If you are a high school student focused on getting into top universities and exciting careers, learning AI early can set you apart in a competitive field.

As AI continues to redefine industries from healthcare to entertainment, understanding its core principles early gives you a strategic advantage while preparing your resume for college applications or internships. 

Learning AI now shows that you're adaptable, forward-thinking, and ready to engage with the tech shaping tomorrow's world. These qualities don’t just enhance your college applications—they build a resume that signals readiness for innovation, making you stand out whether you're applying to internships, universities, or future jobs.

Why Learn Artificial Intelligence in High School?

Here’s why diving into AI now can be a game-changer while working on research projects, internships, or while preparing for college applications!

1. AI is a fast-growing field:

According to the World Economic Forum’s Future of Jobs report, AI is expected to be one of the most rapidly growing job sectors in the next five years. With industries like finance, healthcare, and technology adopting AI at breakneck speed, there has never been a better time to enter this field.

2. AI offers high-paying careers:

The financial rewards of working in AI are as attractive as the technology itself. AI professionals often rank among the highest-paid tech roles. According to Glassdoor as of September 2024, the average salary for an AI engineer in the U.S. is around $132,000 per year, and other AI-related roles, like machine learning engineers and data scientists, command even higher salaries. 

3. AI is intellectually challenging. 

By working in AI, you’ll engage in solving complex problems, designing systems that mimic human intelligence, and developing algorithms that process and analyze vast amounts of data. For students who enjoy tackling intricate challenges, AI offers the perfect blend of creativity, logic, and innovation. You’ll constantly be learning and evolving alongside the technology itself.

4. AI is reshaping every industry. 

Understanding AI equips you with the skills to work across sectors, whether you’re interested in finance, marketing, engineering, or even medicine. AI’s interdisciplinary applications give you the flexibility to apply your skills in virtually any field, making it one of the most versatile areas to pursue.

5. Early adoption gives you a competitive edge.

As universities and employers increasingly seek candidates with AI expertise, showcasing hands-on experience in this area can make your college application and resume stand out. The sooner you begin, the more time you have to deepen your understanding and work on real-world projects, positioning yourself as a leader in the field even before you enter college.

10 Ways to Learn Programming in AI as a High School Student

1. Start with the basics of programming

To kick off your journey in AI, it’s essential to start with the basics of programming. Mastering fundamental concepts like variables, loops, and data types is crucial, as these elements form the backbone of any coding language. This foundational knowledge is essential for students as it equips them with the skills needed to understand more complex AI concepts and frameworks. Python is particularly recommended for beginners due to its straightforward syntax and vast community resources.

Starting with the basics allows you to grasp key concepts such as variables, data types, loops, and conditional statements. By understanding these basics, you not only develop your problem-solving skills but also enhance your logical thinking, both of which are vital for programming AI solutions. 

Numerous online platforms offer resources for learning programming basics. Websites like Codecademy and Coursera provide interactive courses that guide students step-by-step through the learning process.

Resources for Learning:

• Codecademy’s Learn Python

• Coursera’s Python for Everybody

2. Engage in online courses and massive open online courses (MOOCs)

Engaging in online courses, especially MOOCs, is a vital strategy for enhancing your programming skills in AI. MOOCs provide structured learning paths and a wealth of resources, allowing you to delve deeper into specific topics at your own pace. They often feature comprehensive curricula designed by experts in the field, making them an ideal choice for both beginners and those looking to expand their knowledge.

1. Veritas AI - AI Scholars Program: A good place to start if you prefer an application-oriented approach are programs like the Veritas AI - AI Scholars Program. This boot camp consists of ten sessions designed for beginners, covering essential topics such as linear regression, image classification, neural networks, and natural language processing (NLP). The program culminates in team-based projects, allowing you to apply your newly acquired skills under the guidance of experienced mentors. You can apply for the program here.

2. Coursera: Another standout example is Andrew Ng’s Machine Learning course on Coursera. This course has gained immense popularity for its thorough exploration of the theoretical foundations of machine learning, along with practical coding assignments using MATLAB/Octave. Completing this course equips you with both the theoretical understanding and hands-on experience necessary to tackle real-world AI challenges. 

3. Work on real-world AI projects through research programs or internships

Working on real-world projects is vital for solidifying your programming knowledge in AI and enhancing your problem-solving skills. Engaging in real-world projects provides hands-on experience that reinforces what you've learned and helps bridge the gap between theory and practice. You'll develop a deeper understanding of AI methodologies, improve your coding skills, and learn how to approach real-world problems with a structured mindset. 

A good platform to start your journey is Kaggle, which offers a variety of datasets and challenges that cater to all skill levels. For beginners, the Titanic Survival Prediction challenge can be an interesting entry point. It requires you to clean data, perform exploratory analysis, and build predictive models. This project not only helps you practice data manipulation but also introduces you to important concepts like feature engineering and model evaluation. Engaging in such challenges allows you to learn from the community, compare your solutions, and even collaborate with others.

To dive into these opportunities, visit Kaggle Competitions. The platform hosts numerous competitions across various domains, enabling you to explore machine learning, data science, and AI applications. Additionally, Kaggle provides kernels (notebooks) and discussions where you can learn from the approaches of others, fostering a collaborative learning environment.

4. Read books on AI and programming

Reading books focused on AI and programming is essential for deepening your understanding and expanding your knowledge base. They provide comprehensive coverage of topics, allowing you to explore complex concepts at your own pace. They often include examples, exercises, and case studies that reinforce learning.

Resources:

• Consider reading "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This foundational text delves into the theory and practice of deep learning, making it a must-read for anyone serious about AI. 

• Another great choice is "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili, which focuses on practical applications using Python, a key programming language in AI.

You can also explore the latest recommendations on AI and programming books through sites like Goodreads or Amazon. These platforms offer user reviews and ratings to help you select the best books for your learning journey.

5. Join online communities in the field of AI 

Engaging with online communities is crucial for enhancing your learning experience in programming and AI. When you engage in discussions, you not only gain answers to your queries but also deepen your understanding of complex concepts. Moreover, being part of a community can keep you motivated and accountable in your learning journey.

Stack Overflow is a well-known platform where you can ask coding questions, troubleshoot issues, and find solutions shared by experienced developers. 

Another valuable platform is Reddit, specifically the r/MachineLearning subreddit, where enthusiasts and professionals discuss the latest trends, share resources, and seek advice on various AI-related topics.

6. Contribute to open-source projects

Contributing to open-source projects is an excellent way to enhance your programming skills while collaborating with others in the tech community. Engaging with open-source projects not only sharpens your coding abilities but also provides practical experience in collaborative development. You learn how to work with others, understand project structures, and navigate version control systems like Git. These experiences are invaluable in building your resume and demonstrating your commitment to real-world coding practices.

TensorFlow and PyTorch are two prominent open-source AI frameworks to which you can contribute. You might start by fixing bugs, improving documentation, or adding new features. For instance, TensorFlow's GitHub repository is a great place to find beginner-friendly issues labeled "good first issue," which can help you ease into contributing. Participating in these projects exposes you to best practices in coding and helps you understand how large-scale software development works.

You can also explore opportunities on GitHub by searching for open-source projects related to AI. 

7. Engage in camps, classes, and competitions in AI

Participating in specialized camps, classes, and competitions can significantly enhance your learning experience in AI and programming. These programs provide structured learning environments where you can gain hands-on experience with real-world applications. Camps and summer programs often focus on specific areas of AI, offering intensive training that goes beyond traditional classroom settings. Competitions, like hackathons and coding contests, push you to apply your knowledge under pressure, fostering problem-solving and teamwork skills.

Examples:

• Camps and Classes: Programs such as AI4ALL offer summer camps designed to inspire high school students to pursue careers in AI. These camps focus on ethical considerations and practical applications of AI, allowing you to work on projects and collaborate with peers. Similarly, most universities often offer introductory programming courses or summer workshops, which provide an in-depth understanding of foundational concepts. For example, Stanford AI4ALL, Beaver Works Summer Institute, MIT, NYU’s Summer Program on Machine Learning, and so on.

• Competitions: Participating in competitions like Google’s Code-in or Kaggle competitions can help you build a portfolio of work that showcases your skills. These events not only challenge you but also connect you with mentors and industry experts, providing invaluable networking opportunities.

8. Experiment with AI frameworks

Experimenting with various AI frameworks is crucial for gaining practical skills in artificial intelligence and machine learning. Familiarizing yourself with different frameworks helps you understand how AI models are built, trained, and deployed. Each framework has unique features and strengths, which can enhance your ability to select the right tools for specific projects. By experimenting, you not only deepen your technical expertise but also boost your problem-solving capabilities.

3 best AI frameworks to learn as a high school student

• TensorFlow: Developed by Google, TensorFlow is one of the most widely used frameworks for machine learning and deep learning. Start by exploring the official documentation and tutorials on the TensorFlow website. You can work on projects like image classification or text generation, which will give you hands-on experience in building neural networks.

• PyTorch: Known for its flexibility and ease of use, PyTorch is favored by researchers and developers alike. Engage with the PyTorch tutorials to get started on projects such as reinforcement learning or natural language processing. Many academic papers are published using PyTorch, making it a valuable tool for implementing cutting-edge algorithms.

• Keras: As a high-level API for TensorFlow, Keras simplifies the process of building and training models. Use Keras documentation to learn about its capabilities and try building simple neural networks for tasks like digit recognition with the MNIST dataset.

9. Stay updated with trends and research breakthroughs in the field of AI

Understanding the latest developments in AI can shape your learning and project choices, ensuring you focus on relevant skills and methodologies. The field is rapidly advancing, with new algorithms, frameworks, and applications emerging frequently. By staying informed, you can anticipate future trends and position yourself competitively in the job market, or even tailor your resume for college applications.

Resources:

• arXiv: Regularly reading research papers from arXiv can provide valuable insights into the latest AI research and methodologies. You can explore specific areas like natural language processing (NLP) or computer vision. 

• Blogs and Newsletters: Follow AI-focused blogs and newsletters, such as the Towards Data Science on Medium or the AI Weekly. These resources summarize important research papers, industry news, and practical applications, making it easier to stay updated.

• Podcasts and Webinars: Listening to podcasts like The TWIML AI Podcast or attending other webinars can provide in-depth discussions on recent advancements and practical applications in AI.

10. Develop a personal learning plan

Having a personal learning plan keeps you focused, organized, and motivated. By defining specific goals, you can measure your progress and adjust your strategies as needed. This approach helps you allocate time efficiently and ensures that you're building foundational knowledge while progressing toward more complex topics.

Start by setting clear short-term and long-term goals. For instance, you might aim to master Python basics within one month and complete a small AI project in three months. Break these goals into manageable tasks, such as completing online tutorials, practicing coding challenges, or reading specific chapters in relevant books.

Resources:

• Trello: Use Trello to create a personalized learning board. This tool allows you to organize tasks, set deadlines, and track your progress visually. You can create lists for different topics, such as programming fundamentals, AI algorithms, and project ideas, making it easier to stay on course.

• Google Calendar or Notion: Additionally, tools like Google Calendar can help you schedule dedicated study times, while Notion can serve as a more comprehensive workspace for notes, resources, and tasks.

One more way to learn programming for AI - Veritas AI

If you want to build a project/research paper in STEM and AI & ML, consider applying to Veritas AI! 

Veritas AI is founded by Harvard graduate students. Through the programs, you get a chance to work 1:1 with mentors from universities like Harvard, Stanford, MIT, and more to create unique, personalized projects. In the past year, we had over 1000 students learn AI & ML with us. You can apply here!

Image Source - Github Logo