“Whether you want to uncover the secrets of the universe or want to pursue a career in the 21st century, basic computer programming is an essential skill to learn.”
The empowering words mentioned above by late physicist Stephen Hawking highlights the importance of learning programming languages for moving into a career in the current data-driven world. Programming is the art of defining implausible concepts into plausible and human interpreted forms. Be it providing an interactive interface for communicating with computer systems, commanding the servers to perform tasks or generating results based on the information fed into the systems for computation, all these activities require extensive utilisation of programming languages.
Programming is a vital tool in the data science, analytics and machine learning fields. The crux of machine learning revolves around deducing results from the data provided into the system—professionals who aim to learn machine learning online need to enrol for credible programs. Undertaking the right set of courses offered by high-rated universities can enable young engineers to map out their corporate careers. The IIIT machine learning course is an excellent program for learning about the need and importance of programming languages in ML silos.
Programming Languages Used by Machine Learning Silos
- As per Global Newswire, the value of the machine learning market is slated to reach USD 117 billion by end of the year 2027, with a growth percentage of 39%.
- According to a survey conducted by MemSQL, nearly 74% of respondents reported that the adoption of ML has resulted in the transformation of corporate jobs and industry as a whole.
If the two states mentioned above make one thing clear, the future markets will see an explosion of machine learning practices. The sheer dominance of machine learning has been led by the nuanced programming languages that have emerged in recent years.
Given how the machine learning markets are booming, it’s practical and logical to pursue a machine learning and data analytics course from IIIT Allahabad. With the amalgamation of correct programming environments in the ML landscape, pattern search’s overall efficiency and accuracy can be optimised.
Interested engineers and machine learning enthusiasts who want to learn machine learning online and move into these career paths need to understand the variety of programming languages used by this sector:-
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Python Programming Language
Python is undoubtedly the leader in providing customizable syntax, integration with open source data reserves, and other practices in modern programming languages. Currently, there are nearly 8.2 million developers who use Python for their daily operations. Python offers several options for programmers to derive results for machine learning models and their training.
Python houses a great combination of libraries and packages for dealing with different data types. These packages help in code reusability, thereby saving time and energy spent on writing code from scratch. Some of the popular packages and libraries that Python supports include- TensorFlow, Sci-py and Seaborn.
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R Programming Language
The R programming language works well for machine learning silos that require heavy usage of statistics. R has an astonishing 2 million users all over the world. R is easy to comprehend and learn a language that can also be used for data mining, data analysis and for rendering deeper insights about data sets.
R is the best language for crunching the numbers game, training algorithms for generating results as per corporation’s preferences. Several resourceful packages help in predicting future outcomes. Some of these include- MICE for figuring out missing values, PARTY for developing data partitions, randomFOREST for deploying decision trees etc.
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Java Programming Language
JAVA is another programming language that programmers deploy for providing solutions to complex machine learning problems. Java APIs consist of several open-source tools for data processing in data science and machine learning landscapes. The open-source toolkits include- Hadoop, Spark and others. Java offers the luxury of integration with existing code repositories.
Java also renders a clear set of syntax practices and easily understandable code structures, making it the default programming language in modern coding scenarios. With the JavaML third-party library, users can access the algorithms that conform to machine learning industries. Moreover, with the inception of DeepLearning4J and Neuroph libraries, machine learning engineers can effectively develop KNN and deep neural network algorithms.
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Lisp Programming Language
Lisp is a problem-oriented programming language that directly deals with machine learning and AI issues and provides in-built coding mechanisms for the same. Currently, lisp is used for solving inductive logic problems and other machine learning practices.
One of the advantages of working with lisp programming language is that it similarly treats data and codes, leading to increased speed and accuracy. Lisp is a dynamic programming language that helps in creating chatbots and other interactive methodologies.
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Julia
Julia is a programming language that exhibits high performance and scalability in conducting numerical analysis and rendering insights on computational data. Julia works well with hardware such as TPU’s and GPU’s for cloud-based storage services.
The application of Julia helps in implementing basic mathematical and arithmetic queries in machine learning models. Julia supports the LLVM framework for solving inherent problems and can be applied in different programming environments. Julia’s several advantages include rapid prototyping capabilities, flexibility in terms of working with several programming scenarios, and the only language that supports symbolic expressions.
As seen from the languages discussed above, these different programming applications help perform different tasks pertaining to machine learning. The IIIT machine learning provides further knowledge about the different types of languages and their uses in ML landscapes.
Conclusion
The programming languages mentioned adobe highlights the need for interactive and comprehensible computation practices that can interact with computer systems and offer meaningful insights. Be it utilising an extensive set of libraries, working with statistical information, taking the help of third-party open source tools, or solving problems, the adoption of the right programming language is vital for deploying successful algorithms.
In order to learn machine learning online and understand its basic concepts, it’s necessary to register for credible programs. The IIIT machine learning course enables learners to get a grip over the key dynamics of this sector.
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