Most Popular Programming Languages for Machine Learning
There are thousands of programming languages available, with more being created every year. In this article, you’ll learn about the best programming languages for machine learning, including a step-by-step guide on how to learn machine learning.
Different programming languages have various uses, such as statistical computing, scientific computing, predictive modeling, and AI. This guide will discuss the most popular programming languages for machine learning and what programming languages machine learning professionals use.
What Is Machine Learning?
Machine learning (ML) is a branch of AI dealing with the statistical analysis of data to uncover patterns in large volumes of information. Its focus is creating computer systems that can identify patterns, learn from data, produce decision trees, and make conclusions. Machine learning takes an iterative approach to learning from data, which helps in automating the creation of analytical machine learning models.
ML manages and supervises many services, such as the recommendation systems of Facebook, YouTube, and Netflix. It is vital in today’s technology and can be found in search engines, social media feeds, and computerized voice assistants. ML also helps in areas including medical diagnosis and image recognition.
What Are Programming Languages?
What Programming Languages Do Machine Learning Professionals Use?
Best Programming Languages to Learn for Machine Learning
Which Programming Language Is Best for Machine Learning?
Python is a popular programming language for machine learning practitioners. It has an extensive machine learning library, classic ML algorithms, machine learning systems, high-level data structures, and analysis. There are many resources available if you want to learn Python for machine learning .
Many engineers consider Python the best choice for machine learning as it is easy to use, flexible, and has fewer possibilities for error. Python is the primary choice of programming language for natural language processing (NLP) and sentiment analysis.
C/C++ is another popular language in terms of machine learning. Embedded computing hardware and electronics engineers use it on near-the-hardware scalable machine learning projects, like IoT edge analytics projects.
Industrial maintenance, image classification, robot locomotion, IoT, and AR/VR are the range of machine learning projects that significantly utilize C/C++. Another use of C/C++ is to enhance existing machine learning applications and projects. Your language of choice for a machine learning project will often be determined by the type of project you are working on.
Java’s strength lies in its popularity and pervasiveness as a pioneering general-purpose programming language. Many companies use it to generate array-backed support systems or a Java virtual machine, as well as for desktop, mobile, and web applications.
Internal development teams of various companies and financial institutions consider Java as the programming language that is most suitable for financial analysis, network security, preventing cyber attacks, and fraud detection algorithms. Many companies prefer to have their enterprise developers create Java-based enterprise-focused applications.
R offers top-notch machine learning packages to fulfill most statistical and data visualization demands as a free, open-source programming language. Neural networks and neural network libraries, non-linear regression, advanced graphing, and linear algebra are all included in these packages.
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Usually, it is considered a complementary language for machine learning practitioners’ existing projects. Application areas for R include sentiment analysis, bioengineering, and bioinformatics. Biomedical statistics heavily use R inside and outside academia.
Which Programming Language Should I Learn First?
You should first learn Python as it is one of the best programming languages for machine learning. It is easy to use, has highly rated machine learning repositories, and contains a vast and powerful collection of machine learning libraries.
For enterprise-related employment, it is better to learn Java . Most companies prefer to have their enterprise developers create Java-based enterprise-focused applications. However, after you learn your primary language, it will often become easier to learn more.
Is it Possible to Choose the ‘Wrong’ Programming Language?
Yes, picking the wrong programming language is possible. Not every programming language is compatible with all projects. New technologies, programming languages, and frameworks are constantly created and developed in the fast and ever-changing technology sector every day. Research and scrutinize which language is best to use before you decide to apply it to your project.
How to Learn Machine Learning
Machine learning involves assessing and organizing data and executing tests and experiments. Machine learning techniques are generally monitored and optimized to help develop robust and effective machine learning systems. Below is a five-step list on how you can learn ML, so you can embark on your new career in this exciting field.
Learn a Programming Language
Programming language knowledge and proficiency are essential for a career in machine learning. Skills in one or two of the best programming languages for machine learning, such as Python and C++, are necessary to undertake various projects. If you’re wondering how to choose a programming language to learn, there are many online guides.
Python is one of the most popular programming languages for machine learning professionals. Python bootcamps are one way to master the fundamentals of programming quickly. There are also bootcamps that cover the basics of machine learning to help you get started.
Take a Machine Learning Course
Without a college diploma, machine learning is one of the highest-paying occupations available. However, completing an online course or bootcamp is still required. There are many highly regarded machine learning bootcamps and programs that allow students to learn and understand machine learning quickly.
Start Projects Using Machine Learning
Review and recreate simple machine learning projects from Scikit-learn and other comparable resources to understand machine learning better. This exercise will give you a better idea of how machine learning works. You can create your own projects that you may post online or include on your resume.
Try taking on a project that requires a simple machine learning algorithm and building it from the ground up. Don’t waste time collecting data for the training process if you’re a beginner. Use public repositories such as the UCI Machine Learning Repository and Quandl to get started.
Study Data Gathering Methodologies
Machine learning heavily relies on gathered data. Being familiar with different data gathering methodologies will help you collect the necessary data effectively and efficiently. With the right data, machine learning systems can perform tasks quickly and easily.
Apply for Machine Learning Internships and Apprenticeships
Competitions and personal projects are fun ways to learn. However, you’ll need appropriate work experience to develop the business-specific machine learning skills that many organizations want. A machine learning apprenticeship is a great way to start a successful career.
Internships or entry-level jobs in the field of machine learning are one way to gain experience. Moreover, in online job posts, you can apply for an entry-level position, such as junior machine learning engineer.
How to Learn Machine Learning: Top Resources
- Simplilearn . This is one of the leading bootcamps offering machine learning online courses. Its basic programs, such as its machine learning introductory course, are free. However, advanced courses like its applied machine learning program cost around $2,500.
- Sentdex . One of the best Youtube channels teaching machine learning is Sentdex. In this channel you can also access videos on neural networks, deep learning, and AI.
- The Stanford AI Lab Blog . This blog is one of the best machine learning blogs you can find. Its articles include a wide variety of topics, such as neural networks, scalable machines, and different machine learning algorithms.
- This Week in Machine Learning & AI (TWiML). This is a popular podcast about machine learning and AI. Machine learning engineers and professionals consider TWiML a trusted and insightful guide to all interesting and important machine learning and AI updates.
- Machine learning books . Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow 2.0 Book by Aurelien Geron-O’Reilly, is another excellent resource in machine learning. This book has detailed explanations of every essential machine learning concept. It even has an open-sourced code you can access and try out.
Ready to Break into Tech?
Machine learning is one of the fastest-growing industries with growing demand and high salaries. The US Bureau of Labor Statistics expects accelerated j ob growth of 22 percent from 2020 to 2030 for this field. Approximately 3,200 openings for computer and information professionals, including machine learning practitioners, are projected yearly for the next decade.
If you’re into data analysis, automation challenges, and algorithms, then machine learning is the right career choice for you. Moving large amounts of raw data, implementing algorithms, and then automating the data process for optimization will be your daily tasks once you build up the necessary programming skills.
The compensation is another reason why machine learning is a viable career path. One of the highest-paying engineering jobs is a machine learning engineer. According to Indeed, a machine learning engineer can earn an average wage of $132,651 annually. ML engineers in the Bay Area of San Francisco earn the highest ML average salary, which is around $193,485 per year.
Best Programming Languages for Machine Learning FAQ
ML engineers use three programming languages most frequently, C++, Java, and Python. Other machine learning programming languages they use are R, Lisp, and Prolog, which are essential languages to learn when getting into machine learning.
No, machine learning is not hard to study. Beginners can start with the basics, which are widely accessible online. Many learning models are available via the Internet for free and can be quickly set up on your computer system. You can perform exciting and vital machine learning tasks with simple linear or logistic regression models.
Good math skills are needed if you want to master machine learning. Linear algebra, statistics, and probabilities are fundamental parts of machine learning. Apart from math, you’ll need data analysis skills to crunch and collect valuable insights and uncover patterns in data.
Machine learning is used in a variety of industries, including finance, retail, transportation, oil, and gas. In the automotive industry, self-driving cars use machine learning. Fraud detection, identity theft prevention, the discovery of new energy sources, and truck route optimization are some of the other practical applications of machine learning.