Does Machine Learning Require Coding?

Yes, if you’re looking to pursue a career in artificial intelligence and machine learning, a little coding is necessary. Machine learning is implemented through coding and programmers who understand how to implement that code will have a strong grasp on how the algorithms work and will be better able to monitor and optimize those algorithms.

Three programming languages come up most frequently: C++, Java, and Python, but it can get much more specific as well. Languages like R, Lisp, and Prolog become important languages to learn when specifically diving into machine learning.

Having said that, a previous understanding of other languages like HTML and CSS isn’t necessarily needed. Instead you can start with the more relevant languages like Python, which are considered relatively easy to learn because of features like their use of English words in place of punctuation.

Paradoxically, machine learning is interesting in that the main goal is to train a computer to learn on its own without human intervention. Some key skills you’ll need are knowledge of probability and statistics, complex linear algebra, and differential and integral calculus in order to understand the fundamentals of what’s propelling machine learning and easily work with data matrices and vector operations. Other important foundational machine learning paradigms to learn include supervised learning, unsupervised learning, reinforcement learning, and neural networks.

Some Machine Learning Engineers recommend that those interested in pursuing the career start with these key concepts as opposed to starting with coding and learning to write code. It’s critical that you understand the underlying concepts that allow artificial intelligence to function.

Although there is some debate over exactly how important coding skills are for a Machine Learning Engineer, it is generally agreed that you would need to develop at least basic programming skills in order to most effectively leverage leverage, create, and implement machine learning models and machine learning algorithms.

For anyone looking for a career in machine learning or hoping to level up your machine learning skills for jobs in related fields like data science or data analytics, these are the best programming languages for machine learning:

One of the world’s most popular programming languages with near-endless applications in data science, data analysis, artificial intelligence, web development, and software engineering, Python has also become one of the most important languages for machine learning because of its readable code, flexibility, and vast collection of libraries and packages.

Python libraries and packages can be crucial in saving time and effort for any number of complex machine learning frameworks and applications. For instance, TensorFlow or Keras are effective for deep learning, NumPy might be the best tool for working with textual data, while scikit-learn is useful for implementing ML algorithms.

Another popular open-source programming language, R is widely used in statistical computing and it’s often the language of choice for Data Scientists who are working with large amounts of statistical data. R can be used for a variety of machine learning applications, including data sampling, data visualization, supervised or unsupervised learning, and machine learning model training.

Also boasting a huge list of useful packages, R is often used for implementing machine learning methodologies including classification, decision tree formation, and regression.

These versatile, powerful, and fast programming languages have proven efficient and useful for machine learning. C++ can be used to manipulate machine learning algorithms or exert control over a variety of different performance parameters. C++ boasts powerful open-source libraries like TensorFlow, mlpack, and Torch, which can perform scientific and numerical operations, boost efficiency, and supply a large number of popular machine learning algorithms.

JavaScript and Java are multipurpose programming languages that, like Python, have a seemingly endless number of possible applications — including in machine learning. Java and JavaScript are fast, powerful tools for developing machine learning algorithms, regression techniques, and processing data.

Java frameworks like Weka, Rapid Miner, and JavaML provide support for a number of tasks relating to machine learning and data mining, including data preprocessing, data preparation, clustering, and classification. Meanwhile, JavaScript’s libraries can be used to integrate neural networks or build and train an ML model.

A more specialized and relatively new programming language, Julia is specifically popular for machine learning model analytics, developing machine learning apps, and conducting high-performance numerical analysis. With support for all types of hardware, Julia is fast with uncomplicated syntax and universally executable code that can be compiled in Julia from languages like Python or R in a wrapper.

Julia’s ecosystem offers a number of useful packages for machine learning. The MLJ.jl package supplies a unified interface for popular ML algorithms like clustering, decision trees, and generalized linear models, while Turing.jl is a powerful package for probabilistic programming and Flux.jl is useful for deep learning.