Does machine learning require coding and how much?

Subscribe to our newsletter for social resources

Join 5000+ students that regularly receive our informative and interesting newsletters.

Committed to your privacy.

In this article, we will go over the prerequisites required for machine learning when it comes to coding. We’ll look at what level of coding expertise is required to become an ML engineer and what different coding languages you can learn. So, sit back and read along to see if coding is necessary for machine learning or if it’s all a myth.

 

Is it necessary to code for Machine Learning?

Yes, coding is at the heart of machine learning because it allows for the development, training, testing, and evaluation of machine learning models as well as their implementation on computer systems. Coding is the only way to communicate with computers and instruct them to perform specific tasks, so the ML algorithms must be coded in order to allow them to be interpreted.

However, once the machine learning model has been trained and starts producing accurate results, there is little to no need for further coding. The models can be controlled via simple dashboards, and unless you want to improve the accuracy or tweak the model, you won’t need to dive back into coding.

 

How Much Coding Is Required in Machine Learning?

Learning about the basic functioning of programming languages can help in getting a proper grasp of machine learning. 

Machine learning is mostly done with tools and libraries, so hard coding is not required. Furthermore, the languages recommended for ML, such as R and Python, are not particularly syntax-aggressive, so learning them for the sole purpose of ML is relatively simple.

But it is always recommended to learn to program because it can help you to better understand the working of ML algorithms which in turn allows you to properly optimize and monitor them.

Still, if you wish to take the easier way, here are a few libraries you could use to avoid having to code everything from scratch and instead focus on optimization and accuracy:

  • NumPy: It is a linear algebra library that can be used to perform mathematical calculations as well as work with multidimensional arrays and convert complex and massive data into data frames.
  • Scikit-learn: This library implements a variety of machine learning algorithms such as regression, clustering, and classification. 
  • Keras: It is a deep-learning library used to build neural networks.
  • TensorFlow: It is a machine learning library that can be used to train deep neural networks.

 

Programming languages required for machine learning

Basic programming skills are required to become an ML engineer. As an ML engineer you may not need to explicitly code like other software developers, surface-level knowledge is useful in a variety of situations and distinguishes average engineers from the best in the field. Some key topics which you should master are

  • Data structures: A thorough understanding of how various data structures such as trees, queues, stacks, and graphs can be used to aid in the development of ML models.
  • Algorithms: Algorithm fundamentals such as searching, sorting, and optimization
  • Complexity: The ability to deal with difficult problems such as Big-O notation and np-complete problems.
  • Object-oriented programming (OOP): A solid understanding of OOP (object-oriented programming) concepts such as inheritance, objects and polymorphism, and abstraction.

 

Machine learning as a career

As machine learning advances, so does the jobs that go with it. Here are some career options which you can take in the field of ML: 

  1. Machine Learning Engineer

A machine learning engineer is a programmer who uses programming languages such as Python, Java, Scala, and others to run experiments and develop new programs.

  1.   Data Scientist 

Data scientists examine massive amounts of data to gain valuable insights into where action should be taken. You will not only spend a significant amount of time researching, but you will also solve problems, find meaning in data, and “understand the deeper implications of and human impact of [the] project.” 

  1. Software Developer

Software developers, also known as the creative minds behind computer programs, use technical skills to build programs or supervise their team’s creation. A career as a software developer is a rapidly expanding field that is becoming increasingly important for many businesses.

 

Skills required for machine learning

Now, let’s talk about what skills or prerequisites are needed to become a machine learning engineer.

  • Statistics and Probability

To ensure efficient results, many machine learning algorithms employ probability functions and techniques such as Bayes rules, conditional probability, likelihood, sampling, and others. ML requires statistics to analyze and observe the data in order to build models and train them. 

  • Linear Algebra 

A machine learning engineer must understand linear algebra and should be able to manipulate matrices and perform basic calculations such as addition, subtraction, scalar, and vector multiplication.

  • Calculus 

In order for you to become a proficient machine engineer, you must have a basic understanding of calculus to study continuous change, as well as an understanding of differential and integral calculus. 

 

Conclusion

Being an experienced or expert coder is not necessarily required because the main focus should be on the machine learning pipeline rather than the programming. 

Although, it is always a good idea to learn to code because you cannot implement and modify algorithms on computer systems without coding. And it is an even better idea to do so with Skolar!

Share

Leave a Reply

Your email address will not be published. Required fields are marked *