AI Coding for High School Students
Quantum by Hatch is an pre-college enrichment program to prepare teens with the skills and portfolio to excel in tech after High School.
Start building apps from day 1
Earn a certificate in 10 weeks
25 certificates total
Future Proof Your Kid's Career Opportunities
By 2027, demand for AI specialists will jump by 40%, and the U.S. will face a shortfall of 6.1 million tech jobs by 2030.
Learning AI now means your teen will be equipped with the most sought-after skills, ensuring they stand out in college applications and future job markets.
The program focuses on custom projects that showcase students' skills, enhancing their portfolios with each certificate earned. These projects are vital for job college admissions and interviews, as they demonstrate real-world abilities over completed coursework.
Build custom AI apps
This program emphasizes creating custom projects that showcase students' skills, making their portfolios more impressive with each completed certificate.
Build problem solving skills
Building great products comes with building resilience to move through failure and communicate your journey to others.
Become an elite college applicant
Get a competitive edge in elite college admissions by having a unique, high-demand skill and project case study to showcase.
Personalized experience
Our platform adapts to each student’s needs for personalized learning journey that keeps them engaged and eager to explore more.
Self-paced learning path
Earn up to 25 certificates with over 350 hours of lessons. Choose which certificates to focus on and learn at your own pace, or with a mentor.
Developed by AI professionals
This course was developed in partnership with Ekkel AI to build practical skills that students can leverage in the future job market.
Aaron Z.
Verified Student ✔
Eliana R.
Verified Student ✔
Hrehaan M.
Verified Student ✔
How It Works
Unlike traditional coding courses that follow a one-size-fits-all approach, Quantum's 100-lesson learning path combines core general knowledge with tailored content for each student's unique "North Star" app.
Every lesson imparts essential coding skills and directly contributes to the development of the learner's app. Our coding and AI courses aim to equip middle and high school students for success in the future AI economy, fostering curiosity, creativity, and resilience.
Weekly Interactive Lessons
Learn live in a small class with a mentor dedicated to student success.
Team Based Activities
Work alongside a tight-knit team to accelerate progress and motivation.
AI Empowered Learning
Learn around the clock from anywhere with software that adapts to the student & creates a fun hands-on learning program.
Supportive Community & Mentors
Receive constant support from a thriving community of peers and expert mentors.
How It Works
Unlike traditional coding courses that follow a one-size-fits-all approach, Quantum's 100-lesson learning path combines core general knowledge with tailored content for each student's unique "North Star" app.
Every lesson imparts essential coding skills and directly contributes to the development of the learner's app. Our coding and AI courses aim to equip middle and high school students for success in the future AI economy, fostering curiosity, creativity, and resilience.
Start Module & Define App
Choose your own idea, or select from pre-defined options. It's all about getting experience building products.
Define App
Choose your own idea, or select from pre-defined options. It's all about getting experience building products.
Complete Lessons
Gre
Earn Certificates
Receive constant support from a thriving community of peers and expert mentors.
Repeat
Receive constant support from a thriving community of peers and expert mentors.
Learn to Build Anything with AI Code
Earn up to 25 certificates with over 350 hours of lessons. Choose which certificates to focus on and learn at your own pace, or with a mentor.
Lessons
Certifications to Earn
Core AI Coding Modules
Join an elite cohort.
Limited seats available
Made for students 13+ years old
Must know python
Learn From Brilliant Young Minds
Your child will be mentored by gifted youth who are trailblazers in the AI field at elite universities and also learned code at a young age.
Jack L.
Claire B.
Jacky Z.
Victor C.
AI Coding Skills for the Future
Earn up to 25 certificates across 7 modules.
- Python Basics for Data Science
- NumPy Fundamentals
- Data Manipulation with Pandas
- Data Visualization with Matplotlib and Seaborn
- Exploratory Data Analysis
- Data Preprocessing and Cleaning
- Introduction to Machine Learning Concepts
- Supervised Learning Overview
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines
- Naive Bayes and K-Nearest Neighbors
- Model Evaluation Metrics
- Model Selection and Tuning
- Regularization and Ensemble Methods
- Introduction to Unsupervised Learning
- Clustering: K-means
- Clustering: Hierarchical and DBSCAN
- Dimensionality Reduction: PCA
- Dimensionality Reduction: t-SNE
- Anomaly Detection Techniques
- Gaussian Mixture Models
- Evaluation Metrics for Unsupervised Learning
- Practical Applications and Projects
- Introduction to Neural Networks
- PyTorch Basics
- Feed Forward Neural Networks
- Activation Functions
- Loss Functions
- Backpropagation
- Gradient Descent and Optimization
- Regularization Techniques
- Putting it All Together
- Introduction to Convolutional Neural Networks (CNNs)
- Convolution and Pooling Layers
- Activation Functions and CNN Architectures
- Regularization Techniques for CNNs
- Data Augmentation for CNNs
- Transfer Learning with CNNs
- CNN Project - Part 1: Data Preparation and Model Design
- CNN Project - Part 2: Training and Evaluation
- RNN Fundamentals
- Recurrent Units and Backpropagation Through Time
- Vanishing and Exploding Gradients
- Gradient Clipping
- Long Short-Term Memory (LSTM) Networks
- Gated Recurrent Units (GRUs)
- Bidirectional RNNs
- Sequence-to-Sequence Models and Attention
- Implementing RNNs with PyTorch - Part 1
- Implementing RNNs with PyTorch - Part 2
- Introduction to Transformers
- Attention Mechanism
- Encoding and Decoding in Transformers
- Transformer Architectures: BERT
- Transformer Architectures: GPT
- Hugging Face Library: Introduction and Setup
- Fine-tuning Transformers with Hugging Face
- Transformer Applications and Case Studies
- Introduction to NLP and Text Preprocessing
- Tokenization with NLTK and spaCy
- Stop Word Removal with NLTK and spaCy
- Stemming and Lemmatization with NLTK
- Bag-of-Words Model with scikit-learn
- TF-IDF with scikit-learn
- Word Embeddings with Gensim - Word2Vec
- Word Embeddings with Gensim - GloVe and FastText
- Building a Feature Extraction Pipeline with scikit-learn
- Introduction to NLTK library
- Scikit-learn for text classification
- Sentiment Analysis: Lexicon-based approaches
- Sentiment Analysis: Supervised learning
- Named Entity Recognition basics
- Sequence labeling for NER
- Evaluating NER models
- Project: Sentiment Classifier
- Project: Named Entity Recognizer
- Probability and Statistics for Language Models
- N-gram Language Models with NLTK
- Introduction to Neural Networks and PyTorch
- Recurrent Neural Networks (RNNs) for Language Modeling
- Transformer Architecture and Attention Mechanism
- BERT: Bidirectional Encoder Representations from Transformers
- GPT: Generative Pre-trained Transformer
- Using the Hugging Face Transformers Library
- Evaluating Language Models
- NumPy Basics for Image Processing
- OpenCV Basics - Reading, Writing & Displaying Images
- Color Spaces in OpenCV
- Image Filtering - Convolution and Kernels
- Smoothing Images - Blur, Gaussian Blur, Median, Bilateral Filter
- Morphological Operations - Erosion, Dilation, Opening, Closing
- Image Gradients and Edge Detection
- Image Thresholding, Binarization and Adaptive Thresholding
- Contours and Moments
- Histogram Computation and Equalization
- Sliding Window and Region Proposals
- R-CNN and Fast R-CNN
- Faster R-CNN and Anchor Boxes
- Single Shot Detector (SSD)
- YOLO (You Only Look Once)
- Evaluation Metrics and Non-Max Suppression
- TensorFlow Object Detection API
- PyTorch torchvision for Object Detection
- Convolutional Neural Networks (CNNs) Review
- Fully Convolutional Networks (FCNs)
- Encoder-Decoder Architectures and Skip Connections
- U-Net Architecture
- Transposed Convolutions
- Mask R-CNN for Instance Segmentation
- Image Preprocessing and Augmentation
- Training and Fine-tuning Segmentation Models
- Evaluating Segmentation Results
- Introduction to Markov Decision Processes
- States, Actions, and Rewards in MDPs
- Transition Probabilities and the Transition Function
- Return, Discount Factor, and Horizons in MDPs
- Policies and Value Functions in MDPs
- Bellman Equations for MDPs
- Solving MDPs with Dynamic Programming
- Reinforcement Learning and MDPs
- Implementing MDPs in Python with NumPy
- Review of Markov Decision Processes
- Introduction to Q-Values and Q-Tables
- The Q-Learning Algorithm
- Coding Q-Learning from Scratch
- Exploration vs Exploitation Tradeoff
- Implementing Epsilon-Greedy
- Introduction to Deep Q-Networks (DQN)
- Coding DQN in PyTorch
- PyTorch Basics
- Policy Gradient Methods
- REINFORCE Algorithm
- Actor-Critic Methods
- Proximal Policy Optimization (PPO)
- AlphaGo Case Study
- AlphaZero Case Study
- Multi-Agent RL
- Deep Learning Review
- Autoencoder Architectures
- Latent Variable Models
- Variational Inference
- Evidence Lower Bound (ELBO)
- Reparameterization Trick
- VAE Loss Function
- Implementing VAEs in PyTorch
- Convolutional VAEs
- Conditional VAEs
- Gaussian Noise and Denoising
- Markov Chains
- Neural Networks Review
- Forward Diffusion Process
- Reverse Diffusion Process
- Denoising Diffusion Probabilistic Models (DDPM)
- Denoising Diffusion Implicit Models (DDIM)
- Training and Sampling Diffusion Models
- Introduction to Modern AI Models
- Language Models (LLMs) - GPT-4, Claude-3, Gemini, LLaMA
- Image Generation Models - Stable Diffusion, Midjourney, DALL-E
- Video Generation Models - Sora, Pika, Moonvalley
- Prompt Engineering
- Fine-tuning and Adapting Models
- Combining Models and Modalities
- Ethical Considerations and Limitations
- Deploying Models in Production
- Working with Context and Context Windows
- Generating Chat Completions and Formatting JSON Output
- Calling Functions and Parsing Model Output
- Optimizing and Managing Token Usage
- Leveraging Vector Embeddings for Semantic Search
- Fine-Tuning Models with Custom Datasets
- Prompt Engineering for Few-Shot and Zero-Shot Learning
- Implementing a Complete AI-Powered Application
- Introduction to Internships
- Identifying Your Interests and Goals
- Crafting the Perfect Resume
- Writing Impressive Cover Letters
- Searching for Internship Opportunities
- Networking for Internship Success
- Contacting Potential Internship Hosts
- Acing the Internship Interview
- Strategies for Internship Success
- The Importance of Projects for Landing Jobs
- Choosing the Right Projects to Work On
- Planning Out Projects Before Starting
- Building Complete, Functional and Impressive Projects
- Documenting Projects Thoroughly
- Creating a Portfolio of Projects
- Hosting and Promoting a Portfolio
- Highlighting Your Skills in Projects
- Tailoring Projects to Specific Jobs
- Discussing Projects in Interviews
- Defining Your Long-Term Career Goals
- Researching Potential Career Paths
- Choosing the Right College Degree
- Gaining Practical Experience Through Internships
- Building Impactful Projects to Showcase Your Skills
- Networking and Building Professional Relationships
- Creating a Targeted Job Search Strategy
- Crafting Your Career Action Plan
- The Power of Networking
- Strategically Targeting Companies
- Identifying Key Players
- Mastering Your Professional Pitch
- Leveraging Industry Events
- The Art of Reaching Out
- Conducting Informational Interviews
- Obtaining Powerful Referrals
- Tailoring Your Application
- Maintaining Meaningful Relationships
What Parents Are Saying
We turn their kids into expert coders in a fun and engaging way.
.jpg)
Paul Trudel
Elin Searfoss
%20(2)%20(1).jpeg)
John Sicard
.jpg)
Helen Rosenfeld
Asheesh Advani
Join the list!
If you'd like more information on our program, or want your child to be considered for our next cohort, please get in touch via the form.
Starts September 2024
Only 60 spots available
Made for high school students 13+ years old
Start Anytime
No locked contracts, no hidden penalties. Pay as you go and cancel anytime.
Want to contact with us?
Have questions?
We have answers.
The program is designed to emphasize the creation of custom projects that highlight the students' skills. The more projects a student completes with each certificate, the more impressive their portfolio becomes. These custom projects are pivotal in opening doors during job interviews, as interviewers tend to prioritize tangible examples of what students have built over the coursework they have completed. By focusing on practical, hands-on projects, the program aims to equip students with a portfolio that truly reflects their abilities and readiness for the job market.
Each certificate in the program takes 40 hours and are designed to be completed over 10 weeks. However, students can complete the certificates at their own pace, whether faster, or slower depending on the time they have available. The more certificates earned, the more projects created for the portfolio.
Lessons are on weekends or weekdays depending on availability of the student and available time slots. Each mentor session is 1 hour long, while team-based meetings can be scheduled based on student availability.
Course materials and mentor sessions are all online. Mentor sessions happen over Zoom.
Yes. This extracurricular is designed to give high-school students and edge in elite college admissions by empowering them with the skills to build custom projects that showcase their problem solving abilities. Imagine your child telling an admissions advisor at Stanford: "I built an emergency room triage app that uses AI to reduce patient wait times by 10%."
.png)