Starting a career in artificial intelligence/machine learning (AI/ML) requires mastering programming—but the real purpose is to be able to work with and manipulate large sets of information. With so many programming languages to choose from, it’s easy to feel overwhelmed, but it’s helpful to understand the distinction between AI and ML.
While AI refers to the broader field of creating systems capable of reasoning, problem-solving, and learning, ML is a more specific subset that focuses on developing algorithms that allow machines to learn from data, examples, or behaviors. Basically, machine learning is a subset of the broader category of AI. As Kersting (2018) explains, this relationship is best visualized as concentric circles, with AI at the outer level and ML within it, further subdivided by deep learning (DL). Understanding this distinction can guide your approach, as the tools and techniques you use will depend on whether you’re solving general intelligence problems (AI) or applying learning-based methods to data (ML). While peer advice can be helpful, the variety of opinions often makes it difficult to know where to begin. Ultimately, there is no single “best language for machine learning or AI.” It depends on what you want to build, your background, and your interest in the field. The programming language you choose will be shaped by the problems you are solving and your goals. One thing is clear: if you are diving into AI/ML, you will be handling massive amounts of data. And that’s where Python shines.
Known for its clean, intuitive syntax and powerful libraries like TensorFlow and PyTorch, Python allows you to focus on core concepts and algorithms without getting lost in complex code. It’s designed to simplify the process, making it the perfect starting point for anyone entering the world of AI/ ML—especially those just starting out. Once you’re comfortable with Python, learning other languages like R, Java, or C++ can help expand your skill set and tackle more complex AI/ML challenges. But for beginners, Python’s accessibility and libraries make it an excellent first step in your journey. If you’re completely new to AI/ML or just curious about the field, Andrew Ng’s “AI For Everyone” on Coursera is a great place to start. Designed for beginners, this course requires no prior knowledge of mathematics or programming and is available for free. It provides a broad, intuitive overview of AI‘s capabilities and future potential, making it a perfect introduction for those who want to understand the basics without diving into technical details.
For those ready to dive deeper, the HarvardX “Computer Science for Artificial Intelligence Professional Certificate” is an excellent option. This five-month program on edX mirrors Harvard’s in-person CS50 course and offers a solid foundation in the fundamentals, specialized knowledge in a particular area, and hands-on experience with real projects. While the course covers multiple programming languages, it may be challenging if you are new to coding or not grounded in python before taking the course.
To build your programming skills, Codecademy offers interactive Python courses that allow you to practice at your own pace. Once you’re comfortable, you can use your skills on platforms like LeetCode. LeetCode features coding challenges and collaboration with other users to solve problems and share tips. It could be especially valuable for preparing for technical interviews, as some of the coding challenges are commonly seen at top companies like Apple, Amazon, Google, Microsoft, Meta, and others. The free version provides plenty of practice, but the paid plan offers real interview simulations, helping you prepare for those competitive job market. If you’re looking for a different collaborative approach or want to work with real data, Kaggle is another great free platform. It lets you work with real-world datasets and build machine learning models at your own pace. Kaggle’s vast community also provides support and resources, making it a fantastic place to deepen your understanding of AI/ML and apply your skills to real-life projects.
As you work through these platforms and complete projects, you can upload your work to GitHub. GitHub is an essential tool for showcasing a professional portfolio. Having your work publicly available on GitHub not only demonstrates your progress but also gives potential employers a tangible way to see your skills in action. Plus, it allows you to document your growth which speaks much louder than a certificate, as that might not fully demonstrate how well you can apply your knowledge.
The road to mastering AI/ML is as much about building your foundation in programming as it is about honing your ability to work with large data. Whether you’re exploring introductory courses like “AI For Everyone,” diving into more technical certifications like the HarvardX Professional Certificate, or practicing problem-solving abilities on platforms like LeetCode and Kaggle, the valuable thing is to stay curious and committed. The AI/ML field is vast and constantly evolving, and you’re learning journey will be unique—so embrace it and keep moving forward.
Tamara Mestvirishvili holds an MS in Biology from New York University, focusing on Bioinformatics and Systems Biology. With a BS from City College and post-baccalaureate studies at Columbia University, she is a bioinformatics analyst at NYU Langone Medical Center (NYUMC). She has mentored at the New York Academy of Science and is currently Science Communicator for LifeSci NYC.
This article was originally published in AWIS Magazine. Join AWIS to access the full issue of AWIS Magazine and more member benefits.