Machine learning is an artificial intelligence and data science process wherein a computer algorithm is trained to read and interpret data without the intervention of a human operator. While the technology is primarily used for content recommendation algorithms on platforms like Netflix and Amazon, machine learning programs are becoming increasingly powerful, nuanced, and accessible. AI image recognition technology, for instance, has the potential to radically alter many industries and new developments in the field are happening regularly. High school students interested in computer science and artificial intelligence may want to learn more about machine learning algorithms to build the skills necessary to start working with this new technology.
1) NextGen Bootcamp: Python Data Science & Machine Learning Summer Program
NextGen Bootcamp offers a comprehensive Python Data Science & Machine Learning course available in-person at its Manhattan campus and live online in digital classrooms. Students enrolled in this class will learn how to read and write Python code and how to use major Python libraries, like Pandas and Matlib, for data science and analysis processes. These lessons aim to teach students the fundamental computer programming skills they need to work on any coding project. Python is the most popular programming language and one of the most accessible languages for first-time programmers, making this an ideal course to serve as an introduction to computer science. In addition to programming skills, students will learn how to create data visualizations, including maps, graphs, and charts, to help them communicate their findings to non-expert audiences. Students enrolled in this class will receive the introductory data science lessons needed to apply their knowledge to virtually any data-related project.
Once students are familiar with different kinds of datasets and databases, they will receive hands-on experience working with machine learning algorithms and understanding how they work. Machine learning algorithms use elaborate Python programming strings to organize and interpret the presented data, so students will need to understand how to write an algorithm and how to clean a dataset. Students will learn about various logical regression models that machines use to interpret data, including multi-linear and logistic regressions. Students will leave the course with an understanding of the technologies and theories that undergird machine learning applications, and they will be equipped to start working on writing and training their own algorithms.
Students regularly praised the accessibility and depth of the course. Since this is an introduction to computer science program, it is designed for students without prior knowledge of Python. Despite this, students spoke positively about the complexity of the instruction, with one student saying, “this class really helped to improve my programming skills and knowledge! It covers a good span of topics ranging from vanilla Python programming to machine learning.” Students spoke positively about how much they learned, with one student saying, “you learn a lot in a short time.” Any student looking to work with machine learning algorithms stands to benefit from enrolling in this program.
2) NextGen Bootcamp: Computer Science Summer Certificate Program
NextGen Bootcamp also offers a lengthier Computer Science Summer Certificate program which combines its Python Data Science & Machine Learning session with its Java Programming Summer session. This course provides students with a comprehensive introduction to computer science and programming, making this an ideal course for students who are confident they want to work in a tech field but want to learn broadly transferable skills. Java and Python are two of the most popular programming languages in the world and are among the most accessible programming languages for first-time coders to learn. These are also the languages that are most commonly utilized in high school AP computer science courses, making them productive to learn if you intend to complete those exams. This is a beginner-friendly program, so students without prior coding experience can learn the basics of computer and data science.
After students have completed their Java training, the course will replicate the lessons offered in NextGen’s Python Data Science & Machine Learning session. This means that students will learn how to work with large datasets, write queries and produce graphic visualizations of their data using Python libraries. Once students are comfortable working with datasets and Python, they will learn how machine learning algorithms work and how they are written. Students will become familiar with the logic behind machine learning programs and learn best practices for training algorithms to reduce bias or flaws in machine logic. By the end of the program, students will have learned the basics of artificial intelligence programming and gained the foundational skills they need to apply their training to virtually any future computer science course.
Students regularly praise the quality of the instruction and the ease with which they can pick up new skills. One student wrote, "the instructor was very nice and taught the information in a way that was easy to follow and understand.” Another student spoke positively of the structure of the course, writing that the course “laid out an easy-to-follow path for me to learn so much in just 15 days.”
3) Practical Programming: Python Machine Learning Immersive
Practical Programming offers a live online Python Machine Learning Immersive program for students 18 and over. This course aims to teach students the important Python programming concepts that they need to master to start programming productive and effective machine learning algorithms. Students will start by learning how to use Pandas and scikit-learn to clean and prepare datasets for a machine learning algorithm to read since the algorithm can’t produce useful feedback if it has biased or poor data. Then, students will learn the various logical regression analysis techniques that data scientists use to train machine learning algorithms. These kinds of logical statements are important since an algorithm needs to have a firm idea of the logic that it is using to read and interpret data. Finally, students will learn how to program decision trees and random forests that allow machine learning algorithms to produce new information from the data they read.
This course assumes students have a background in Python programming, so it isn’t the most beginner-friendly machine learning summer program. However, it is among the most immersive training programs a student can enroll in since it is aimed at computer science professionals looking to improve their programming skills. Students in this course will receive accelerated machine learning instruction, and by the end of the program, students will not only know how to write machine learning algorithms but they will also know how to train them and how to evaluate whether or not their algorithms and datasets are working correctly.
4) iDTech: Artificial Intelligence and Machine Learning with NVIDIA
Students interested in getting specific hands-on experience working with machine learning algorithms may want to consider enrolling in iDTech’s Artificial Intelligence and Machine Learning with NVIDIA live online program. In this course, students will learn how to write data science scripts using Python, and they will become familiar with the practical process of working with machine learning tools. As part of a student’s tuition, they will be mailed an NVIDIA® Jetson Nano™ neural network development kit that will let them get hands-on experience training and perfecting a machine learning program. The course is divided into two parts. In the first half of the course, students will learn how Python code is written and how it functions in machine learning contexts. The second half of the class will focus on learning how to use the Jetson Nano to train a neural network.
Students will get practical experience by creating and training an image classification neural network in Linux using their Jetson Nano. This involves using large image datasets and classification tags (as well as a lot of Python code) to train a neural network to automatically identify and classify the content of images. This can involve processes like teaching the machine to identify whether or not a photograph was taken indoors or outdoors and whether or not the image contains a human being. The possibilities of this technology are vast, and once students are comfortable, they will finalize and submit the algorithm they were using to train their Jetson for final review. Once approved, students will receive an industry-recognized certificate of completion directly from NVIDIA.
Students who would like an immersive, in-person experience can enroll in the residential two-week summer program, where they’ll live on a university campus and enjoy workshops and recreational activities with their peers. University labs are an exciting place to learn about this cutting-edge technology.
5) iD Tech: Machine Learning Academy: Coding Deep Neural Networks
Students who want to explore cutting-edge technology in a university lab should consider iDTech’s Machine Learning Academy: Coding Deep Neural Networks. This two-week course features small classes sizes and individual attention, so students can learn at their own pace whether they are beginners or more experienced coders. The residential two-week program balances the daily schedule between assignment-driven workshops and outdoor and recreational activities. Classes take a deep dive into machine learning technology. With the guidance of experienced instructors, students master industry-standard tools like Python, TensorFlow, and Keras. Using hands-on projects and experiments like crafting image recognition models and analyzing textual data from sources like Amazon reviews, participants witness firsthand the immense potential of machine learning algorithms.
This course helps students develop coding and computational thinking skills as they explore machine learning with Python, train models to learn without being directly coded, and optimize learning rates and algorithms. The course begins with the basics of Python coding. Students dive right into building programs by working on a player vs. player text-based game. They’ll explore NumPy, Matplotlib, and Pandas libraries, and learn how to implement neural networks and image recognition models with the MNIST database, CIFAR-10 dataset, and TensorFlow.
Next, students advance to analyzing webpage data and build a sentiment analysis network using Amazon reviews. They’ll hone their skills by creating a text generator and building a chatbot system. Teens leave the academy with new friends and mentors along with newfound resilience and critical thinking skills. They’ll receive an official iDTech diploma and course transcript that they can add to their college and job application materials, as well as course project files they can continue working with at home.
6) iDTech: Academy NEXT: Advanced Laboratory for Emerging Technologies
Academy NEXT: Advanced Laboratory for Emerging Technologies at iDTech is an opportunity for students to take their machine learning and AI skills to the next level. This application-only program is for advanced students who are ready to tackle real-world problems with radical solutions using breakthrough technologies. It’s a great opportunity for high school students to gain experience that puts them a step ahead in college or their career. During the two-week course, Academy NEXT students work together to plan and execute projects. They’ll leverage the latest in coding, artificial intelligence, data science, and machine learning to tackle pressing problems and devise solutions that make the world a better place. This course prepares students to be the leaders and changemakers of tomorrow.
Through independent projects of the student’s choice, they’ll learn advanced techniques in emerging technologies like AI and data science. Students learn to collaborate with a team to deliver meaningful projects and build a portfolio of tech-driven solutions. Teens learn in a lab on an inspiring college campus, where they’ll have access to the latest and best tools and mentors. After completing the course with a prototype and a pitch deck, students will receive a certificate of completion
7) Digital Media Academy: Applied Data Science & Machine Learning With Python
Digital Media Academy offers an online Applied Data Science & Machine Learning with Python course for students seeking practical, hands-on experience working on data science and machine learning projects. In this course, students will learn how to code and train neural networks to perform basic tasks independently. Students will learn the basics of programming using Python, and how AI programs are built, trained, and utilized in various fields. This course is a good place for students interested in AI development to learn the foundational skills they need to work in the field. Their Python and data science skills are widely applicable to other fields of computer science. Students enrolled in this program will build their own neural networks, and learn how machine learning programming skills can be applied to almost any professional industry.
The practical exercises students will work on in this course include building machine learning algorithms capable of playing simple games independently. Students will start by building a machine that can play simple puzzles and board games, and then they will train a neural network until it is capable of playing and completing classic Atari games without user inputs. Students will then work on training an LSTM algorithm to read the text and utilize sentiment analysis programming to determine the tone and tenor of the inputs. Students will complete the course by working on a project of their own design based on their interests within the course. This course is an ideal place for students to gain hands-on experience writing machine learning algorithms and developing an understanding of the uses and functions of those algorithms in various contexts.
8) New York University: Machine Learning
New York University is one of many colleges and universities that offer robust summer course options for high school students, including a Machine Learning offering. In this course, offered through NYU Tandon School of Engineering, students will spend two weeks at the NYU campus learning the basics of Python programming, data analytics, and machine learning. The course is taught by members of the NYU Engineering faculty who are present to provide students with expert instruction and personalized feedback on their programming work. Students will work with state-of-the-art technology and cutting-edge machine learning models to produce powerful and practical algorithms. Students will need some programming experience to successfully complete the program, but they don’t need any prior data science experience.
Students enrolled in the program will receive guided instruction on programming and training machine learning algorithms that serve a wide range of functions. Students will learn the programming logic and mathematical principles underpinning machine learning technologies, and they will learn how to think like programmers when approaching data science and machine learning problems. Every day, students will receive focused instruction from members of the NYU faculty, and they will spend the remainder of the day working on their own or in small groups responding to assignments and programming their own machine learning algorithms. Students will also learn how machine learning engineers approach real-world problems when designing and developing algorithms. This makes the course ideal for students interested in exploring AI technology's practical, social, and ethical ramifications. This is a non-credit course, but students who complete the class will have developed a portfolio of projects that they can include as part of their college application or job materials.
9) Stanford Summer Institute: Machine Learning
Stanford’s Pre-College Summer Institute offers a live online summer high school program, Introduction to Machine Learning, for students entering the 11th and 12th grades. Students in this course gain an overview of the machine learning process and an introduction to advanced techniques for extracting insights from large datasets. Students learn to use Python programming language to preprocess and visualize structured data. They’ll train, test, and tune machine learning models, and evaluate the performance of algorithms to select a final model for presentation.
Students should have prior exposure to a computer programming language and a working knowledge of statistics before taking this course. In addition to two hours daily of live online classes, students complete out-of-class learning assignments and engage in group projects. This course is excellent preparation for programs in game design and discrete mathematics.
10) Stanford Summer Institute: Artificial Intelligence
Stanford also offers a high school program in Artificial Intelligence. In this course, students will learn the theoretical and technical concepts that go into programming and training machine learning and artificial intelligence algorithms. Students will learn the mathematical theorems that programmers use to build machine learning logic, and they will learn how data is wrangled and cleaned before being fed to an algorithm. This is a vital step of the process, and this course emphasizes teaching students how bias enters large datasets and how that bias can taint the output of a machine learning algorithm. This course aims to give students the skills to program safe, efficient, and ethical AI algorithms with practical, real-world problem-solving functions.
This course will be divided between in-class instruction, during which students will receive college-level training from members of the Stanford faculty, and out-of-class work time, which includes pre-recorded lectures, homework and collaborative lab work. Students will work with real-world datasets, and be encouraged to consider the practical and real-world ramifications of machine learning technologies. Students will learn various methods of training machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning algorithms. This is an ideal course for students looking to learn about the theories and techniques that underlie machine learning technologies and who are interested in understanding the real-world implications of new artificial intelligence developments.
11) Columbia University: Big Data, Machine Learning, and Their Real World Applications
Columbia University offers a Big Data, Machine Learning, and Their Real World Applications course as part of their summer immersive program for high school students. In this course, students will learn the programming and data science skills they need to navigate the world’s newly emerging ‘data economy.’ To accomplish this, the course focuses on practical, business-focused data problems which students will learn to identify, plan for, and solve. Students will learn how to read, understand, and analyze data using programming languages like Python and data visualization tools like Tableau, making this course an ideal introduction to data science. This program has no formal prerequisites and is designed to be inviting to beginners, but a background knowledge of mathematics and basic computer programming skills will help students ease into the course assignments more easily.
Once students are comfortable with their understanding of data science, the course will turn to the real-world application of machine learning algorithms and their potential to impact business operations and other industries. Students will learn how to inspect data to ensure that it is clean and minimally unbiased and how to analyze the output of a machine learning algorithm to determine whether or not it is operating properly. Once students grasp machine learning, they will work with these algorithms to understand common techniques for utilizing machine learning technology to solve business and data problems. This practical course is useful for students considering applying to applied data science programs centering on the financial or business aspects of data science or for students hoping to use these tools in their business ventures.
12) Purdue University: Machine Learning for High School and Undergraduates
Purdue University is one of the country's most well-respected engineering schools. They offer a Machine Learning for High School and Undergraduates course through their Academy of Global Engineering program. In this online, one-week program, students will learn the basics of machine learning and the foundational Python programming skills utilized in machine learning projects. Students will be introduced to different data types, and learn how to write different conditional statements such as If-then-else, for-loop, and while-loop statements. Then, they will learn how to use these conditional statements to write linear regression functions that allow machines to ‘learn’ from the data they are fed. This program, which has been made possible through NSF funding, is open to students in 10th-12th grade or who are about to enter into their freshman year of college.
This course introduces machine learning models and is designed to help guide students as they pursue further training. Students will learn how machine learning models operate and how they interpret data, but they will not learn how to program a complete algorithm of their own fully, nor will they learn machine learning related artificial intelligence skills. This course is a great program for introducing yourself to the basics concepts that underscore machine learning projects and the ways in which algebra and Python are used in these projects.
13) InSpirit AI: Pre-College Artificial Intelligence Program
Offered in conjunction with Stanford University, InSpirit AI’s Pre-College Artificial Intelligence Program serves as a strong introduction for students interested in pursuing a college degree in computer science with an emphasis on AI programming. This program is taught by Stanford graduate students and is designed around experiential, hands-on learning pedagogy. Students enrolled in the course will start by learning the foundational theories underlying modern AI technology such as neural networks, decision trees, and logistic regression. Students will also learn how to write code in Python to begin writing AI and machine learning algorithms. In the second half of the program, students will collaborate with one another to build working AI projects that they will present at the conclusion of the course.
This course prioritizes the practical application of AI technology and the social implications of these projects. Students will learn how AI can function as a social good, and they will consider the practical and theoretical ethics and implications of these programs. Students will also spend the latter half of the program on college preparation activities, including an essay writing workshop for students compiling their application materials. This course serves as a practical and productive introduction to artificial intelligence programming.
14) Fordham STEM Bootcamp: Future Leaders of AI
Fordham University’s pre-college high school training programs include a Future Leaders of AI program that aims to teach students how to respond to the massive demand for AI applications and technologies. This course aims to provide students with an education in the practical and theoretical applications of Web3 technology. Lessons include working with chatbots and other AI applications to streamline productivity in schools and workplaces, AI art programs and their use in creative industries, and decentralization practices that utilize AI programs. This course aims to give students a broad overview of the applications and ethical considerations surrounding emerging artificial intelligence technologies.
This course emphasizes the relationship between AI technology and Web3 technology. To facilitate this, students will experience the course through Fordham University’s Metaverse platform and attend class using a personalized avatar. The course will provide students with hands-on experience writing code and working with AI technology. All students who complete the course will receive a certificate from Fordham University and a credential preserved on blockchain technology. Students looking to learn about the potential applications of AI technology may want to consider enrolling in a course like this to set themselves up for long-term success no matter where they end up working.
15) UC San Diego Extended Studies: Machine Learning for High Schoolers
This nine-month series of courses might be just the ticket for students who want to explore machine learning over a longer period. In UC San Diego Extended Studies course, Machine Learning for High Schoolers, students enjoy the flexibility of learning at their own pace. Participants delve into the foundational principles of machine learning armed with Python and essential mathematical tools. From mastering fundamental data types to exploring advanced algorithms, they'll gain a solid understanding of probability, statistics, linear algebra, and calculus. Students witness firsthand the practical applications of these concepts by completing Python projects that prepare them for real-world challenges.
To complete the program, students complete three core classes. Python and Mathematics for Machine Learning provides a focused introduction to foundational mathematics and programming constructs that are essential to machine learning. Students learn to work in the Google Collaboratory Integrated Development Environment and leverage Python for manipulating data, and they gain a solid grounding in probability and statistics, linear algebra, and calculus for machine learning. The second course, Machine Learning Algorithms, explores the complexities of constructing models, training, and testing, using common models for supervised and unsupervised learning algorithms. Students gain a working knowledge of Regression, Naive Bayes, K-nearest neighbors, K-means, and DBSCAN, learn dimension reduction techniques like Principal Component Analysis, and pre-process data using Linear Discriminant Analysis. Deep Neural Networks, the final course in the series, introduces students to the Artificial Neural Network (ANN). They’ll develop a Deep Neural Network (DNN) from scratch and apply it to classification and recommendation systems. In addition, they’ll use the framework to solve complex mathematical functions and specialized algorithms like Convolutional Neural Networks.
Why Study Machine Learning and Artificial Intelligence over the Summer?
There are many reasons that high school students looking to learn new skills over the summer should consider enrolling in a machine learning-related course. These programs blend theoretical and practical skills training and offer students the chance to learn the computer science skills they need to work on the cutting-edge of new technological developments. As more and more industries begin to experiment with machine learning algorithms and the tools used to train those algorithms to become more advanced, machine learning has the potential to upend a sizable portion of the economy, meaning that learning these skills early on will help prepare you for a future job market that rewards an understanding of how to solve complex data problems.
Machine learning and artificial intelligence summer programs also tend to teach Python and data science skills, as well as basic computer science lessons, meaning that the skills you learn in these courses will be easily transferable to other STEM projects. While students learning Python for data science will learn specialized libraries and frameworks, they will still be learning the syntax and grammar of the most popular programming language in the world. In addition, learning how to work with data is applicable in virtually any field since data science has become so ubiquitous across industries and professions. While students who go into other fields may not be directly applying their machine learning training, they are likely to find a use for the underlying data analytics skills that they need to hone to start working with machine learning algorithms.
A machine learning or AI course can also benefit students looking to pursue more advanced computer science training in their future education. Students enrolled in a summer training program will get the opportunity to practice their computer programming skills in a structured classroom environment, and they are likely to start learning advanced STEM concepts that they will explore in greater depth in a college program. These courses also provide students with evidence of their skills, often in the form of an official certificate of completion, that they can use to demonstrate their proficiency in computer science, making their applications to programming schools more competitive. Finally, many of these courses will help students test out of introductory computer science courses at many colleges and universities (though this varies greatly from program to program).
What Will Students Learn in a Machine Learning Class?
Machine learning is an advanced field of computer science that requires a diverse set of skills. Students who dive into courses without preparation might feel overwhelmed, or assume they are unable to learn the material. It’s more likely, though, that with a systematic approach and one-on-one support, the skills are attainable and highly rewarding.
Principles of Machine Learning
A course might start with a general introduction to the concepts of machine learning, like supervised learning, unsupervised learning, and reinforcement learning. Students gain an understanding of how algorithms learn from data to improve their performance over time.
Programming Language
Machine learning classes will introduce coding with a commonly used language like Python. Becoming a well-rounded coder makes it easier to tackle the techniques used in machine learning and AI. Python uses “libraries” like NumPy, Pandas, and Matplotlib for data manipulation and visualization. Coding is used to preprocess data, build machine learning models, and evaluate performance. Handling and processing data is the next crucial step. Students learn techniques for cleaning, transforming, and preparing data so it’s ready for training machine learning models.
Algorithms
Courses also cover machine learning algorithms. An algorithm is a set of rules or processes that an AI system uses to conduct a task. It may discover a new data insight or patterns, or predict output. Algorithms are trained using datasets to determine classifications or make predictions, and they can uncover insights in data mining projects.
Math
Machine learning is largely about math, so a course may go in-depth into mathematical concepts like statistics, linear algebra, probability theory, and differential calculus. Statics is at the heart of machine learning, and enables it to predict future events or data using probability. Linear algebra is used to create machine learning algorithms and to assess data collection efforts. Probability theory addresses the uncertainty in inaccurate or insufficient information, and makes it possible to account for these differences and still extract meaningful predictions. Students don’t need to be an expert in calculus, but it’s important to understand the fundamentals. Calculus is used to build models and apply machine learning.
Advanced Deep Learning
Advanced courses may dive into deep learning. This subset of machine learning focuses on training neural networks with multiple layers to learn complex patterns in data. Students explore deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and learn how to apply them to tasks like image recognition and natural language processing.
Real-World Projects
Hands-on projects are essential for learning and retaining these new skills. A machine learning class will usually include individual or group projects that apply what you’ve learned to hands-on challenges. Projects reinforce the theoretical concepts learned in class and build problem-solving skills, and experiential learning provides a deeper understanding of the challenges involved in machine learning. Working on real-world datasets and problems makes the material more relevant and meaningful and increases memory retention. Plus, completed projects provide tangible evidence of skills that set students apart in college applications and job searches.