Learning track: Data Science & ML

Machine Learning Masterclass

Learn how to build practical, real-world AI models, how to train and reinforce those models, as well as understand the mathematical theory behind them.
Duration

10-Week Program

Prerequisites

Basic Python Knowledge

Learn by doing

Class Projects
+ Capstone

Recommended Ages

14-18

Next Available Start Dates

Monday, June 3

Class schedule: Mon, Wed
Start time: 8pm EST / 5pm PST
Duration: 1 hour

Sunday, July 7

Class schedule: Sun
Start time: 1pm EST / 10am PST
Duration: 2 hours

This Program is For

  • Teens interested in understanding how AI learns and makes decisions, ready to explore the world of machine learning in an engaging and interactive way.
  • High school students looking to build strong foundations in Machine Learning, preparing themselves for further studies or careers in AI.
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Class Syllabus:

(10 week program, 2 sessions per week)

Week 1: ML Concepts Overview & Python Fundamentals

  • Types of ML models & their applications
  • Overview of machine learning algorithms
  • Supervised machine learning process
  • Python data types, functions, classes, and modules

Week 2: Linear Regression (Start of Supervised Learning)

  • Understanding linear regression & it's applications
  • Cost functions & gradient descent
  • Introducing Scikit-Learn with performance evaluation
  • Residual plots & model deployment

Week 3: Non-Linear Regression, L1 vs. L2 Regularization

  • Polynomial regression - theory and training
  • Bias variance & trade-off's
  • Model deployment
  • Regularization overview & feature scaling
  • Lasso regression (L1) vs. Ridge regression (L2)

Week 4: Data Preparation + Cross Validation

  • Dealing with outliers and missing data
  • Fixing data and categorical data
  • Cross Validation in-depth
  • Grid search

Week 5: Logistic Regression

  • Logistics functions & the mathematics
  • Logistic Regression with Scikit-Learn
  • Model training & best fit
  • Classification with precision, recall, and curves
  • Performance evaluation and continued training

Week 6: ML Algorithms - KNN vs. SVM

  • KNN classification in-depth
  • SVM intuition and mathematics
  • KNN and SVM with Scikit-Learn
  • Classification & regression tasks

Week 7: Decision Trees vs. Random Forests

  • Building decision trees
  • Creating the decision tree model
  • Random forests - Hyperparameters & subsets
  • Random forest classification & regression

Week 8: Clustering with K-Means (Start of Unsupervised Learning)

  • Unsupervised learning overview
  • K-Means theory & quantization
  • Building K-Means model

Week 9: DBSCAN & Hierarchical Clustering

  • DBSCAN vs. K-Means
  • DBSCAN Hyperparameters & tuning
  • Hierarchical Clustering data & visualization
  • Clustering with Scikit-Learn

Week 10: Final Capstone Project & Model Deployment

Program Requirements:

Incoming Machine Learning Masterclass students should have one of the two following requirements:
Questions on your readiness for this program?
Reach out via email: hello@gogenerationstem.com
or schedule a call with us to talk through it.

A Thriving Community!

Hear From Past Students

The machine learning specialization felt like stepping into the future! We learned how to build programs that can actually learn and adapt on their own. Crazy to see our AI program recognize patterns after feeding them data. This class definitely challenged me, but it was also incredibly fun and sparked a passion. Now I'm super interested in going further into AI.
- Sarah, 16, Machine Learning Masterclass
Learning to code used to seem intimidating, but the beginner python program at Gen STEM made learning to code extremely engaging, and easy to understand. Now I can actually write and execute my own programs, which feels like magic. Now I have a clear direction of where I'd like to take my journey next.
- Alex, 13, Python: Foundations


The Robotics for Beginners class is a must-take! I went from knowing nothing about the ROS 2 framework to feeling like I understand well a lot of the functionality. We learned some pretty advanced robot techniques like sensor integration and communication between robots. This was a great primer for me to pursue a major in robotics engineering next fall
- Maya, 18, Robotics For Beginners
Meet the instructor

Daniel Doody

Daniel is a Senior Backend Engineer specializing in Cloud Infrastructure & helping Fintech companies achieve scalability. Daniel currently serves full-time as the Lead Instructor at Generation STEM.
Patrick Jones - Course author

Building Young AI Engineers.

Have you ever wondered how apps recommend movies or how self-driving cars navigate the road? This course is your launchpad into the exciting world of machine learning, where you'll explore how computers learn and make predictions.

The immersive 10-week curriculum covers the core range of essential concepts as well as advanced techniques. Led by a practicing ML engineer, who has deep experience building and deploying production ML models, this course is packed with real-world knowledge, interactive lessons, and practical projects geared to provide students with hands-on experience in mastering the fundamental principles of machine learning.

From understanding the foundational concepts of supervised learning, including linear and logistic regression, to exploring the intricacies of classification algorithms such as K-nearest neighbors (KNN) and support vector machines (SVM), students gain a solid understanding of machine learning models and their real-world applications.

Then the course delves into unsupervised learning techniques, including clustering algorithms like K-Means and hierarchical clustering, providing students with the tools to uncover hidden patterns and insights within data sets. Each week builds upon the previous, guiding students through a progressive learning journey that culminates in a final capstone project where they apply their skills to develop and deploy a machine learning model of their own design.

By the end of the Machine Learning Masterclass, students emerge with a solid understanding of machine learning concepts, practical experience in implementing machine learning algorithms, and the confidence to tackle real-world AI challenges with creativity and innovation.

** This course is not for complete beginners to the Python programming language. It requires basic Python knowledge, as we move quickly through the overview of Python fundamentals.

  • Earn a Certificate

    Add a professional Machine Learning certificate from Generation STEM to your accomplishments:
    • University applications
    • Hang on your wall
    • LinkedIn profile
    • Resume
    • etc.
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