Is Coding Required in Machine Learning?
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Are you looking to pursue a career in machine learning? If yes, then you must have a question — “is coding required for machine learning”. The answer to this — a little coding is necessary for ML. Implementing Machine learning demands coding. So, programmers who master the skills to implement that code gain a strong understanding of how ML algorithms work. In this article, we shall discuss why coding is required for machine learning in a detailed manner, let’s begin with defining machine learning.
What is Machine Learning?
Machine learning is an important module/application of Artificial Intelligence. Computers and machines use ML algorithms to learn through repetitive learning. Machine learning uses a large amount of data to instruct systems to find patterns and predict new values.
Machine learning applications are prevalent in almost every field of human life. For example:
- ML implementation by tech giants like Google/IBM
- Smart vehicles
- Speech/Image Recognition
- Fraud detection
- Products recommendation
- Fraud/malware detection
Is Coding required for Machine Learning?
Yes, coding is an essential part of machine learning. It is coding that enables programmers/developers to train, test, and evaluate machine learning models and implement them on computer systems. A computer can be programmed in ML algorithms via coding. Codes are the only means to communicate with computers and make them execute specific tasks.
Once the machine learning model is well-trained, it can deliver accurate results. After this point, there is limited or no need for coding. Machine learning models can be commanded via intuitive dashboards only. However, a programmer who wants to enhance the accuracy level or modify the ML needs to dive into coding again.
Programming languages like C++, R, Java, Lisp, and Python are important for machine learning coding. In Machine learning, it is not necessary to have prior experience with other languages like HTML and CSS. You must begin learning Python as it is one of the simplest programming languages for ML.
To pursue a career in Machine Learning, it is very crucial to understand the underlying concepts like probability, stats, complex linear algebra, and calculus. All of them help you gain expertise in developing ML algorithms. You must also master some foundational machine learning paradigms like supervised learning, unsupervised learning, reinforcement learning, and neural networks. Only after you master the underlying Machine Learning Concepts, start with coding and learning to write codes.
Some of the Programming Languages to Learn Coding
Python is a popular programming language that has innumerable applications in data science, artificial intelligence, and machine learning. Python offers readable code, flexibility, and a vast collection of libraries and packages. So, you must learn this programming language now.
R is an open-source programming language used in machine learning and statistical computing. You can use R for a variety of machine learning applications, like data visualization, implementing machine learning methodologies, supervised or unsupervised learning, etc. So, you must learn R now.
C++ or C is the most powerful and one of the fastest programming languages. C++ is preferred by Machine learning experts for manipulating ML algorithms or controlling a variety of different performance parameters. TensorFlow, mlpack, and Torch are some of the C++ libraries that provide a large number of popular machine learning algorithms. So, you must learn C++ now.
What’s the Extent of Coding in ML?
Tools and libraries in Machine learning systems help them function effectively. And these ML tools require hardcore coding. Programming Languages used for machine learning coding are R and Python. These languages are quite simple and have easy syntax. So, a beginner can learn them with the sole purpose of building a career in Machine learning.
If you ask ‘how much’ coding is necessary for Machine Learning, the answer is ‘basic level’. To be an expert in programming & pursue a career as an ML engineer, requires only the basic knowledge of the underlying concepts of the programming language.
See, it might be complex to implement certain machine learning algorithms. But, you can make the task simpler for yourselves. How? Just learn the concepts behind the major ML algorithms like — Naïve Bayes, Gradient Descent, etc. Concepts lead to automatic Programming skills & programming skills are nothing but coding.
To learn ML coding, you must first gain an idea of a few machine learning libraries. These libraries are as follows: Scikit-learn, TensorFlow, NumPy, and Keras.
In conclusion, we can say that a programmer is a programmer only if he knows how to ideally build an algorithm, implement the code, and obtain results. Machine learning coding is a needed approach to becoming a machine learning engineer with lucrative career opportunities. In case you want to have a detailed understanding about ML, you can enroll yourself for the courses offered by The IoT Academy.