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6 Coding Languages You Need to Learn to Get Into Machine Learning

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In this article, we’ll explore the top coding languages you should learn to make your mark in the world of machine learning. We’ll also point out why they’re useful for ML, and some of the top libraries available for each, and briefly offer tidbits about how ChatGPT can be used to further your learning.

Nowadays, machine learning is skyrocketing in popularity and growth, and quite rightly so. This technology has great potential to bring transformational changes in various industries and enhance our lives in numerous ways.

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Learning the coding languages required for machine learning work is a wise move. Image from Envato Elements.

To get started in machine learning, you’ll need to learn the right programming languages. But, which ones should you focus on? That’s what we’re focusing on here today.

Read on to get started, or jump to content in this section:

1. Python


Python has quickly become the go-to language for many developers entering the machine learning field. It’s a versatile, high-level programming language that’s easy to read and write, making it an excellent choice for beginners and experts alike.

Python’s popularity in machine learning can be attributed to several factors:

  1. Easy to learn: Python’s syntax is simple and easy to understand, which makes it perfect for those new to programming. Additionally, Python has a vast and active community that offers plenty of resources and support.
  2. Rich ecosystem of libraries and frameworks: Python boasts a vast array of libraries and frameworks tailored for machine learning, making it easy for developers to build and deploy models.

Top Python Libraries for Machine Learning

When it comes to machine learning in Python, you’ll want to familiarize yourself with the following libraries:

  • TensorFlow: An open-source machine learning library developed by Google, TensorFlow is perfect for deep learning and neural network applications.
  • Keras: A user-friendly neural network library that works as an interface for TensorFlow, Keras simplifies the process of building and training deep learning models.
  • PyTorch: Developed by Facebook’s AI Research lab, PyTorch is another popular open-source library for machine learning and deep learning.

Learn to Code With Python

2. R


R is a programming language specifically designed for statistical computing and data analysis. It’s an excellent choice for those who want to focus on the more statistical side of machine learning.

R’s Role in Machine Learning and Data Analysis

R offers several advantages for machine learning practitioners:

  1. Statistical prowess: R is designed for statistical analysis, which is an essential aspect of machine learning. R’s robust statistical capabilities make it a popular choice for data scientists and analysts.
  2. Rich ecosystem of packages: Like Python, R has a comprehensive collection of packages dedicated to machine learning and data analysis.

Top R Packages for Machine Learning

Some essential R packages for machine learning include:

  • Caret: A popular package for classification and regression training, Caret provides a consistent interface for various machine learning algorithms.
  • Random Forest: This package implements the widely-used Random Forest algorithm for classification and regression tasks.
  • Xgboost: Short for eXtreme Gradient Boosting, Xgboost is an efficient implementation of the gradient boosting algorithm.

3. Java


Java is a popular, versatile, and widely-used programming language. Its platform independence and extensive libraries make it a solid choice for machine learning applications.

Advantages of Using Java for Machine Learning

Java offers several benefits for machine learning practitioners:

  1. Platform independence: Java’s “write once, run anywhere” philosophy means that you can build machine learning applications that work seamlessly across different platforms.
  2. Strong performance: Java’s performance and scalability make it well-suited for large-scale machine learning projects.
  3. Mature ecosystem: Java has a vast collection of libraries and tools, making it easier to find solutions for your machine learning problems.

Java has a range of libraries that cater to machine learning developers:

  • Weka: An open-source collection of machine learning algorithms, Weka is perfect for data mining and predictive modeling tasks.
  • Eclipse Deeplearning4j: A deep learning library for Java, Eclipse Deeplearning4j allows you to build, train, and deploy neural networks.
  • Apache Mahout: Part of the Apache ecosystem, Mahout is a scalable machine learning library focused on collaborative filtering, clustering, and classification.

4. C++


C++ is a high-performance programming language known for its efficiency and control over system resources. While it has a steeper learning curve than languages like Python, its power and speed make it a popular choice for performance-critical machine learning applications.

Why C++ is a Powerful Choice for Machine Learning

C++ offers several advantages for machine learning developers:

  1. Speed: C++ is a compiled language, which means it often outperforms interpreted languages like Python in terms of execution speed.
  2. Control: C++ gives you more control over system resources, which can be crucial for optimizing the performance of machine learning algorithms.
  3. Interoperability: C++ can easily interface with other languages, which allows you to leverage existing code and libraries written in other languages.

Top C++ Libraries for Machine Learning

C++ boasts a collection of libraries tailored to machine learning:

  • Caffe: Developed by the Berkeley AI Research lab, Caffe is a deep learning framework focused on speed, modularity, and expressiveness.
  • Dlib: A general-purpose library containing various machine learning algorithms, Dlib is perfect for tasks like object detection, face recognition, and pose estimation.

5. Julia


Julia is a high-level, high-performance programming language designed for technical computing. It combines the simplicity of Python with the speed of C++, making it an attractive choice for machine learning.

Benefits of Using Julia for Machine Learning

Julia offers several advantages for machine learning practitioners:

  • Performance: Julia’s performance is on par with C++, which means you can build and deploy fast and efficient machine learning applications.
  • Ease of use: Julia’s syntax is similar to Python, which makes it easy to learn and use.
  • Growing ecosystem: While still relatively young, Julia’s ecosystem is rapidly growing, with an increasing number of libraries and tools for machine learning.

Some essential Julia packages for machine learning include:

  • Flux.jl: A flexible and intuitive deep learning library for Julia, Flux.jl allows you to build and train complex neural networks with ease.
  • MLJ.jl: A machine learning framework for Julia, MLJ.jl provides a unified interface to various machine learning algorithms.
  • ScikitLearn.jl: Inspired by Python’s Scikit-learn library, ScikitLearn.jl brings popular machine learning algorithms to the Julia ecosystem.

6. Scala


The programming language Scala is a combination of object-oriented and functional programming, making it another versatile choice. Its compatibility with Java and strong support for parallel and distributed computing make it an excellent choice for machine learning and big data applications.

Scala’s Role in Big Data and Machine Learning

Scala offers several benefits for machine learning practitioners:

  1. Concurrency and parallelism: Scala’s built-in support for parallel and distributed computing allows you to build scalable machine learning applications that can handle large datasets.
  2. Compatibility with Java: Scala is interoperable with Java, which means you can leverage the vast Java ecosystem while taking advantage of Scala’s more expressive syntax and functional programming capabilities.
  3. Strong ecosystem: Scala has a growing ecosystem of libraries and tools tailored for machine learning and big data.

Key Scala Libraries for Machine Learning

Some popular Scala libraries for machine learning include:

  • Apache Spark: A powerful, open-source distributed computing system, Spark is widely used for big data processing and machine learning tasks.
  • Breeze: A numerical processing library for Scala, Breeze offers a variety of machine learning algorithms and utilities.
  • Saddle: A data manipulation library for Scala, Saddle provides tools for data cleaning, transformation, and analysis.

Comparison and Choosing the Right Language

Selecting the right programming language for your machine learning journey depends on various factors, such as your current skill set, project requirements, and personal preferences. Each language has its pros and cons, so it’s essential to evaluate them based on your unique needs.

To help you make an informed decision, we’ve created a quick breakdown to help you assess:

Programming Language 

Ease of Learning Performance Ecosystem Ideal For
Python High Moderate Rich Beginners, quick prototyping, various aspects of ML
R Moderate Moderate Rich Statistical analysis, data visualization
Java Moderate High Mature Large-scale systems, cross-platform applications
C++ Low High Mature Performance-critical applications
Julia High High Growing High-performance technical computing
Scala Low High Growing Big Data applications, concurrent programming

Now it’s up to you to choose the one that best aligns with your goals and start learning. Remember, staying updated on new technologies and trends is crucial for success in the ever-evolving world of machine learning.

Tips for Using ChatGPT for Coding

ChatGPT, developed by OpenAI, is not just a conversational AI; it can also be a fantastic tool to assist you in your coding journey, especially when diving into the realm of machine learning.

chatgpt homepagechatgpt homepagechatgpt homepage

Here are a few tips to make the most out of ChatGPT for programming:

Clarify Your Queries

When asking ChatGPT to help with coding, make sure your queries are clear and concise. The more specific your question, the better the answer you’ll receive. For instance, instead of asking "How to do regression in Python?", you might ask, "How do I implement a linear regression model using the sklearn library in Python?" Specificity is everything.

Use It for Code Reviews

ChatGPT can also be helpful for code reviews. You can input your code and ask ChatGPT for a review or suggestions for improvement. It can help you identify potential bugs, improve code efficiency, and adhere to coding best practices.

Get Help Debugging

Stuck on a tricky bug? ChatGPT can help. Describe the issue you’re facing, provide relevant code snippets, and ask for debugging help. Remember to give as much context as possible to get the best assistance. But you should always be prepared to double check this output via official documentation or other online resources.

Learn New Concepts

ChatGPT can also be used as a learning tool. Ask about machine learning concepts, algorithms, or coding principles, and ChatGPT can provide detailed explanations and examples. It’s like having a personal coding tutor available but again, always (always!) verify what it tells you. Though impressive, generative AI has a tendency to "hallucinate" and produce incorrect results so never rely on it solely to learn code. It works best if you have some understanding of these concepts already.

Will You Pursue Machine Learning?

Whether you’re a seasoned programmer or just starting out, machine learning is an incredibly exciting field that offers plenty of valuable opportunities. With the right language and tools, you can bring your project ideas to life and create something special. So why not give it a go?

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