knowledge of 2-3 languages.

R is tailored for those whose machine learning journey focuses primarily on statistical analysis. Its syntax, though specialized, offers a robust set of tools like ggplot2 for data visualization and caret for machine learning tasks. While it's powerful for statisticians and data scientists, beginners might find its syntax and learning curve challenging. So, if you're diving deep into statistical analysis, R is a strong choice. However, if you're just starting in machine learning with a broader scope, Python's versatility might be more suitable.

Java is widely used in enterprise environments for developing scalable, robust, and reliable applications, including those that incorporate machine learning capabilities. Its strong typing, object-oriented nature, and platform independence make it suitable for building large-scale systems and integrating machine learning models into existing software infrastructure. Java's mature ecosystem, extensive libraries, and frameworks such as Weka and Deeplearning4j provide support for various machine learning tasks, from data preprocessing to model deployment.

Java stands out in the realm of machine learning for its remarkable stability, scalability, and performance, qualities that are especially crucial in large-scale enterprise environments. While Python is often celebrated for its ease of use and rich library ecosystem in machine learning, Java brings its robustness and speed to the table, making it a preferred choice for high-stakes and high-volume applications.

If you're working on enterprise-level ML projects that require robustness and integration, Java is a solid pick. However, for those new to ML or focusing on rapid prototyping, Python's ecosystem might be more fitting.

C++ is favored in machine learning applications where performance and efficiency are critical, such as real-time inference, high-frequency trading, and embedded systems. Its low-level control over hardware resources, memory management, and execution speed make it ideal for implementing algorithms that require maximum computational efficiency. C++ libraries like TensorFlow, OpenCV, and Eigen leverage its performance capabilities, allowing developers to build high-performance machine learning solutions optimized for speed and resource utilization.

C++ offers unparalleled control over hardware and system resources, making it a go-to language for high-performance computing in machine learning. Its influence is deeply rooted in engineering and real-time systems, areas where efficiency and speed are paramount. With C++, you have the power to optimize algorithms to run efficiently on specialized hardware, a crucial skill in fields like robotics, embedded systems, and accelerators. For those with a background in electronic engineering, diving into C++ not only enhances your ML capabilities but also bridges the gap between software and hardware, opening doors to advanced ML applications in embedded systems.

C++ does not have easy syntax. Learning curve is steep. You may take one whole year struggling on coding, debugging errors & even more. You will have to stuggle on Visual Studio too. If you already know Python, preferably stick to it for NLP, ML, DL, LLM. C++ is a robust, general-purpose, object-oriented, fast & speedy. You can code algorithms from scratch when you can't find any relevant library in Python (most of the libraries are sufficient). The level of control is high. C++ has good classes, templates, exceptions, references (not just pointers) & more. It is used in operating systems, robotics, self-driving cars, hardware, banking data, video games, IoT apps, etc. ML frameworks like Dlib, Shark, TensorFlow API aid in algorithms.

For machine learning, C++ can be particularly beneficial in scenarios where the time complexity of data processing and model training needs to be minimized. Although C++ does not have as vast a selection of pre-built machine learning libraries as Python, libraries such as Dlib, Shark, and FANN are tailored for C++ and are highly optimized for performance. These libraries provide a range of functionalities from generic algorithms to complex neural networks.

C++ has enormous advantages when it comes to speed for computation and memory management. It is thus an essential programming language for developing the backend of many popular libraries in python. C++ might have a slight learning curve, however it is often easier to implement ML algorithms in C++. Additionally, since it uses a compiler instead of a interpreter (like Python), debugging and error handling is faster and more efficient.

Given its dominance on the web, JS's role is to make AI/ML functionalities more accessible to web applications, improving how they are delivered and experienced online, particularly in real-time applications. To contribute to the core development of AI technologies, JavaScript requires improvements in handling complex calculations and memory-intensive tasks, and the development of new libraries and frameworks to reduce the gap between ML development and web-based implementations. JS will likely continue to make web experiences more interactive and responsive. So, it's a good choice for someone interested in the intersection of AI/ML and Web development, but Python is a must anyway IMHO. :)

JavaScript's popularity and versatility have led to its increasing adoption in machine learning applications, particularly in areas such as web-based data visualization, interactive demos, and browser-based machine learning models. Libraries like TensorFlow.js and Brain.js enable developers to train and deploy machine learning models directly in the browser, opening up new possibilities for client-side intelligence and edge computing. JavaScript's ubiquity across web and mobile platforms, coupled with its asynchronous programming model and support for event-driven architectures, makes it a valuable addition to the machine learning toolkit.

The advantage of using JavaScript for machine learning lies primarily in its ability to integrate seamlessly with web technologies. This integration is crucial for developing interactive and dynamic web applications that utilize machine learning for real-time predictions, personalization, and other AI-driven features. TensorFlow.js, for instance, provides the tools necessary for training and deploying ML models directly in the browser, reducing server-side dependencies and allowing for client-side model execution, which can significantly speed up interaction times and reduce the load on backend services.

For web developers venturing into ML, JavaScript provides a familiar environment to explore and implement machine learning functionalities, bridging the gap between front-end development and data science.

SQL (Structured Query Language) is essential for managing and querying large volumes of structured data, making it indispensable for data preprocessing, data wrangling, and database management in machine learning projects. Its declarative nature and standardized syntax simplify data manipulation tasks such as filtering, aggregating, joining, and transforming data, facilitating efficient data processing and analysis. SQL's compatibility with relational database management systems (RDBMS) like MySQL, PostgreSQL, and SQLite enables seamless integration with data pipelines and workflows, ensuring data accessibility and reliability throughout the machine learning lifecycle.

While not a programming language for developing ML algorithms per se, mastering SQL allows you to effectively work with the data that powers these algorithms. For those transitioning from database management or data analysis to machine learning, SQL proficiency becomes an invaluable skill, facilitating seamless data integration and preparation for ML tasks.

For anyone working in machine learning, proficiency in SQL is essential because it enables seamless interaction with databases where most of the training data for ML models is stored. Before any machine learning can happen, data must be extracted, cleaned, and preprocessed. SQL provides the tools necessary for these tasks through its powerful querying capabilities, which include filtering, sorting, and aggregating data. These operations are vital for preparing the data set to meet the specific requirements of machine learning models.

There are many alternatives to Python in the ML space. Julia language is a very good example or the new comer Mojo. Julia is a very performing language and it’s picking up adoption so it might be a good secondary language to learn. Mojo, on the other hand, is a pretty good Python replacement as its syntax is very similar and most of the existing libraries can be used with it. Still it’s very difficult to go wrong with Python + c++ with maybe some CUDA thrown in.