--- title: "Machine Learning Competitions for High School Students" description: "AI and machine learning contests US high schoolers can enter in 2026-27: the USA-NA-AIO, IOAI, the Presidential SI Challenge and more, with rules and dates." canonical: https://learn.modernagecoders.com/machine-learning-competitions-for-high-school-students source: src/pages/machine-learning-competitions-for-high-school-students.html --- > AI and machine learning contests US high schoolers can enter in 2026-27: the USA-NA-AIO, IOAI, the Presidential SI Challenge and more, with rules and dates. In short The main machine learning competitions for US high schoolers in 2026-27 are the USA-NA-AIO (an individual, proctored AI olympiad; Round 1 on February 19, 2027, the route to IOAI 2027 in Singapore) and the Presidential SI Challenge (team AI projects; register by December 18, 2026). AI apps can also enter the Congressional App Challenge or a science fair. Modern Age Coders teaches the machine learning behind them live online. Learn it with a teacher ## Three courses that cover the machine learning these contests test From the Python and maths foundations up to models a student can train and defend. [![AI and Machine Learning for Teens course thumbnail](/images/ai-ml-teens.webp) MLCH / 01 AI and machine learning for teens From 13. Python to real models, with the maths explained. Open the syllabus →](/courses/ai-ml-masterclass-teens)[![Data Science for Teens course thumbnail](/images/data-science-teens.webp) MLCH / 02 Data science for teens From 14. NumPy, pandas and first scikit-learn models. Open the syllabus →](/courses/data-science-course-for-teens-python-data)[![Python for Teens course thumbnail](/images/python-teens.webp) MLCH / 03 Python for teens From 13. The language every ML contest here runs on. Open the syllabus →](/courses/python-complete-masterclass-teens) The routes ## Five places a US high schooler can compete with machine learning They differ in one big way: whether the student is tested on understanding, or judged on a project. Every row comes from that organiser’s own pages. | Competition | Type | Solo or team | Who can enter | Next milestone | | --- | --- | --- | --- | --- | | USA-NA-AIO (run by USAAIO) | Proctored olympiad: theory and coding | Individual | K-12 students in the US and Canada who meet the age and status rules | Round 1, Friday, February 19, 2027 | | IOAI 2027 | International olympiad week | National team | High school students chosen by their country; for the US, through the USA-NA-AIO | July 4 to 10, 2027, Singapore | | Presidential SI Challenge | Project, written report and video | One to four students and a Team Leader | High schoolers in grades 9 to 12 | Registration deadline December 18, 2026 | | Congressional App Challenge | An app, which may use AI, with a demo video | One student or a team of four at most | Students in middle or high school whose district takes part | The 2026 deadline is October 26, 2026 | | Science fairs leading to Regeneron ISEF | Research or engineering project, judged in person | Set by each fair | Through a Society-affiliated fair | Set by each local or state fair | ### Olympiad or project? The olympiad suits a student who likes exact answers: deriving an estimator, computing a backpropagation step by hand, or writing a training loop that runs on a CPU within the time. There is one score and it is individual. Project contests suit a student who likes a messy real problem, can work in a team and can explain a model to a judge who is not a specialist. Accuracy still matters, but so do the data, the safety thinking and the presentation. ### Why both are worth doing Olympiad preparation forces the theory. Project work forces the judgment about when a model is good enough and when the data is the real problem. The calendars barely clash: SI Challenge projects are due in December, and the olympiad’s first round is in February. Sources: [USAAIO 2027 USA-NA-AIO](https://usaaio.org/2027-usa-na-aio), [IOAI](https://ioai-official.org/), [Presidential SI Challenge](https://orise.orau.gov/si-challenge/about/eligibility.html), the [Congressional App Challenge](https://www.congressionalappchallenge.us/students/rules/) and [Society for Science](https://www.societyforscience.org/isef/categories-and-subcategories/), checked 17 October 2026. Modern Age Coders is independent of every organiser named here. The AI olympiad ## The USA-NA-AIO: how the 2027 AI olympiad works The USA-North America AI Olympiad is run by USAAIO, which also organises Team USA for the international AI olympiads. Its 2027 rules were posted on September 9, 2026. | Item | As USAAIO publishes it | | --- | --- | | Eligibility | Age: under 20 years old by August 30 of the competition year. Status: a US or Canadian citizen or permanent resident, or a full-time middle or high school student studying in the US or Canada. Not a full-time university student | | Format | "It is an individual competition." Every round is taken onsite with a proctor | | Registration | June 1, 2026 to January 31, 2027 | | Round 1 | Friday, February 19, 2027, from 12:00 p.m. to 3:00 p.m. U.S. Eastern Time, at schools or approved test sites | | Part 1 | Non-coding, multiple choice or fill in the blank, 60 minutes, with the contest app locked down | | Part 2 | Coding, 75 minutes, written and run in Google Colab | | Hardware | "All code must run on CPUs. In Round 1, GPUs are neither required nor allowed." | | Monitoring | Required monitoring software, a single monitor, and an OBS Studio recording of the full screen and the student’s face | | After Round 1 | Round 2 in March or April 2027 for qualifiers, some problems may need GPUs; a camp in June 2027 | ### Where it leads USAAIO says the USA-NA-AIO is the only pathway for students to qualify for Team USA at the international AI olympiads. IOAI 2027, the fourth International Olympiad in AI, is set for July 4 to 10, 2027 in Singapore. USAAIO also sends a team to the International AI Olympiad (IAIO); its 2027 edition is listed for Vietnam from February 25 to March 2, 2027, with that team already chosen. ### Practical points for families Students register themselves, then find a proctor; the proctor’s details are only collected during the contest. A school teacher, or an approved site such as a library or testing center, can host. Because the coding part is in Colab with recording software running, a student should rehearse the whole setup on the same laptop at least once before February. Sources: USAAIO [2027 USA-NA-AIO](https://usaaio.org/2027-usa-na-aio), [About USA-NA-AIO](https://usaaio.org/about-usa-na-aio) and [home page](https://usaaio.org/); [IOAI](https://ioai-official.org/); checked 17 October 2026. What is tested ## The USA-NA-AIO syllabus, topic by topic USAAIO publishes a syllabus for the whole competition and says which parts Round 1 covers. It is a useful map of what high school machine learning looks like at contest level, whether or not a student enters. | Area | Examples USAAIO lists | In Round 1? | | --- | --- | --- | | Markdown in Google Colab | Text cells, code snippets and typeset maths | Yes | | Mathematical foundations | Linear algebra, Bayes’ rule and Hoeffding’s inequality, multivariable derivatives, gradient descent and duality | Yes | | Basic coding | Python, NumPy, pandas, matplotlib, seaborn, scikit-learn | Yes | | Machine learning | Linear and logistic regression, support vector machines, decision trees, kNN, ensembles, cross-validation; k-means and PCA | Yes | | Deep learning in PyTorch | Multilayer perceptrons, batch normalization, dropout, backpropagation by hand | Yes, including basic CNNs | | Transformers and NLP | Attention, tokenization, embeddings, pre-training and fine-tuning | Round 2 and later | | Computer vision and generative AI | Object detection, UNet, autoencoders, GANs, diffusion models | Round 2 and later | ### Two rules worth noticing Deep learning answers "must be programmed with PyTorch rather than TensorFlow", in line with IOAI. A student who learned TensorFlow first should switch early. USAAIO also expects theory and code together. Its own example is that knowing the theory without being able to build a PCA class from scratch in NumPy is not enough, and the reverse is not enough either. ### How we read it for a grade 10 student The maths is the long pole. Most of the Round 1 list is reachable for a strong grade 10 or 11 student, but eigenvalues and multivariable derivatives usually have to be taught ahead of the school curriculum. In USAAIO’s words, the AI Olympiad "is neither a pure math Olympiad nor a pure coding Olympiad". Our plan below splits each week between the two for that reason. Source: [USA-NA-AIO syllabus](https://usaaio.org/syllabus) and [2027 Round 1 rules](https://usaaio.org/2027-usa-na-aio), checked 17 October 2026. Project contests ## Project contests: the Presidential SI Challenge, apps and science fairs Here the model is a means, not the test. Judges want to know what problem it solves, whether it works, and whether the team understands its limits. | Point | What the official pages set out | | --- | --- | | Category | Grades 9 to 12, entered with a parent’s consent | | Team | 1 to 4 high school students and one Team Leader, who must be 21 or older | | Tracks | Standard, or Trailblazer for projects with nationwide impact that are also novel | | Hand-in | One PDF: a title page and up to 10 further pages in 12-point type or larger, plus a video of up to four minutes | | Original work | All content created specifically for the Challenge after August 1, 2026 | | Judging | At least three judges per eligible project, using rubrics; State Champions by state, category and track, with two each for California, Florida, New York and Texas | | Dates | Team Leaders must register teams by December 18, 2026, and the project upload deadline is December 23, 2026 (both 5:00 p.m. Eastern). State Champions are named between February 11 and 15, 2027; regional judging runs from March 4 to April 8, 2027; the national round is in Washington, DC from June 6 to 9, 2027 | ### The question that tests ML understanding Among the questions the report must answer is how the team tested or verified the accuracy of any SI-generated content, outputs or predictions, and how accurate they were. That is a machine learning evaluation question in plain words. A strong answer names the test data, explains why it was kept separate, gives a number, and says where the model fails. The kNN example below shows the smallest version of that habit. ### Two other doors for an ML project The Congressional App Challenge accepts apps built with AI, as long as every use of AI is declared in the entry. Our [guide to that challenge](/congressional-app-challenge-guide) covers the rest of its rules, and our [Presidential AI Challenge guide](/presidential-ai-challenge-guide) goes deeper on the SI Challenge. Science fairs are the research route. Regeneron ISEF 2026 used 22 categories, including Robotics and Intelligent Machines (ROBO), Software Design (SFTD) and Computational Biology and Bioinformatics (CBIO); each affiliated fair sets its own categories. The program now called the Presidential SI Challenge was launched as the Presidential AI Challenge. Its official site explains that the federal term changed from "Artificial Intelligence" to "Super Intelligence" under a September 29, 2026 executive order, so search results may show either name. Sources: [requirements](https://orise.orau.gov/si-challenge/complete-the-challenge/requirements.html), [competition tiers](https://orise.orau.gov/si-challenge/complete-the-challenge/competition-tiers.html), [important dates](https://orise.orau.gov/si-challenge/about/timeline.html), [ISEF categories](https://www.societyforscience.org/isef/categories-and-subcategories/). A model from scratch ## Build a k-nearest-neighbours classifier in NumPy Olympiad problems expect students to build simple models without a library doing the thinking. This example is ours: a complete kNN classifier in a few lines, on six made-up points, runnable on any laptop CPU. ***kNN from scratch with NumPy (ours)**Python* ``` import numpy as np def knn_predict(X_train, y_train, X_new, k=3): # distance from every new point to every training point d = np.linalg.norm(X_new[:, None, :] - X_train[None, :, :], axis=2) nearest = np.argsort(d, axis=1)[:, :k] # indices of the k closest votes = y_train[nearest] # their labels return (votes.mean(axis=1) > 0.5).astype(int) X_train = np.array([[1.0, 1.0], [1.5, 2.0], [2.0, 1.2], [6.0, 5.5], [7.0, 6.0], [6.5, 7.0]]) y_train = np.array([0, 0, 0, 1, 1, 1]) X_new = np.array([[1.8, 1.5], [6.2, 6.1], [4.0, 3.8]]) print(knn_predict(X_train, y_train, X_new, k=3)) # [0 1 0] print(knn_predict(X_train, y_train, X_new, k=1)) # [0 1 1] ``` Run with Python 3 and NumPy. The two outputs differ only in the third point, which sits between the two clusters. | Training point | Label | Distance | Rank | | --- | --- | --- | --- | | (6.0, 5.5) | 1 | 2.62 | 1 | | (1.5, 2.0) | 0 | 3.08 | 2 | | (2.0, 1.2) | 0 | 3.28 | 3 | | (7.0, 6.0) | 1 | 3.72 | 4 | | (6.5, 7.0) | 1 | 4.06 | 5 | | (1.0, 1.0) | 0 | 4.10 | 6 | ### What the example teaches With k = 1, the single closest point has label 1, so the prediction is 1. With k = 3, the three closest are labels 1, 0 and 0, so the majority vote is 0. Same data, same code, different answer, because of a setting the student chose. That is the core idea behind cross-validation, which is on the Round 1 syllabus: choose k by testing on data the model has not seen, not by what looks right. ### Three extensions to try Scale the second feature by 10 and run it again. Why do the predictions change, and what does that say about normalising features? Replace the vote with a distance-weighted vote, then write the same classifier as a small PyTorch function instead of NumPy. Hold out two of the six points, choose k on the other four, and report accuracy on the held-out two. Eighteen weeks ## An 18-week plan to USA-NA-AIO Round 1 Our plan for a student starting in mid-October 2026 with solid Python and algebra. About four hours a week, one of them a live class. February 19, 2027 is roughly 18 weeks away from the date this page was checked. | Weeks | Maths | Code | Done when | | --- | --- | --- | --- | | 1 to 3 | Vectors, matrices, dot products | NumPy arrays and broadcasting | The kNN example runs and the student can explain each line | | 4 to 6 | Derivatives and gradient descent | Linear and logistic regression from scratch | A loss curve that goes down, plotted in matplotlib | | 7 to 9 | Probability, Bayes’ rule | scikit-learn models, cross-validation | k chosen by validation, not by guessing | | 10 to 12 | Eigenvalues, PCA | A PCA class in NumPy; k-means | PCA output matches scikit-learn on the same data | | 13 to 15 | Backpropagation by hand | PyTorch MLPs, dropout, batch norm | One backward pass computed on paper and checked in code | | 16 to 17 | Review | Basic CNNs in PyTorch on CPU | A small image model trained inside the time | | 18 | Rest and rehearsal | Full mock in Colab with recording software on | Setup tested on the contest laptop | ### Grade 9 Start the plan for next year instead; use this year for Python, NumPy and algebra. ### Grades 10 and 11 The plan above, plus an SI Challenge project in December if time allows. ### Grade 12 Check the under-20 and not-at-university rules; a project contest may fit better. Where teens start ## Four levels between using AI and competing in it A teen new to ML competitions is usually at level 1 or 2. | Level | In practice | | --- | --- | | 1. User | Uses AI apps; has not trained a model | | 2. Library user | Can call scikit-learn fit and predict on a dataset | | 3. Builder | Can write a simple model from scratch and explain the maths | | 4. Evaluator | Can show, with held-out data, how good a model is and where it fails | ### For parents Level 2 to level 3 needs maths that many schools teach later. That is normal, and it is the part we spend most time on. Ask your teen how they know their model works. A clear answer is a better sign than any accuracy number. ### For students Write one model from scratch before using a library for it. You will understand the library far better afterwards. Keep a notebook of every experiment, including the ones that failed. Project judges ask about those. The catalogue ## Nine courses for teens heading into ML competitions Live online, one steady teacher per class, in a group or one to one. I ### Foundations Python and maths MLCH / FND / 01 #### Python for teens Clean code before models. [Open the syllabus](/courses/python-complete-masterclass-teens) MLCH / FND / 02 #### High school mathematics Algebra to calculus. [Open the syllabus](/courses/complete-high-school-mathematics-mastery) MLCH / FND / 03 #### AP Statistics Probability with real data. [Open the syllabus](/courses/ap-statistics-maths-exam-prep-course)II ### Machine learning Models and evaluation MLCH / ML / 01 #### AI and machine learning From regression to neural nets. [Open the syllabus](/courses/ai-ml-masterclass-teens) MLCH / ML / 02 #### Data science for teens Data, plots and first models. [Open the syllabus](/courses/data-science-course-for-teens-python-data) MLCH / ML / 03 #### AP Calculus Derivatives behind gradient descent. [Open the syllabus](/courses/ap-calculus-exam-prep)III ### Projects For challenge entries MLCH / PRJ / 01 #### App development Put a model inside an app. [Open the syllabus](/courses/complete-app-development-masterclass-for-teens) MLCH / PRJ / 02 #### Vibe coding for teens AI-assisted builds, disclosed. [Open the syllabus](/courses/vibe-coding-for-teens-python-web-ai-projects-course) MLCH / PRJ / 03 #### Cybersecurity competitions Includes AI security puzzles. [Open the syllabus](/courses/picoctf-cyberpatriot-cybersecurity-competition-prep-for-teens) How lessons run ## Live lessons built around theory and code Weekdays or weekends, flexible: choose your batch, or for a very personalised plan choose 1-on-1 at your convenience, weekend mornings or weekdays. Weekdays Evening batches across US time zones. Weekends Longer sessions suit maths-heavy weeks. 1-on-1 Paced to one student’s syllabus gaps. ### Maths on paper Derivations done by hand, then checked. ### Code in Colab The same tool the olympiad uses. ### From scratch first NumPy before libraries. ### Evaluation Held-out data on every model. ### Any time zone Live online across the US. ### Batch or solo A batch of fifteen to twenty, or private lessons. Student work ## AI and web projects by our students Models and apps our learners have built and shipped. See more in the [student labs](/student-labs). ![NutriLife AI nutrition coach project screenshot](/images/projects/nutrilife.webp) AI and ML #### NutriLife An AI nutrition coach that reads what you eat and works you toward a target. by Bhavya · [Open it](https://nutrilife-bhavya.lovable.app) ![Misti AI chatbot for maths and coding screenshot](/images/projects/misti.webp) AI and ML #### Misti A chatbot that answers mathematics and programming questions, built and deployed by a student. by Harshit · [Open it](https://misti.modernagecoders.com) ![GuardianX AI internet safety assistant screenshot](/images/projects/guardianx.webp) AI and ML #### GuardianX An assistant that helps a young person recognise unsafe situations online. by Vivaan · [Open it](https://guardianx-cybersaver.lovable.app) ![SkyCast weather forecast application screenshot](/images/projects/skycast.webp) Web app #### SkyCast A weather forecasting site with live conditions for any location. by Krish Fees ## Fees The competitions set their own entry terms; we charge only for teaching. Lessons are paid monthly in US dollars and can stop at any month end, which suits a block of preparation before a round. Free first class USD 0 no card required - A free first lesson - Bring a model or a question - No card details Book it Group batch USD 100 a month, billed in US dollars - Fifteen to twenty learners - One regular teacher - Choose your batch - Monthly Start here One to one USD 150 a month, billed in US dollars - A private teacher - Built around the syllabus - Your own projects Enquire What families say ## Rated 4.9 across 547 Google reviews Parents’ Google reviews, as they wrote them. ★★★★★ "The one step solution for my son. Modern Age Coders make learning coding so simple that kids love it. The teachers explain complex concepts clearly with practical exercises and interactive content." Ria Mukherjee Parent ★★★★★ "Modern Age Coders has been a game-changer for me. I struggled to grasp IT concepts and coding before joining, but their classes transformed everything. I can now confidently write complex programs with ease." Samriddha Mondal Student ★★★★★ "One of the most wonderful education centres out there. Education is not limited to school syllabus but focuses on skill development." Vansh Agarwal Student ★★★★★ "My child Dhairya is really enjoying the Modern Age Coders classes. This is his first online class and he eagerly looks forward to it. I can already see his improvement, and the teachers are very cooperative." Sonam Oswal Parent of Dhairya ★★★★★ "Modern Age Coders have wonderful teachers who teach in a clear, easy and practical way. The teacher boosts students' confidence and inspires them to learn without hesitation." Sonu Goyal Parent ★★★★★ "I highly recommend this computer coding class! The teachers are incredibly knowledgeable and passionate about coding." Ritu Kedia Parent Questions from families ## ML competitions for teens: what families ask ### Which ML contests are open to US high schoolers? In 2026-27 the main ones are the USA-NA-AIO (the AI olympiad that selects Team USA for IOAI) and the Presidential SI Challenge for team projects. AI apps can also go to the Congressional App Challenge, and research projects to science fairs on the way to Regeneron ISEF. ### When is the 2027 USA-NA-AIO? USAAIO has scheduled Round 1 for Friday, February 19, 2027, from 12:00 p.m. to 3:00 p.m. U.S. Eastern Time. Registration runs until January 31, 2027, and Round 2 is in March or April 2027. ### Who can take the USA-NA-AIO? A US or Canadian citizen or permanent resident, or a full-time middle or high school student studying in the US or Canada, who is under 20 by August 30 of the competition year and is not a full-time university student. ### Do I need a GPU for the AI olympiad? Not for Round 1. USAAIO says all Round 1 code must run on CPUs and GPUs are neither required nor allowed. Some Round 2 problems may require GPUs. ### Should I learn PyTorch or TensorFlow? For the USA-NA-AIO, PyTorch. Its syllabus says deep learning problems must be programmed with PyTorch rather than TensorFlow, in line with IOAI. ### Where is IOAI 2027? The IOAI site lists the fourth International Olympiad in AI for July 4 to 10, 2027 in Singapore. US students reach it through the USA-NA-AIO. ### Is the Presidential AI Challenge the same as the Presidential SI Challenge? Yes. The official site says the name changed after a September 29, 2026 executive order. High school teams of one to four register by December 18, 2026 and submit by December 23, 2026. ### What times do your classes run? Weekdays or weekends, flexible: choose your batch, or for a very personalised plan choose 1-on-1 at your convenience, weekend mornings or weekdays. ### What about Kaggle competitions? Kaggle pages could not be read as plain text when we checked, so we do not describe their rules here. Each Kaggle competition has its own rules page; read the eligibility section with a parent before entering. ### Are you part of USAAIO or the SI Challenge? No. We have no link with any organiser. We teach the machine learning, maths and Python these competitions draw on; registration is always directly with the organiser. Elsewhere on this site ## Keep reading Other guides for US teens working with AI. ### [AI projects for high schoolers](/ai-projects-for-high-school-students) Project ideas with real data. ### [AI and ML classes in the USA](/ai-and-machine-learning-classes-in-usa) Our classes for US students. ### [Congressional App Challenge](/congressional-app-challenge-guide) Rules for an AI-powered app. ### [Presidential AI Challenge](/presidential-ai-challenge-guide) The SI Challenge rules in depth. ### [picoCTF for beginners](/picoctf-for-beginners) Including its AI security puzzles. ### [Coding classes in the USA](/coding-classes-in-united-states) The full list of US pages. ### [AI courses for high school students](/ai-courses-for-high-school-students) AI courses for US high schoolers: school, free online and live, compared on what they actually teach. ### [AI classes for middle school students](/ai-classes-for-middle-school-students) AI classes for US middle schoolers: what school covers, what a good class adds, and a first model in Python. ### [AI projects for middle school students](/ai-projects-for-middle-school-students) Ten AI projects for US middle schoolers, two in real Python, and how to take one to a science fair. Start here ## Book a free first lesson Share what your teen has built or studied in AI so far. The teacher works through one small model with them and maps the gaps against the olympiad syllabus or a project idea. Before booking, you can read [how we teach](/how-we-teach) and browse the [course syllabi](/courses). [WhatsApp us](https://wa.me/919123366161?text=Hello%20Modern%20Age%20Coders%2C%20my%20teen%20in%20the%20US%20is%20interested%20in%20AI%20and%20machine%20learning%20competitions.%20Could%20we%20book%20a%20free%20first%20lesson%3F) · [+91 91233 66161](tel:+919123366161) · [connect@modernagecoders.com](mailto:connect@modernagecoders.com) Our teachers teach from India over live video, so classes are online only. Families in the US usually reach us fastest on WhatsApp. ### Want a class sooner? Book a priority demo A priority demo is a full class of about 45 to 60 minutes. After the class the mentor sends a written skill report and a personal learning roadmap on WhatsApp. If you enrol, the demo fee is adjusted against your first month's fee. Share your demo Order ID with us on WhatsApp and we take it off. [Book a priority demo](/book-demo) ## Keep exploring Modern Age Coders ### Coding classes in nearby places - [Machine Learning Course for Beginners](/machine-learning-course-for-beginners) - [Coding Classes in Zwolle](/coding-classes-in-zwolle) - [Python for Kids & Teens](/python-and-ai-classes-for-kids-teens) - [Coding Classes in Zuid-Holland](/coding-classes-in-zuid-holland) - [Python Games for Kids: Code Your First Game, Then Pygame](/python-games-for-kids) - [Coding Classes in Zoetermeer](/coding-classes-in-zoetermeer) - [Coding Classes in Amsterdam-Noord](/coding-classes-in-amsterdam-noord) - [Vibe Coding and AI Agents Classes in Armadale, Perth](/vibe-coding-and-ai-agents-classes-in-armadale-perth) - [Coding, Cyber and AI Classes in North Dakota](/coding-and-ai-classes-in-north-dakota) - [AI and Programming Classes in North Sydney, NSW](/ai-and-programming-classes-in-north-sydney-sydney) - [Coding Classes in Drenthe](/coding-classes-in-drenthe) ### Learn more - [AI Certification for High School Students: The Exams Compared](/ai-certification-for-high-school-students) - [AI Research Programs for High School Students: 2026 to 2027](/ai-research-programs-for-high-school-students) - [Coding Competitions for High School Students: 2026-27 Guide](/coding-competitions-for-high-school-students) - [AI Summer Programs for High School Students: 2027 Guide](/ai-summer-programs-for-high-school-students) ### Free resources - [AI & Machine Learning Tutorial: Basics to Deep Learning](/resources/ai-and-machine-learning) - [Data Preprocessing and Exploratory Data Analysis](/resources/ai-and-machine-learning/data-preprocessing-and-eda) - [Ensemble Methods](/resources/ai-and-machine-learning/ensemble-methods-and-boosting) - [Introduction to Neural Networks](/resources/ai-and-machine-learning/introduction-to-neural-networks) ### From the blog - [AI & Machine Learning Guides](/blog/topic/ai) - [IGCSE 0478 Pseudocode: The Rules Examiners Mark](/blog/igcse-computer-science-0478-pseudocode-guide) - [String Handling in Java: The ICSE and CBSE Guide](/blog/string-handling-in-java) - [Why Every Teen Should Learn Coding: The Ultimate Guide for 2025](/blog/why-every-teen-should-learn-coding) ### Start here - [Book a free demo class with Modern Age Coders](/book-demo) - [Real projects built by Modern Age Coders students](/student-labs) Free class[Priority demo](/book-demo)[Call us](tel:+919123366161) --- *Canonical: https://learn.modernagecoders.com/machine-learning-competitions-for-high-school-students*