10 Ways to Learn Machine Learning for High School Students

Machine learning is an increasingly popular field with applications across industries like healthcare, finance, education, and more. As it continues to be a sought after field of study and as a career path, high school students are looking to learn more about it. While it can be difficult to know how to get started on your machine learning journey, there are many resources and platforms out there that can help you.

Why Learn Machine Learning in High School?

As a high schooler, Machine Learning (ML) is an extremely valuable skill set to have. 

Here’s why - it is shaping a lot of industries, making it important for you to engage with it earlyon. It has the potential to equip you with the necessary analytical skills, computational thinking, and problem solving skills. In today’s data driven world, you will need to have a firm grasp on how to extract information from datasets, critically evaluate information, and make data-driven decisions.

If you are someone who is interested in machine learning and wants to pursue a career in it, or at the intersection of it, you should definitely consider getting a head start! It will help pave the way for stronger foundations, innovations, and career opportunities.

To get you started, we have compiled a list of 10 ways for high school students to learn machine learning. 

1. Enroll in Online Courses

One of the easiest ways to get started with data science? Take an online course! These courses  help introduce you to the fundamentals and key topics. You should take advantage of online platforms that offer machine learning courses specifically for high school students. Some helpful courses to get you started are: 

• Veritas AI - AI Scholars:  This 10-session bootcamp is great for beginners with no coding experience looking to learn the basics of artificial intelligence and machine learning. Through the course, you cover various topics like regressions, image classification, neural networks, NLP, and work on hands-on, application based projects in groups under the guidance of experienced mentors. You can apply for the program here. 

• Google - Machine Learning Foundational Courses: The foundational courses in machine learning offered by Google cover a vast range of topics including a thorough introduction to the different models, supervised learning, neural networks, data representation, and more. If you are someone who benefits from self-paced learning and testing what you have learned through exercises and quizzes, you should consider taking this course. 

• Coursera - Machine Learning: This course is best suited for students who are interested in building ML models using python. It covers various topics like supervised learning, logistic regression, and gradient descent. Note that this course is a good fit only if you have beginner-level coding experience.

• Fast.ai - Introduction to Machine Learning for Coders: This self-paced course is a good option if you have some coding experience and want to learn about important ML models. It teaches you how to create models, validate them, prepare data, and build data products.  

2. Join Online Communities and Forums

Engaging with online communities of other machine learning enthusiasts can help you learn more about concepts and gain access to valuable resources. Joining these communities helps you interact with others, have discussions about various ML topics, and ask questions for prompt responses.

Here are some you can join: 

• Reddit’s r/machine learning Community: This subreddit is dedicated to machine learning content that helps beginners learn concepts. It includes various resources like tutorials, research papers, news articles, project ideas, and discussions.

• Discord - MLSpace Machine Learning Community: This ML Discord is a community of machine learning enthusiasts which includes high school students. It helps you connect with others, share various resources, and engage with different machine learning topics. 
  
  Some other communities to look at are Stack Overflow and Learn AI Together. 

If you are interested in taking a look at projects others are working on and latest research, you should consider joining a community or forum. Additionally, if you are working on a machine learning project, you can share it to receive feedback from experienced individuals. However, you should note that through these communities, there is no direct teaching involved and you will have to take initiative to learn about concepts that excite you. 

3. Engage with ML Platforms 

There are many coding platforms and websites that provide tutorials available. These are good for students looking to engage in coding exercises, hands-on machine learning projects, and working with real-world datasets. Here are some useful platforms and tutorials that you should take a look at: 

• Kaggle: This is a useful platform for students keen on learning machine learning and data science. Kaggle gives you access to various tutorials that cover topics including data exploration, model validation, random forests, and more. Moreover, it lets you practice what you’ve learnt through coding exercises where you can write and test out the code on their platform to experiment with machine learning datasets and algorithms. Note that this is a self-learning platform and is a good fit for you if you enjoy learning and experimenting at your own pace!

• CodaLab: This is an open-source platform that provides students with opportunities to engage in computational research. This is helpful to work in a more efficient and collaborative manner. CodaLab gives you access to worksheets that helps you work through machine learning problems and lets you use any data format or programming language. They also host competitions for high school students to work together to tackle machine learning and computational challenges. 

4. Follow YouTube Channels

YouTube channels are a great way to engage in free, easy, self-paced learning and start at the very beginning. There are various channels and tutorials that cater to different levels of learning and knowledge. Check these channels out to get started:

• Sentdex: The channel is run by Harrison Kinsley who creates a range of programming tutorials about machine learning, data analysis, robotics, and python. These tutorials are specifically designed for beginners and intermediate programmers to build on their skills and knowledge. 

• DeepLearningAI: This channel is founded by Andrew Ng who founded Google Brain and is a well known ML & AI practitioner. DeepLearningAI offers a bunch of tutorials and lecture videos to help students understand various applications of machine learning. It also gives you access to interviews with experts in the field and live Q&A sessions. This is great if you want to stay updated with the latest trends and advancements in machine learning. 

• Data School: The channel is run by Kevin Markham who is a data science educator and the founder of dataschool.io. The focus of this channel is on making data science lessons accessible to students of all educational backgrounds and levels. He provides in-depth tutorials, resources, and webinars to help you build data science and machine learning skills. This is great if you want clear, easy to understand, and step-by-step explanations of concepts.

5. Participate in Machine Learning/Coding Competitions

Taking part in machine learning and coding competitions can help you apply what you already know and learn from others. It gives you access to a community of peers who are also interested in machine learning. You can take a look at these high school hackathons and coding competitions to get started - USA Computing Olympiad (check out some great resources for USACO here), Technovation Challenge, HPE CodeWars. You can check out a range of other computer science competitions for high school students here!

While competitions are great for exposure, you should keep in mind that they are extremely competitive and will require you to have some coding background. However, it is still worth the experience to take part in one! 

6. Learn Programming Languages 

One of the most fundamental ways to get started on your machine learning journey is to brush up on your coding skills by learning programming languages like python, C++, and R. You can check out different courses and platforms like Udemy, Educative.io, and edx that help you learn relevant languages and coding skills. 

Learning these languages helps you with essential skills to engage with, experiment with, and implement machine learning concepts. Programming languages are necessary to implement machine learning algorithms and to write code needed for data processing and model building. Additionally, since machine learning involves working with datasets, it becomes a prerequisite to data manipulation and analysis. 

7. Get an ML internship

Internships provide high school students with an opportunity to apply theoretical knowledge to hands-on projects and real-world applications. They give you access to a network of machine learning experts who can help you with resources, gaining new knowledge, practical experience, and career guidance. While internships are great value-adds to college applications, it is important to keep in mind that it is not easy to get ML internships in high school. They often require you to have a strong coding background and prior knowledge of certain concepts in machine learning. That being said, there are some organizations that offer selective internships to high school students.

• Ladder Internships: They are a selective program exclusively for high school students to work with startups. You should consider applying to Ladder Internships given the range of machine learning, computer science, and tech related opportunities they have. Students have previously worked on building a database of startups who are gamifying healthcare protocol, building backend and frontend hosts, and building technical MVPs using generative AI and tools like Figma. They have a range of different organizations and projects that you can check out on their application form. 

• SPARK Summer Internship Program (SPARKSIP): This is a great option for students interested in pursuing machine learning or computer science at the university level. The internship gives you the opportunity to work on hands-on projects that expose you to the real-world applications of machine learning, neural networks, computer programming, and computer vision. Moreover, you learn from industry experts who can help expose you to the latest trends in ML. 
  
  If you are interested in doing an internship in machine learning or computer science, you can check out this list of computer science internships for high school students! 

8. Join a High School Club

To gain exposure to topics in machine learning, the easiest way could be to join your school's AI, ML, computer science, data science, or robotics club. Not only will you get a chance to advance your skill sets, but also interact and collaborate with like minded peers. Some examples of clubs are Palo Alto High School Computer Science Club, Irvington High’s Computer Science Club, and TJ Machine Learning Club . Some of these clubs give you access to discord servers as well, enabling you to share ideas and projects and receive feedback. The Girls Who Code club is another option for high school students. 

If a machine learning or artificial intelligence club doesn’t already exist at your high school, you can create your own and build a community of machine learning enthusiasts! 

9. Contribute to Open Source Projects 

Open source is a software that is available for free and allows people to edit and distribute projects, encouraging collaboration and feedback. Contributing to open source projects helps build your machine learning skills in a practical way. You get a chance to interact with advanced coders and collaborate with them. As a high school student, you can also use open source contributions to add to your professional portfolio. By actively participating and working on different projects, you can showcase your coding skills, ability to problem solve, and work with teams. To get started, you can take a look at the following open source projects:

• DeOldify: A machine learning based project to colorize and restore old images and videos.

• Real - Time Voice Cloning: It is a machine learning and deep learning software that takes 5 seconds of somebody’s voice and is able to clone it to generate arbitrary speech in real-time.

• OpenFace: It is a machine learning based project that is able to recognize faces and is considered to be one of the simplest facial recognition APIs
  
  While considering this as a way to learn machine learning, you should note that open source contributions typically require a high level of coding skills, but it is still worth engaging in! 

10. Do a Research Project or Build a Personal Project

Research and personal projects are a great way to dive deep into machine learning topics and understand its various applications. It can help you identify which area (or intersection) of machine learning interests you! It is also a valuable addition to college applications if you are looking to pursue machine learning or computer science at university. There are programs that offer mentorship opportunities to high school students to work on independent research papers and personal projects that you should consider applying to. 

One such program is Lumiere Education, which is good for beginners looking to start their research journey in machine learning. Lumiere is a selective research program for high school students, founded by Harvard and Oxford PhDs. You get to work one on one with a PhD mentor to develop an independent research paper. They offer research opportunities across various fields, including machine learning, data science, artificial intelligence, robotics, computer science, and more! This is a good fit for those getting started on their ML and research journey and looking to do interdisciplinary research. 

However, you should keep in mind that this is suited for you only if you enjoy research and understanding the theoretical aspects of machine learning. If you are looking for technical skill development opportunities, then a hands-on personal project is a better fit for you. 

One such hands-on program is the Veritas AI Fellowship. Through this program, you get to work one on one with a mentor who has a background in machine learning to build a personalized project at the intersection of ML. By the end of the 12-week program, you would have built your own model, app, software, or research paper that showcases your unique abilities and interests. This is a great option for anyone who has some background in python and a clear area of focus within machine learning. You can check out some of the projects students have worked on in the past here. 

Image source: Lumiere Education Logo